diff --git "a/nohup.out" "b/nohup.out"
new file mode 100644--- /dev/null
+++ "b/nohup.out"
@@ -0,0 +1,20593 @@
+wandb: Currently logged in as: sanchit-gandhi (use `wandb login --relogin` to force relogin)
+wandb: wandb version 0.13.2 is available!  To upgrade, please run:
+wandb:  $ pip install wandb --upgrade
+wandb: Tracking run with wandb version 0.12.15
+wandb: Run data is saved locally in /home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/wandb/run-20220828_085247-2hx8pk65
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run flax-wav2vec2-2-bart-large-ls-960h-black-box
+wandb: ⭐️ View project at https://wandb.ai/sanchit-gandhi/librispeech_960h
+wandb: 🚀 View run at https://wandb.ai/sanchit-gandhi/librispeech_960h/runs/2hx8pk65
+INFO:__main__:Training/evaluation parameters FlaxSeq2SeqTrainingArguments(
+_n_gpu=-1,
+adafactor=False,
+adam_beta1=0.9,
+adam_beta2=0.999,
+adam_epsilon=1e-08,
+auto_find_batch_size=False,
+bf16=False,
+bf16_full_eval=False,
+data_seed=None,
+dataloader_drop_last=False,
+dataloader_num_workers=0,
+dataloader_pin_memory=True,
+ddp_bucket_cap_mb=None,
+ddp_find_unused_parameters=None,
+debug=,
+deepspeed=None,
+disable_tqdm=None,
+do_eval=True,
+do_predict=True,
+do_train=True,
+eval_accumulation_steps=None,
+eval_delay=0,
+eval_steps=10000,
+evaluation_strategy=no,
+final_generation_max_length=200,
+final_generation_num_beams=5,
+fp16=False,
+fp16_backend=auto,
+fp16_full_eval=False,
+fp16_opt_level=O1,
+fsdp=,
+fsdp_min_num_params=0,
+fsdp_transformer_layer_cls_to_wrap=None,
+full_determinism=False,
+generation_length_penalty=1.2,
+generation_max_length=200,
+generation_num_beams=5,
+gradient_accumulation_steps=1,
+gradient_checkpointing=True,
+greater_is_better=None,
+group_by_length=False,
+half_precision_backend=auto,
+hub_model_id=None,
+hub_private_repo=False,
+hub_strategy=every_save,
+hub_token=<HUB_TOKEN>,
+ignore_data_skip=False,
+include_inputs_for_metrics=False,
+jit_mode_eval=False,
+label_names=None,
+label_smoothing_factor=0.0,
+learning_rate=0.0001,
+length_column_name=length,
+load_best_model_at_end=False,
+local_rank=-1,
+log_level=passive,
+log_level_replica=passive,
+log_on_each_node=True,
+logging_dir=None,
+logging_first_step=False,
+logging_nan_inf_filter=True,
+logging_steps=25,
+logging_strategy=steps,
+lr_scheduler_type=linear,
+matmul_precision=default,
+max_grad_norm=1.0,
+max_steps=50000,
+metric_for_best_model=None,
+mp_parameters=,
+no_cuda=False,
+num_train_epochs=3.0,
+optim=adamw_hf,
+output_dir=./,
+overwrite_output_dir=True,
+past_index=-1,
+per_device_eval_batch_size=4,
+per_device_train_batch_size=8,
+precision=full,
+predict_with_generate=True,
+prediction_loss_only=False,
+push_to_hub=True,
+push_to_hub_model_id=None,
+push_to_hub_organization=None,
+push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
+ray_scope=last,
+remove_unused_columns=True,
+report_to=None,
+resume_from_checkpoint=None,
+run_name=None,
+save_on_each_node=False,
+save_steps=10000,
+save_strategy=steps,
+save_total_limit=None,
+seed=42,
+sharded_ddp=,
+skip_memory_metrics=True,
+sortish_sampler=False,
+tf32=None,
+torchdynamo=None,
+tpu_metrics_debug=False,
+tpu_num_cores=None,
+use_ipex=False,
+use_legacy_prediction_loop=False,
+warmup_ratio=0.0,
+warmup_steps=500,
+weight_decay=0.0,
+xpu_backend=None,
+)
+INFO:__main__:JAX devices: 8, matmul precision: default
+Downloading and preparing dataset librispeech_asr/all (download: 57.14 GiB, generated: 59.44 GiB, post-processed: Unknown size, total: 116.59 GiB) to /home/sanchitgandhi/cache/huggingface/datasets/librispeech_asr/all/2.1.0/14c8bffddb861b4b3a4fcdff648a56980dbb808f3fc56f5a3d56b18ee88458eb...
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Downloading data:  58% 3.67G/6.39G [00:33<00:26, 104MB/s]
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Downloading data:  58% 3.70G/6.39G [00:34<00:25, 104MB/s]
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Downloading data:  58% 3.72G/6.39G [00:34<00:26, 102MB/s]
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Downloading data:  58% 3.73G/6.39G [00:34<00:26, 102MB/s]
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Downloading data:  59% 3.74G/6.39G [00:34<00:25, 103MB/s]
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Downloading data:  60% 3.85G/6.39G [00:35<00:24, 102MB/s]
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Downloading data:  60% 3.86G/6.39G [00:35<00:24, 103MB/s]
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Downloading data:  61% 3.89G/6.39G [00:36<00:23, 106MB/s]
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Downloading data:  61% 3.90G/6.39G [00:36<00:23, 106MB/s]
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Downloading data:  61% 3.92G/6.39G [00:36<00:23, 106MB/s]
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Downloading data:  61% 3.93G/6.39G [00:36<00:23, 107MB/s]
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Downloading data:  62% 3.94G/6.39G [00:36<00:23, 106MB/s]
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Downloading data:  62% 3.95G/6.39G [00:36<00:23, 105MB/s]
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Downloading data:  62% 3.96G/6.39G [00:36<00:25, 95.0MB/s]
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Downloading data:  62% 3.97G/6.39G [00:36<00:27, 88.5MB/s]
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Downloading data:  62% 3.99G/6.39G [00:37<00:29, 82.3MB/s]
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Downloading data:  63% 4.00G/6.39G [00:37<00:30, 79.2MB/s]
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Downloading data:  63% 4.01G/6.39G [00:37<00:30, 78.1MB/s]
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Downloading data:  63% 4.02G/6.39G [00:37<00:30, 77.7MB/s]
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Downloading data:  63% 4.03G/6.39G [00:37<00:30, 78.2MB/s]
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Downloading data:  63% 4.04G/6.39G [00:37<00:29, 78.5MB/s]
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Downloading data:  63% 4.05G/6.39G [00:37<00:29, 79.5MB/s]
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Downloading data:  64% 4.06G/6.39G [00:38<00:29, 80.3MB/s]
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Downloading data:  64% 4.07G/6.39G [00:38<00:28, 80.1MB/s]
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Downloading data files: 100% 7/7 [13:18<00:00, 213.19s/it]
Downloading data files: 100% 7/7 [13:18<00:00, 114.12s/it]
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Extracting data files:   0% 0/7 [00:00<?, ?it/s]
Extracting data files:  14% 1/7 [00:01<00:09,  1.59s/it]
Extracting data files:  29% 2/7 [00:03<00:07,  1.55s/it]
Extracting data files:  43% 3/7 [00:04<00:06,  1.56s/it]
Extracting data files:  57% 4/7 [00:06<00:04,  1.58s/it]
Extracting data files:  71% 5/7 [00:30<00:19,  9.83s/it]
Extracting data files:  86% 6/7 [02:01<00:37, 37.25s/it]
Extracting data files: 100% 7/7 [04:04<00:00, 65.53s/it]
Extracting data files: 100% 7/7 [04:04<00:00, 35.00s/it]
+
Generating train.clean.100 split:   0% 0/28539 [00:00<?, ? examples/s]
Generating train.clean.100 split:   0% 1/28539 [00:00<2:11:13,  3.62 examples/s]
Generating train.clean.100 split:   1% 190/28539 [00:00<00:44, 640.85 examples/s]
Generating train.clean.100 split:   1% 299/28539 [00:00<01:31, 307.60 examples/s]
Generating train.clean.100 split:   2% 495/28539 [00:01<00:49, 561.53 examples/s]
Generating train.clean.100 split:   2% 613/28539 [00:01<00:50, 557.12 examples/s]
Generating train.clean.100 split:   3% 768/28539 [00:01<00:42, 659.50 examples/s]
Generating train.clean.100 split:   3% 975/28539 [00:01<00:30, 911.31 examples/s]
Generating train.clean.100 split:   4% 1104/28539 [00:01<00:30, 892.67 examples/s]
Generating train.clean.100 split:   4% 1280/28539 [00:01<00:29, 928.97 examples/s]
Generating train.clean.100 split:   5% 1498/28539 [00:01<00:22, 1180.43 examples/s]
Generating train.clean.100 split:   6% 1640/28539 [00:02<00:25, 1075.12 examples/s]
Generating train.clean.100 split:   6% 1792/28539 [00:02<00:24, 1105.78 examples/s]
Generating train.clean.100 split:   7% 1982/28539 [00:02<00:20, 1281.30 examples/s]
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Generating train.clean.100 split:   8% 2304/28539 [00:02<00:26, 971.76 examples/s] 
Generating train.clean.100 split:   9% 2560/28539 [00:02<00:23, 1105.14 examples/s]
Generating train.clean.100 split:  10% 2816/28539 [00:03<00:22, 1157.04 examples/s]
Generating train.clean.100 split:  11% 3043/28539 [00:03<00:19, 1290.64 examples/s]
Generating train.clean.100 split:  11% 3181/28539 [00:03<00:22, 1130.98 examples/s]
Generating train.clean.100 split:  12% 3328/28539 [00:03<00:23, 1081.67 examples/s]
Generating train.clean.100 split:  13% 3584/28539 [00:03<00:20, 1220.34 examples/s]
Generating train.clean.100 split:  13% 3809/28539 [00:03<00:17, 1403.68 examples/s]
Generating train.clean.100 split:  14% 3958/28539 [00:04<00:20, 1216.20 examples/s]
Generating train.clean.100 split:  14% 4096/28539 [00:04<00:19, 1243.05 examples/s]
Generating train.clean.100 split:  15% 4337/28539 [00:04<00:16, 1459.30 examples/s]
Generating train.clean.100 split:  16% 4491/28539 [00:04<00:20, 1193.19 examples/s]
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Generating train.other.500 split:  99% 147712/148688 [02:00<00:00, 1172.81 examples/s]
Generating train.other.500 split:  99% 147894/148688 [02:00<00:00, 1295.61 examples/s]
Generating train.other.500 split: 100% 148030/148688 [02:01<00:00, 1052.41 examples/s]
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Generating train.other.500 split: 100% 148619/148688 [02:01<00:00, 1057.95 examples/s]
                                                                                      

Generating validation.clean split:   0% 0/2703 [00:00<?, ? examples/s]
Generating validation.clean split:   9% 256/2703 [00:00<00:01, 1767.90 examples/s]
Generating validation.clean split:  19% 512/2703 [00:00<00:01, 1857.54 examples/s]
Generating validation.clean split:  28% 768/2703 [00:00<00:01, 1773.38 examples/s]
Generating validation.clean split:  38% 1024/2703 [00:00<00:00, 1777.85 examples/s]
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Generating validation.clean split:  66% 1792/2703 [00:01<00:00, 1591.74 examples/s]
Generating validation.clean split:  76% 2048/2703 [00:01<00:00, 1660.65 examples/s]
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Generating validation.clean split:  95% 2560/2703 [00:01<00:00, 1601.07 examples/s]
                                                                                   

Generating validation.other split:   0% 0/2864 [00:00<?, ? examples/s]
Generating validation.other split:   9% 256/2864 [00:00<00:01, 1746.90 examples/s]
Generating validation.other split:  18% 512/2864 [00:00<00:01, 1855.12 examples/s]
Generating validation.other split:  27% 768/2864 [00:00<00:01, 1621.27 examples/s]
Generating validation.other split:  36% 1024/2864 [00:00<00:01, 1662.42 examples/s]
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Generating validation.other split:  63% 1792/2864 [00:01<00:00, 1628.36 examples/s]
Generating validation.other split:  72% 2048/2864 [00:01<00:00, 1771.92 examples/s]
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Generating validation.other split:  98% 2816/2864 [00:01<00:00, 1594.15 examples/s]
                                                                                   

Generating test.clean split:   0% 0/2620 [00:00<?, ? examples/s]
Generating test.clean split:  10% 256/2620 [00:00<00:01, 1383.63 examples/s]
Generating test.clean split:  19% 503/2620 [00:00<00:01, 1856.88 examples/s]
Generating test.clean split:  27% 706/2620 [00:00<00:01, 1392.15 examples/s]
Generating test.clean split:  33% 863/2620 [00:00<00:01, 1314.62 examples/s]
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Generating test.clean split:  49% 1280/2620 [00:00<00:00, 1557.30 examples/s]
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Generating test.clean split:  78% 2048/2620 [00:01<00:00, 1485.48 examples/s]
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Generating test.clean split:  98% 2560/2620 [00:01<00:00, 1481.17 examples/s]
                                                                             

Generating test.other split:   0% 0/2939 [00:00<?, ? examples/s]
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Generating test.other split:  24% 704/2939 [00:00<00:01, 1698.51 examples/s]
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Generating test.other split:  78% 2304/2939 [00:01<00:00, 1800.72 examples/s]
Generating test.other split:  87% 2560/2939 [00:01<00:00, 1809.93 examples/s]
Generating test.other split:  96% 2816/2939 [00:01<00:00, 1709.51 examples/s]
                                                                             
WARNING:datasets.builder:Reusing dataset librispeech_asr (/home/sanchitgandhi/cache/huggingface/datasets/librispeech_asr/all/2.1.0/14c8bffddb861b4b3a4fcdff648a56980dbb808f3fc56f5a3d56b18ee88458eb)
+WARNING:datasets.builder:Reusing dataset librispeech_asr (/home/sanchitgandhi/cache/huggingface/datasets/librispeech_asr/all/2.1.0/14c8bffddb861b4b3a4fcdff648a56980dbb808f3fc56f5a3d56b18ee88458eb)
+WARNING:datasets.builder:Reusing dataset librispeech_asr (/home/sanchitgandhi/cache/huggingface/datasets/librispeech_asr/all/2.1.0/14c8bffddb861b4b3a4fcdff648a56980dbb808f3fc56f5a3d56b18ee88458eb)
+WARNING:datasets.builder:Reusing dataset librispeech_asr (/home/sanchitgandhi/cache/huggingface/datasets/librispeech_asr/all/2.1.0/14c8bffddb861b4b3a4fcdff648a56980dbb808f3fc56f5a3d56b18ee88458eb)
+loading configuration file https://huggingface.co/sanchit-gandhi/flax-wav2vec2-2-bart-large-scan/resolve/main/config.json from cache at /home/sanchitgandhi/.cache/huggingface/transformers/e6d3af8a2b6624d8adf8fc289717c121400164223b3e51d49b639aa34d1d3048.c9a58c9120361b7b034a0136cc74d5dce009e745c4cc111c255d5f3d0a9e2fd9
+/home/sanchitgandhi/transformers/src/transformers/configuration_utils.py:368: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.
+  warnings.warn(
+Model config SpeechEncoderDecoderConfig {
+  "_name_or_path": "sanchit-gandhi/flax-wav2vec2-2-bart-large-scan",
+  "architectures": [
+    "SpeechEncoderDecoderModel"
+  ],
+  "decoder": {
+    "_name_or_path": "",
+    "activation_dropout": 0.1,
+    "activation_function": "gelu",
+    "add_bias_logits": false,
+    "add_cross_attention": true,
+    "add_final_layer_norm": false,
+    "architectures": [
+      "BartModel"
+    ],
+    "attention_dropout": 0.1,
+    "bad_words_ids": null,
+    "bos_token_id": 0,
+    "chunk_size_feed_forward": 0,
+    "classif_dropout": 0.1,
+    "classifier_dropout": 0.0,
+    "cross_attention_hidden_size": null,
+    "d_model": 1024,
+    "decoder_attention_heads": 16,
+    "decoder_ffn_dim": 4096,
+    "decoder_layerdrop": 0.0,
+    "decoder_layers": 12,
+    "decoder_start_token_id": 2,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "dropout": 0.1,
+    "early_stopping": true,
+    "encoder_attention_heads": 16,
+    "encoder_ffn_dim": 4096,
+    "encoder_layerdrop": 0.0,
+    "encoder_layers": 12,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": 2,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": 0,
+    "forced_eos_token_id": 2,
+    "fuse_matmuls": false,
+    "gradient_checkpointing": true,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1",
+      "2": "LABEL_2"
+    },
+    "init_std": 0.02,
+    "is_decoder": true,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1,
+      "LABEL_2": 2
+    },
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "max_position_embeddings": 1024,
+    "min_length": 0,
+    "model_type": "bart",
+    "no_repeat_ngram_size": 3,
+    "normalize_before": false,
+    "num_beam_groups": 1,
+    "num_beams": 4,
+    "num_hidden_layers": 12,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": 1,
+    "prefix": null,
+    "problem_type": null,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "scale_embedding": false,
+    "sep_token_id": null,
+    "task_specific_params": {
+      "summarization": {
+        "length_penalty": 1.0,
+        "max_length": 128,
+        "min_length": 12,
+        "num_beams": 4
+      },
+      "summarization_cnn": {
+        "length_penalty": 2.0,
+        "max_length": 142,
+        "min_length": 56,
+        "num_beams": 4
+      },
+      "summarization_xsum": {
+        "length_penalty": 1.0,
+        "max_length": 62,
+        "min_length": 11,
+        "num_beams": 6
+      }
+    },
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": "float32",
+    "torchscript": false,
+    "transformers_version": "4.21.0.dev0",
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "use_cache": true,
+    "use_scan": true,
+    "vocab_size": 50265
+  },
+  "decoder_start_token_id": 0,
+  "encoder": {
+    "_name_or_path": "",
+    "activation_dropout": 0.1,
+    "adapter_kernel_size": 3,
+    "adapter_stride": 2,
+    "add_adapter": true,
+    "add_cross_attention": false,
+    "apply_spec_augment": true,
+    "architectures": [
+      "Wav2Vec2ForPreTraining"
+    ],
+    "attention_dropout": 0.1,
+    "bad_words_ids": null,
+    "bos_token_id": 1,
+    "chunk_size_feed_forward": 0,
+    "classifier_proj_size": 256,
+    "codevector_dim": 768,
+    "contrastive_logits_temperature": 0.1,
+    "conv_bias": true,
+    "conv_dim": [
+      512,
+      512,
+      512,
+      512,
+      512,
+      512,
+      512
+    ],
+    "conv_kernel": [
+      10,
+      3,
+      3,
+      3,
+      3,
+      2,
+      2
+    ],
+    "conv_stride": [
+      5,
+      2,
+      2,
+      2,
+      2,
+      2,
+      2
+    ],
+    "cross_attention_hidden_size": null,
+    "ctc_loss_reduction": "sum",
+    "ctc_zero_infinity": false,
+    "decoder_start_token_id": null,
+    "diversity_loss_weight": 0.1,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "do_stable_layer_norm": true,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": 2,
+    "exponential_decay_length_penalty": null,
+    "feat_extract_activation": "gelu",
+    "feat_extract_dropout": 0.0,
+    "feat_extract_norm": "layer",
+    "feat_proj_dropout": 0.0,
+    "feat_quantizer_dropout": 0.0,
+    "final_dropout": 0.0,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "fuse_matmuls": false,
+    "gradient_checkpointing": true,
+    "hidden_act": "gelu",
+    "hidden_dropout": 0.1,
+    "hidden_dropout_prob": 0.1,
+    "hidden_size": 1024,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-05,
+    "layerdrop": 0.0,
+    "length_penalty": 1.0,
+    "mask_feature_length": 10,
+    "mask_feature_min_masks": 0,
+    "mask_feature_prob": 0.0,
+    "mask_time_length": 10,
+    "mask_time_min_masks": 2,
+    "mask_time_prob": 0.1,
+    "max_length": 20,
+    "min_length": 0,
+    "model_type": "wav2vec2",
+    "no_repeat_ngram_size": 0,
+    "num_adapter_layers": 3,
+    "num_attention_heads": 16,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_codevector_groups": 2,
+    "num_codevectors_per_group": 320,
+    "num_conv_pos_embedding_groups": 16,
+    "num_conv_pos_embeddings": 128,
+    "num_feat_extract_layers": 7,
+    "num_hidden_layers": 24,
+    "num_negatives": 100,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_size": 1024,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": 0,
+    "prefix": null,
+    "problem_type": null,
+    "proj_codevector_dim": 768,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "task_specific_params": null,
+    "tdnn_dilation": [
+      1,
+      2,
+      3,
+      1,
+      1
+    ],
+    "tdnn_dim": [
+      512,
+      512,
+      512,
+      512,
+      1500
+    ],
+    "tdnn_kernel": [
+      5,
+      3,
+      3,
+      1,
+      1
+    ],
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": null,
+    "torchscript": false,
+    "transformers_version": "4.21.0.dev0",
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "use_scan": true,
+    "use_weighted_layer_sum": false,
+    "vocab_size": 32,
+    "xvector_output_dim": 512
+  },
+  "eos_token_id": 2,
+  "is_encoder_decoder": true,
+  "max_length": 40,
+  "model_type": "speech-encoder-decoder",
+  "pad_token_id": 1,
+  "processor_class": "Wav2Vec2Processor",
+  "tie_word_embeddings": false,
+  "transformers_version": null,
+  "use_cache": false
+}
+
+loading feature extractor configuration file https://huggingface.co/sanchit-gandhi/flax-wav2vec2-2-bart-large-scan/resolve/main/preprocessor_config.json from cache at /home/sanchitgandhi/.cache/huggingface/transformers/bc2232c616201c7d3d66ba3f6a7d1186306134838dfb19786149f0e16122787d.bbc1eb890a39c82e710a893223b8452ac5b78e8b57083b2f893aa7dc59d4ed69
+Feature extractor Wav2Vec2FeatureExtractor {
+  "do_normalize": true,
+  "feature_extractor_type": "Wav2Vec2FeatureExtractor",
+  "feature_size": 1,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "return_attention_mask": true,
+  "sampling_rate": 16000
+}
+
+loading file https://huggingface.co/sanchit-gandhi/flax-wav2vec2-2-bart-large-scan/resolve/main/vocab.json from cache at /home/sanchitgandhi/.cache/huggingface/transformers/86c0de13925d1534934e540ff4c9dd778f49761b4eaf59dae3335a4f6690a814.bfdcc444ff249bca1a95ca170ec350b442f81804d7df3a95a2252217574121d7
+loading file https://huggingface.co/sanchit-gandhi/flax-wav2vec2-2-bart-large-scan/resolve/main/merges.txt from cache at /home/sanchitgandhi/.cache/huggingface/transformers/7cf4fc91891684e1177d1c519689e4c310ebdec965e00d6e45134bb9227ab01b.f5b91da9e34259b8f4d88dbc97c740667a0e8430b96314460cdb04e86d4fc435
+loading file https://huggingface.co/sanchit-gandhi/flax-wav2vec2-2-bart-large-scan/resolve/main/tokenizer.json from cache at /home/sanchitgandhi/.cache/huggingface/transformers/c02f3f3009bfacaa24cfead1d0f7fbf4fc2fb5f8092f68703449f02aa3a28e03.393fa6a095aa312a3cce4d5263e471bd94ec0215e6c63448a6464d59ff900814
+loading file https://huggingface.co/sanchit-gandhi/flax-wav2vec2-2-bart-large-scan/resolve/main/added_tokens.json from cache at None
+loading file https://huggingface.co/sanchit-gandhi/flax-wav2vec2-2-bart-large-scan/resolve/main/special_tokens_map.json from cache at /home/sanchitgandhi/.cache/huggingface/transformers/505d61b8f6e05764b5aec1483bfdd13a310681a5af54957263604323be3bbabf.a11ebb04664c067c8fe5ef8f8068b0f721263414a26058692f7b2e4ba2a1b342
+loading file https://huggingface.co/sanchit-gandhi/flax-wav2vec2-2-bart-large-scan/resolve/main/tokenizer_config.json from cache at /home/sanchitgandhi/.cache/huggingface/transformers/ff79c23164eac352d7f9651f3c3774a962ce80f81460d9e17d689235fa34ee80.0e8b2b497f91e23302894a5c1f19ced6334b0abd450a7bce75a67bf0f9ee5c54
+loading weights file https://huggingface.co/sanchit-gandhi/flax-wav2vec2-2-bart-large-scan/resolve/main/flax_model.msgpack from cache at /home/sanchitgandhi/.cache/huggingface/transformers/1279dc21f7dd9ed546f166e7e445e068b2672ddfa5386b2e3a3a973b8d668365.8e03496bb6919447aeb468483249e7b65dfb59c42989be9787af0aa6aa9b3f50
+tcmalloc: large alloc 2353618944 bytes == 0xb1ee8000 @  0x7f7cba873680 0x7f7cba894824 0x5fb391 0x64be71 0x5c6366 0x4f3b9e 0x651588 0x505a63 0x56bbfa 0x569dba 0x50bca0 0x56cc1f 0x569dba 0x5f6eb3 0x56bacd 0x569dba 0x6902a7 0x67f951 0x67f9cf 0x67fa71 0x681b97 0x6b9d32 0x6ba0bd 0x7f7cba6830b3 0x5fc5fe
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+All model checkpoint weights were used when initializing FlaxSpeechEncoderDecoderModel.
+
+All the weights of FlaxSpeechEncoderDecoderModel were initialized from the model checkpoint at sanchit-gandhi/flax-wav2vec2-2-bart-large-scan.
+If your task is similar to the task the model of the checkpoint was trained on, you can already use FlaxSpeechEncoderDecoderModel for predictions without further training.
+/home/sanchitgandhi/transformers/src/transformers/modeling_flax_utils.py:904: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  param_dtypes = jax.tree_map(lambda x: x.dtype, state)
+
filtering data where the targets are ignored in scoring:   0% 0/282 [00:00<?, ?ba/s]Dataset librispeech_asr downloaded and prepared to /home/sanchitgandhi/cache/huggingface/datasets/librispeech_asr/all/2.1.0/14c8bffddb861b4b3a4fcdff648a56980dbb808f3fc56f5a3d56b18ee88458eb. Subsequent calls will reuse this data.
+
filtering data where the targets are ignored in scoring:  13% 37/282 [00:00<00:00, 367.66ba/s]
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+
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+
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+
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+
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+
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+
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+
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preprocess train dataset: 100% 2620/2620 [00:13<00:00, 199.87ex/s]
+
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+run_flax_speech_recognition_seq2seq.py:1052: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate
+  wer_metric = load_metric("wer")
+Feature extractor saved in ./preprocessor_config.json
+tokenizer config file saved in ./tokenizer_config.json
+Special tokens file saved in ./special_tokens_map.json
+Configuration saved in ./config.json
+loading feature extractor configuration file ./preprocessor_config.json
+loading configuration file ./config.json
+/home/sanchitgandhi/transformers/src/transformers/configuration_utils.py:368: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.
+  warnings.warn(
+Model config SpeechEncoderDecoderConfig {
+  "_name_or_path": "./",
+  "architectures": [
+    "SpeechEncoderDecoderModel"
+  ],
+  "decoder": {
+    "_name_or_path": "",
+    "activation_dropout": 0.2,
+    "activation_function": "gelu",
+    "add_bias_logits": false,
+    "add_cross_attention": true,
+    "add_final_layer_norm": false,
+    "architectures": [
+      "BartModel"
+    ],
+    "attention_dropout": 0.1,
+    "bad_words_ids": null,
+    "bos_token_id": 0,
+    "chunk_size_feed_forward": 0,
+    "classif_dropout": 0.1,
+    "classifier_dropout": 0.0,
+    "cross_attention_hidden_size": null,
+    "d_model": 1024,
+    "decoder_attention_heads": 16,
+    "decoder_ffn_dim": 4096,
+    "decoder_layerdrop": 0.0,
+    "decoder_layers": 12,
+    "decoder_start_token_id": 2,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "dropout": 0.2,
+    "early_stopping": true,
+    "encoder_attention_heads": 16,
+    "encoder_ffn_dim": 4096,
+    "encoder_layerdrop": 0.0,
+    "encoder_layers": 12,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": 2,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": 0,
+    "forced_eos_token_id": 2,
+    "fuse_matmuls": false,
+    "gradient_checkpointing": true,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1",
+      "2": "LABEL_2"
+    },
+    "init_std": 0.02,
+    "is_decoder": true,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1,
+      "LABEL_2": 2
+    },
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "max_position_embeddings": 1024,
+    "min_length": 0,
+    "model_type": "bart",
+    "no_repeat_ngram_size": 3,
+    "normalize_before": false,
+    "num_beam_groups": 1,
+    "num_beams": 4,
+    "num_hidden_layers": 12,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": 1,
+    "prefix": null,
+    "problem_type": null,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "scale_embedding": false,
+    "sep_token_id": null,
+    "task_specific_params": {
+      "summarization": {
+        "length_penalty": 1.0,
+        "max_length": 128,
+        "min_length": 12,
+        "num_beams": 4
+      },
+      "summarization_cnn": {
+        "length_penalty": 2.0,
+        "max_length": 142,
+        "min_length": 56,
+        "num_beams": 4
+      },
+      "summarization_xsum": {
+        "length_penalty": 1.0,
+        "max_length": 62,
+        "min_length": 11,
+        "num_beams": 6
+      }
+    },
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": "float32",
+    "torchscript": false,
+    "transformers_version": "4.21.0.dev0",
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "use_cache": true,
+    "use_scan": true,
+    "vocab_size": 50265
+  },
+  "decoder_start_token_id": 0,
+  "encoder": {
+    "_name_or_path": "",
+    "activation_dropout": 0.2,
+    "adapter_kernel_size": 3,
+    "adapter_stride": 2,
+    "add_adapter": true,
+    "add_cross_attention": false,
+    "apply_spec_augment": true,
+    "architectures": [
+      "Wav2Vec2ForPreTraining"
+    ],
+    "attention_dropout": 0.1,
+    "bad_words_ids": null,
+    "bos_token_id": 1,
+    "chunk_size_feed_forward": 0,
+    "classifier_proj_size": 256,
+    "codevector_dim": 768,
+    "contrastive_logits_temperature": 0.1,
+    "conv_bias": true,
+    "conv_dim": [
+      512,
+      512,
+      512,
+      512,
+      512,
+      512,
+      512
+    ],
+    "conv_kernel": [
+      10,
+      3,
+      3,
+      3,
+      3,
+      2,
+      2
+    ],
+    "conv_stride": [
+      5,
+      2,
+      2,
+      2,
+      2,
+      2,
+      2
+    ],
+    "cross_attention_hidden_size": null,
+    "ctc_loss_reduction": "sum",
+    "ctc_zero_infinity": false,
+    "decoder_start_token_id": null,
+    "diversity_loss_weight": 0.1,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "do_stable_layer_norm": true,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": 2,
+    "exponential_decay_length_penalty": null,
+    "feat_extract_activation": "gelu",
+    "feat_extract_dropout": 0.0,
+    "feat_extract_norm": "layer",
+    "feat_proj_dropout": 0.2,
+    "feat_quantizer_dropout": 0.0,
+    "final_dropout": 0.0,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "fuse_matmuls": false,
+    "gradient_checkpointing": true,
+    "hidden_act": "gelu",
+    "hidden_dropout": 0.2,
+    "hidden_dropout_prob": 0.1,
+    "hidden_size": 1024,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-05,
+    "layerdrop": 0.0,
+    "length_penalty": 1.0,
+    "mask_feature_length": 10,
+    "mask_feature_min_masks": 0,
+    "mask_feature_prob": 0.0,
+    "mask_time_length": 10,
+    "mask_time_min_masks": 2,
+    "mask_time_prob": 0.1,
+    "max_length": 20,
+    "min_length": 0,
+    "model_type": "wav2vec2",
+    "no_repeat_ngram_size": 0,
+    "num_adapter_layers": 3,
+    "num_attention_heads": 16,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_codevector_groups": 2,
+    "num_codevectors_per_group": 320,
+    "num_conv_pos_embedding_groups": 16,
+    "num_conv_pos_embeddings": 128,
+    "num_feat_extract_layers": 7,
+    "num_hidden_layers": 24,
+    "num_negatives": 100,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_size": 1024,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": 0,
+    "prefix": null,
+    "problem_type": null,
+    "proj_codevector_dim": 768,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "task_specific_params": null,
+    "tdnn_dilation": [
+      1,
+      2,
+      3,
+      1,
+      1
+    ],
+    "tdnn_dim": [
+      512,
+      512,
+      512,
+      512,
+      1500
+    ],
+    "tdnn_kernel": [
+      5,
+      3,
+      3,
+      1,
+      1
+    ],
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": null,
+    "torchscript": false,
+    "transformers_version": "4.21.0.dev0",
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "use_scan": true,
+    "use_weighted_layer_sum": false,
+    "vocab_size": 32,
+    "xvector_output_dim": 512
+  },
+  "eos_token_id": 2,
+  "is_encoder_decoder": true,
+  "max_length": 40,
+  "model_type": "speech-encoder-decoder",
+  "pad_token_id": 1,
+  "processor_class": "Wav2Vec2Processor",
+  "tie_word_embeddings": false,
+  "transformers_version": null,
+  "use_cache": false
+}
+
+loading feature extractor configuration file ./preprocessor_config.json
+Feature extractor Wav2Vec2FeatureExtractor {
+  "do_normalize": true,
+  "feature_extractor_type": "Wav2Vec2FeatureExtractor",
+  "feature_size": 1,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "return_attention_mask": true,
+  "sampling_rate": 16000
+}
+
+Didn't find file ./added_tokens.json. We won't load it.
+loading file ./vocab.json
+loading file ./merges.txt
+loading file ./tokenizer.json
+loading file None
+loading file ./special_tokens_map.json
+loading file ./tokenizer_config.json
+WARNING:__main__:Unable to display metrics through TensorBoard because the package is not installed: Please run `pip install tensorboard` to enable.
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/./ is already a clone of https://huggingface.co/sanchit-gandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box. Make sure you pull the latest changes with `repo.git_pull()`.
+WARNING:huggingface_hub.repository:/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/./ is already a clone of https://huggingface.co/sanchit-gandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box. Make sure you pull the latest changes with `repo.git_pull()`.
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+run_flax_speech_recognition_seq2seq.py:336: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, t)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/optax/_src/transform.py:319: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  mu = jax.tree_map(  # First moment
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/optax/_src/transform.py:321: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  nu = jax.tree_map(jnp.zeros_like, params)  # Second moment
+INFO:__main__:***** Running training *****
+INFO:__main__:  Num examples = 281186
+INFO:__main__:  Num Epochs = 12
+INFO:__main__:  Instantaneous batch size per device = 8
+INFO:__main__:  Num gradient accumulation steps = 1
+INFO:__main__:  Total train batch size (w. parallel & distributed) = 64
+INFO:__main__:  Total optimization steps = 50000
+INFO:__main__:  Gradient checkpointing: True
+INFO:__main__:  Use scan: True
+INFO:__main__:  Fuse matmuls: False
+
Epoch ... (1/12):   0% 0/12 [00:00<?, ?it/s]
+
Training...:   0% 0/4393 [00:00<?, ?it/s]/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/training/common_utils.py:25: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+run_flax_speech_recognition_seq2seq.py:1266: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  grad = jax.tree_map(lambda g: g / total_samples, grad)
+run_flax_speech_recognition_seq2seq.py:1267: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  loss = jax.tree_map(lambda l: l / total_samples, loss)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/optax/_src/linear_algebra.py:29: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  sum([jnp.sum(numerics.abs_sq(x)) for x in jax.tree_leaves(updates)]))
+run_flax_speech_recognition_seq2seq.py:399: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  grads = jax.tree_map(lambda t: (t / g_norm) * casted_max_grad_norm, grads)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/optax/_src/transform.py:82: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/optax/_src/transform.py:99: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/optax/_src/transform.py:106: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(lambda t: t / bias_correction.astype(t.dtype), moment)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/optax/_src/transform.py:331: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  updates = jax.tree_map(
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/optax/_src/transform.py:610: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  updates = jax.tree_map(
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/optax/_src/transform.py:647: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  updates = jax.tree_map(
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/optax/_src/update.py:42: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(
+run_flax_speech_recognition_seq2seq.py:1277: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  layer_grad_norm = jax.tree_map(jnp.linalg.norm, grad)
+run_flax_speech_recognition_seq2seq.py:1286: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  layer_param_norm = jax.tree_map(jnp.linalg.norm, new_state.params)
+
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+  return jax.tree_map(lambda x: x[0], tree)
+
+
                                                    

                                            

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Epoch ... (1/12):   0% 0/12 [38:45<?, ?it/s]
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Training...:  15% 649/4393 [1:02:20<1:52:23,  1.80s/it]
Epoch ... (1/12):   0% 0/12 [1:02:28<?, ?it/s]
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Training...:  15% 650/4393 [1:02:20<1:42:31,  1.64s/it]
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Epoch ... (1/12):   0% 0/12 [1:04:52<?, ?it/s]
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Training...:  15% 675/4393 [1:04:45<5:47:11,  5.60s/it]
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Epoch ... (1/12):   0% 0/12 [1:06:23<?, ?it/s]
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Training...:  16% 700/4393 [1:06:15<1:39:28,  1.62s/it]
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Epoch ... (1/12):   0% 0/12 [1:08:47<?, ?it/s]
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Training...:  17% 725/4393 [1:08:40<5:42:20,  5.60s/it]
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Epoch ... (1/12):   0% 0/12 [1:10:18<?, ?it/s]
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Training...:  17% 750/4393 [1:10:11<1:37:20,  1.60s/it]
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Training...:  17% 751/4393 [1:10:17<3:00:09,  2.97s/it]
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Epoch ... (1/12):   0% 0/12 [1:12:43<?, ?it/s]
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Training...:  18% 775/4393 [1:12:35<5:38:46,  5.62s/it]
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Epoch ... (1/12):   0% 0/12 [1:14:11<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [1:16:36<?, ?it/s]
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+
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Epoch ... (1/12):   0% 0/12 [1:18:06<?, ?it/s]
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Training...:  19% 850/4393 [1:17:59<1:36:10,  1.63s/it]
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Training...:  19% 851/4393 [1:18:05<2:57:24,  3.01s/it]
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Training...:  19% 852/4393 [1:18:11<3:54:21,  3.97s/it]
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Training...:  20% 871/4393 [1:20:01<5:22:30,  5.49s/it]
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Training...:  20% 872/4393 [1:20:06<5:21:44,  5.48s/it]
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Training...:  20% 874/4393 [1:20:17<5:16:10,  5.39s/it]
+
                                                       

                                              

+
Training...:  20% 874/4393 [1:20:23<5:16:10,  5.39s/it]
Epoch ... (1/12):   0% 0/12 [1:20:30<?, ?it/s]
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Training...:  20% 875/4393 [1:20:23<5:27:47,  5.59s/it]
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Training...:  20% 876/4393 [1:20:28<5:22:59,  5.51s/it]
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Training...:  20% 883/4393 [1:21:03<4:46:02,  4.89s/it]
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Training...:  20% 884/4393 [1:21:08<4:44:32,  4.87s/it]
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Training...:  20% 889/4393 [1:21:29<4:04:31,  4.19s/it]
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Training...:  20% 890/4393 [1:21:32<3:52:24,  3.98s/it]
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Training...:  20% 891/4393 [1:21:36<3:40:00,  3.77s/it]
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Training...:  20% 892/4393 [1:21:39<3:27:08,  3.55s/it]
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Training...:  20% 893/4393 [1:21:41<3:12:49,  3.31s/it]
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Training...:  20% 894/4393 [1:21:44<2:58:46,  3.07s/it]
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Training...:  20% 895/4393 [1:21:46<2:44:27,  2.82s/it]
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Training...:  20% 896/4393 [1:21:48<2:31:12,  2.59s/it]
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Training...:  20% 897/4393 [1:21:50<2:16:45,  2.35s/it]
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Training...:  20% 898/4393 [1:21:51<2:02:22,  2.10s/it]
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Training...:  20% 899/4393 [1:21:53<1:47:20,  1.84s/it]
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+
Training...:  20% 899/4393 [1:21:54<1:47:20,  1.84s/it]
Epoch ... (1/12):   0% 0/12 [1:22:01<?, ?it/s]
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Training...:  20% 900/4393 [1:21:54<1:38:28,  1.69s/it]
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Training...:  21% 901/4393 [1:22:00<2:59:33,  3.09s/it]
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Training...:  21% 902/4393 [1:22:07<3:55:20,  4.04s/it]
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Training...:  21% 903/4393 [1:22:13<4:29:01,  4.62s/it]
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Training...:  21% 904/4393 [1:22:19<4:54:32,  5.07s/it]
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Training...:  21% 905/4393 [1:22:25<5:08:07,  5.30s/it]
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Training...:  21% 907/4393 [1:22:36<5:25:30,  5.60s/it]
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Training...:  21% 908/4393 [1:22:42<5:30:44,  5.69s/it]
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Training...:  21% 909/4393 [1:22:48<5:33:18,  5.74s/it]
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Training...:  21% 910/4393 [1:22:54<5:34:28,  5.76s/it]
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Training...:  21% 911/4393 [1:23:00<5:34:26,  5.76s/it]
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Training...:  21% 912/4393 [1:23:06<5:35:19,  5.78s/it]
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Training...:  21% 913/4393 [1:23:11<5:32:31,  5.73s/it]
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Training...:  21% 914/4393 [1:23:17<5:32:55,  5.74s/it]
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Training...:  21% 915/4393 [1:23:22<5:29:39,  5.69s/it]
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Training...:  21% 916/4393 [1:23:28<5:28:39,  5.67s/it]
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Training...:  21% 917/4393 [1:23:34<5:25:24,  5.62s/it]
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Training...:  21% 918/4393 [1:23:39<5:25:07,  5.61s/it]
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Training...:  21% 919/4393 [1:23:45<5:23:24,  5.59s/it]
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Training...:  21% 920/4393 [1:23:50<5:25:07,  5.62s/it]
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Training...:  21% 921/4393 [1:23:56<5:21:23,  5.55s/it]
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Training...:  21% 922/4393 [1:24:01<5:19:36,  5.52s/it]
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Training...:  21% 923/4393 [1:24:07<5:16:03,  5.46s/it]
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Training...:  21% 924/4393 [1:24:12<5:13:42,  5.43s/it]
+
                                                       

                                              

+
Training...:  21% 924/4393 [1:24:18<5:13:42,  5.43s/it]
Epoch ... (1/12):   0% 0/12 [1:24:25<?, ?it/s]
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Training...:  21% 925/4393 [1:24:18<5:23:39,  5.60s/it]
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Training...:  21% 926/4393 [1:24:23<5:18:53,  5.52s/it]
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Training...:  21% 927/4393 [1:24:28<5:11:43,  5.40s/it]
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Training...:  21% 928/4393 [1:24:34<5:09:23,  5.36s/it]
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Training...:  21% 929/4393 [1:24:39<5:04:21,  5.27s/it]
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Training...:  21% 930/4393 [1:24:44<5:00:23,  5.20s/it]
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Training...:  21% 931/4393 [1:24:49<4:54:30,  5.10s/it]
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Training...:  21% 932/4393 [1:24:54<4:51:14,  5.05s/it]
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Training...:  21% 933/4393 [1:24:58<4:44:59,  4.94s/it]
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Training...:  21% 934/4393 [1:25:03<4:40:24,  4.86s/it]
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Training...:  21% 935/4393 [1:25:07<4:34:03,  4.76s/it]
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Training...:  21% 936/4393 [1:25:12<4:28:03,  4.65s/it]
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Training...:  21% 937/4393 [1:25:16<4:18:52,  4.49s/it]
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Training...:  21% 938/4393 [1:25:20<4:13:46,  4.41s/it]
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Training...:  21% 939/4393 [1:25:24<4:02:20,  4.21s/it]
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Training...:  21% 940/4393 [1:25:28<3:52:18,  4.04s/it]
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Training...:  21% 941/4393 [1:25:31<3:40:36,  3.83s/it]
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Training...:  21% 942/4393 [1:25:34<3:27:34,  3.61s/it]
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Training...:  21% 943/4393 [1:25:37<3:13:35,  3.37s/it]
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Training...:  21% 944/4393 [1:25:39<2:59:27,  3.12s/it]
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Training...:  22% 945/4393 [1:25:42<2:44:25,  2.86s/it]
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Training...:  22% 946/4393 [1:25:44<2:29:42,  2.61s/it]
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Training...:  22% 947/4393 [1:25:45<2:14:52,  2.35s/it]
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Training...:  22% 948/4393 [1:25:47<2:00:12,  2.09s/it]
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Training...:  22% 949/4393 [1:25:48<1:45:14,  1.83s/it]
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Training...:  22% 949/4393 [1:25:49<1:45:14,  1.83s/it]
Epoch ... (1/12):   0% 0/12 [1:25:57<?, ?it/s]
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Training...:  22% 950/4393 [1:25:49<1:35:47,  1.67s/it]
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Training...:  22% 951/4393 [1:25:56<2:53:56,  3.03s/it]
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Training...:  22% 952/4393 [1:26:02<3:51:05,  4.03s/it]
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Training...:  22% 953/4393 [1:26:08<4:26:33,  4.65s/it]
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Training...:  22% 954/4393 [1:26:14<4:50:54,  5.08s/it]
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Training...:  22% 955/4393 [1:26:20<5:05:17,  5.33s/it]
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Training...:  22% 956/4393 [1:26:26<5:17:09,  5.54s/it]
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Training...:  22% 957/4393 [1:26:32<5:24:53,  5.67s/it]
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Training...:  22% 958/4393 [1:26:38<5:31:49,  5.80s/it]
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Training...:  22% 959/4393 [1:26:44<5:34:08,  5.84s/it]
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Training...:  22% 960/4393 [1:26:50<5:36:05,  5.87s/it]
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Training...:  22% 961/4393 [1:26:56<5:32:08,  5.81s/it]
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Training...:  22% 962/4393 [1:27:01<5:31:48,  5.80s/it]
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Training...:  22% 963/4393 [1:27:07<5:28:19,  5.74s/it]
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Training...:  22% 964/4393 [1:27:13<5:28:56,  5.76s/it]
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Training...:  22% 965/4393 [1:27:19<5:26:42,  5.72s/it]
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Training...:  22% 966/4393 [1:27:24<5:25:22,  5.70s/it]
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Training...:  22% 967/4393 [1:27:30<5:20:47,  5.62s/it]
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Training...:  22% 968/4393 [1:27:35<5:18:50,  5.59s/it]
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Training...:  22% 972/4393 [1:27:57<5:08:51,  5.42s/it]
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Training...:  22% 974/4393 [1:28:07<5:03:41,  5.33s/it]
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Epoch ... (1/12):   0% 0/12 [1:28:20<?, ?it/s]
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Training...:  22% 975/4393 [1:28:13<5:14:36,  5.52s/it]
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Training...:  22% 976/4393 [1:28:18<5:09:11,  5.43s/it]
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Training...:  22% 979/4393 [1:28:33<4:53:49,  5.16s/it]
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Training...:  22% 980/4393 [1:28:38<4:50:44,  5.11s/it]
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Training...:  22% 984/4393 [1:28:57<4:34:31,  4.83s/it]
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Training...:  22% 985/4393 [1:29:02<4:26:27,  4.69s/it]
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Training...:  22% 986/4393 [1:29:06<4:20:01,  4.58s/it]
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Training...:  22% 988/4393 [1:29:14<4:02:42,  4.28s/it]
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Training...:  23% 989/4393 [1:29:18<3:51:49,  4.09s/it]
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Training...:  23% 990/4393 [1:29:21<3:41:05,  3.90s/it]
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Training...:  23% 991/4393 [1:29:24<3:28:19,  3.67s/it]
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Training...:  23% 992/4393 [1:29:27<3:15:31,  3.45s/it]
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Training...:  23% 993/4393 [1:29:30<3:02:42,  3.22s/it]
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Training...:  23% 994/4393 [1:29:32<2:48:35,  2.98s/it]
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Training...:  23% 995/4393 [1:29:35<2:34:20,  2.73s/it]
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Training...:  23% 996/4393 [1:29:36<2:21:08,  2.49s/it]
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Training...:  23% 997/4393 [1:29:38<2:08:11,  2.26s/it]
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Training...:  23% 998/4393 [1:29:40<1:54:40,  2.03s/it]
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Training...:  23% 999/4393 [1:29:41<1:40:50,  1.78s/it]
+
                                                       

                                              

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Epoch ... (1/12):   0% 0/12 [1:29:49<?, ?it/s]
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Training...:  23% 1000/4393 [1:29:42<1:32:59,  1.64s/it]
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Training...:  23% 1005/4393 [1:30:13<5:01:09,  5.33s/it]
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Training...:  23% 1024/4393 [1:32:00<4:59:59,  5.34s/it]
+
                                                        

                                              

+
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Epoch ... (1/12):   0% 0/12 [1:32:13<?, ?it/s]
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Training...:  23% 1025/4393 [1:32:05<5:09:50,  5.52s/it]
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Training...:  23% 1026/4393 [1:32:11<5:04:59,  5.43s/it]
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Training...:  23% 1029/4393 [1:32:26<4:48:46,  5.15s/it]
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Training...:  23% 1030/4393 [1:32:31<4:46:19,  5.11s/it]
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Training...:  23% 1031/4393 [1:32:36<4:40:22,  5.00s/it]
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Training...:  24% 1048/4393 [1:33:32<1:50:49,  1.99s/it]
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Training...:  24% 1049/4393 [1:33:34<1:37:06,  1.74s/it]
+
                                                        

                                              

+
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Epoch ... (1/12):   0% 0/12 [1:33:42<?, ?it/s]
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Training...:  24% 1050/4393 [1:33:35<1:28:50,  1.59s/it]
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Training...:  24% 1051/4393 [1:33:41<2:43:56,  2.94s/it]
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Training...:  24% 1052/4393 [1:33:47<3:39:35,  3.94s/it]
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Training...:  24% 1074/4393 [1:35:58<4:58:36,  5.40s/it]
Epoch ... (1/12):   0% 0/12 [1:36:05<?, ?it/s]
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Training...:  24% 1075/4393 [1:35:58<5:07:59,  5.57s/it]
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Training...:  25% 1099/4393 [1:37:28<1:35:25,  1.74s/it]
Epoch ... (1/12):   0% 0/12 [1:37:35<?, ?it/s]
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Training...:  25% 1100/4393 [1:37:28<1:28:02,  1.60s/it]
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Training...:  26% 1124/4393 [1:39:50<4:49:51,  5.32s/it]
Epoch ... (1/12):   0% 0/12 [1:39:57<?, ?it/s]
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Training...:  26% 1125/4393 [1:39:50<4:59:14,  5.49s/it]
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Training...:  26% 1141/4393 [1:41:00<3:14:41,  3.59s/it]
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Training...:  26% 1145/4393 [1:41:10<2:24:34,  2.67s/it]
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Training...:  26% 1146/4393 [1:41:11<2:11:15,  2.43s/it]
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Training...:  26% 1147/4393 [1:41:13<1:58:34,  2.19s/it]
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Training...:  26% 1148/4393 [1:41:15<1:46:14,  1.96s/it]
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Training...:  26% 1149/4393 [1:41:16<1:33:08,  1.72s/it]
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+
Training...:  26% 1149/4393 [1:41:17<1:33:08,  1.72s/it]
Epoch ... (1/12):   0% 0/12 [1:41:24<?, ?it/s]
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Training...:  26% 1150/4393 [1:41:17<1:25:09,  1.58s/it]
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Training...:  26% 1151/4393 [1:41:23<2:37:49,  2.92s/it]
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Training...:  26% 1152/4393 [1:41:29<3:31:06,  3.91s/it]
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Training...:  26% 1153/4393 [1:41:35<4:03:37,  4.51s/it]
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Training...:  26% 1154/4393 [1:41:41<4:29:53,  5.00s/it]
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Training...:  26% 1161/4393 [1:42:22<5:05:22,  5.67s/it]
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Training...:  26% 1162/4393 [1:42:28<5:05:48,  5.68s/it]
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Training...:  26% 1163/4393 [1:42:33<5:04:11,  5.65s/it]
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Training...:  26% 1164/4393 [1:42:39<5:04:52,  5.66s/it]
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Training...:  27% 1165/4393 [1:42:45<5:02:06,  5.62s/it]
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Training...:  27% 1168/4393 [1:43:01<4:56:18,  5.51s/it]
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Training...:  27% 1171/4393 [1:43:17<4:49:43,  5.40s/it]
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Training...:  27% 1172/4393 [1:43:22<4:49:06,  5.39s/it]
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Training...:  27% 1174/4393 [1:43:33<4:44:06,  5.30s/it]
+
                                                        

                                              

+
Training...:  27% 1174/4393 [1:43:39<4:44:06,  5.30s/it]
Epoch ... (1/12):   0% 0/12 [1:43:46<?, ?it/s]
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Training...:  27% 1175/4393 [1:43:39<4:53:33,  5.47s/it]
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Training...:  27% 1176/4393 [1:43:44<4:48:45,  5.39s/it]
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Training...:  27% 1177/4393 [1:43:49<4:41:17,  5.25s/it]
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Training...:  27% 1178/4393 [1:43:54<4:38:01,  5.19s/it]
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Training...:  27% 1179/4393 [1:43:59<4:32:49,  5.09s/it]
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Training...:  27% 1180/4393 [1:44:04<4:29:58,  5.04s/it]
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Training...:  27% 1182/4393 [1:44:13<4:24:01,  4.93s/it]
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Training...:  27% 1184/4393 [1:44:22<4:12:34,  4.72s/it]
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Training...:  27% 1191/4393 [1:44:49<3:17:11,  3.70s/it]
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Training...:  27% 1198/4393 [1:45:05<1:47:38,  2.02s/it]
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Training...:  27% 1199/4393 [1:45:06<1:34:08,  1.77s/it]
+
                                                        

                                              

+
Training...:  27% 1199/4393 [1:45:07<1:34:08,  1.77s/it]
Epoch ... (1/12):   0% 0/12 [1:45:14<?, ?it/s]
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Training...:  27% 1200/4393 [1:45:07<1:25:59,  1.62s/it]
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Training...:  27% 1201/4393 [1:45:13<2:36:20,  2.94s/it]
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+
                                                        

                                              

+
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Epoch ... (1/12):   0% 0/12 [1:47:37<?, ?it/s]
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Training...:  28% 1225/4393 [1:47:29<4:51:05,  5.51s/it]
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+
                                                        

                                              

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Training...:  28% 1249/4393 [1:48:58<1:32:47,  1.77s/it]
Epoch ... (1/12):   0% 0/12 [1:49:06<?, ?it/s]
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Training...:  28% 1250/4393 [1:48:58<1:24:39,  1.62s/it]
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Training...:  29% 1274/4393 [1:51:21<4:40:01,  5.39s/it]
Epoch ... (1/12):   0% 0/12 [1:51:28<?, ?it/s]
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Training...:  29% 1275/4393 [1:51:21<4:47:12,  5.53s/it]
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Training...:  30% 1299/4393 [1:52:49<1:30:19,  1.75s/it]
Epoch ... (1/12):   0% 0/12 [1:52:56<?, ?it/s]
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Training...:  30% 1300/4393 [1:52:49<1:22:35,  1.60s/it]
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Training...:  30% 1324/4393 [1:55:11<4:32:13,  5.32s/it]
Epoch ... (1/12):   0% 0/12 [1:55:18<?, ?it/s]
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Training...:  30% 1325/4393 [1:55:11<4:40:49,  5.49s/it]
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Training...:  31% 1349/4393 [1:56:42<1:37:43,  1.93s/it]
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Training...:  31% 1349/4393 [1:56:43<1:37:43,  1.93s/it]
Epoch ... (1/12):   0% 0/12 [1:56:50<?, ?it/s]
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Training...:  31% 1350/4393 [1:56:43<1:27:39,  1.73s/it]
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Training...:  31% 1351/4393 [1:56:49<2:33:56,  3.04s/it]
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Training...:  31% 1374/4393 [1:59:04<4:25:50,  5.28s/it]
Epoch ... (1/12):   0% 0/12 [1:59:11<?, ?it/s]
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Training...:  31% 1375/4393 [1:59:04<4:34:41,  5.46s/it]
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Training...:  31% 1376/4393 [1:59:10<4:30:51,  5.39s/it]
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Epoch ... (1/12):   0% 0/12 [2:00:40<?, ?it/s]
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Training...:  32% 1400/4393 [2:00:33<1:20:30,  1.61s/it]
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+
                                                        

                                              

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Epoch ... (1/12):   0% 0/12 [2:03:02<?, ?it/s]
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Training...:  32% 1425/4393 [2:02:55<4:30:15,  5.46s/it]
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+
                                                        

                                              

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Epoch ... (1/12):   0% 0/12 [2:04:29<?, ?it/s]
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Training...:  33% 1450/4393 [2:04:21<1:17:22,  1.58s/it]
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Training...:  34% 1474/4393 [2:06:44<4:19:18,  5.33s/it]
Epoch ... (1/12):   0% 0/12 [2:06:51<?, ?it/s]
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Training...:  34% 1475/4393 [2:06:44<4:27:25,  5.50s/it]
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Training...:  34% 1499/4393 [2:08:14<1:29:29,  1.86s/it]
Epoch ... (1/12):   0% 0/12 [2:08:22<?, ?it/s]
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Training...:  34% 1500/4393 [2:08:14<1:21:17,  1.69s/it]
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Training...:  35% 1524/4393 [2:10:37<4:14:50,  5.33s/it]
Epoch ... (1/12):   0% 0/12 [2:10:44<?, ?it/s]
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Training...:  35% 1525/4393 [2:10:37<4:23:47,  5.52s/it]
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Training...:  35% 1549/4393 [2:12:06<1:24:52,  1.79s/it]
Epoch ... (1/12):   0% 0/12 [2:12:13<?, ?it/s]
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Training...:  35% 1550/4393 [2:12:06<1:17:28,  1.64s/it]
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Epoch ... (1/12):   0% 0/12 [2:14:36<?, ?it/s]
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Training...:  36% 1575/4393 [2:14:29<4:20:44,  5.55s/it]
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Epoch ... (1/12):   0% 0/12 [2:16:08<?, ?it/s]
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Training...:  36% 1600/4393 [2:16:01<1:20:49,  1.74s/it]
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+
                                                        

                                              

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Epoch ... (1/12):   0% 0/12 [2:18:29<?, ?it/s]
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Training...:  37% 1625/4393 [2:18:22<4:10:29,  5.43s/it]
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+
                                                        

                                              

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Epoch ... (1/12):   0% 0/12 [2:19:57<?, ?it/s]
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Training...:  38% 1650/4393 [2:19:50<1:15:50,  1.66s/it]
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Epoch ... (1/12):   0% 0/12 [2:22:19<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [2:23:49<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [2:26:10<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [2:27:39<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [2:30:00<?, ?it/s]
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Training...:  40% 1775/4393 [2:29:53<3:59:32,  5.49s/it]
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Training...:  41% 1799/4393 [2:31:23<1:21:22,  1.88s/it]
Epoch ... (1/12):   0% 0/12 [2:31:31<?, ?it/s]
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Training...:  42% 1824/4393 [2:33:45<3:46:43,  5.30s/it]Step... (25 | Loss: 8.328937530517578, Learning Rate: 4.800000169780105e-06, Gradient Norm: 87.96355438232422)
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+Step... (900 | Loss: 3.417492389678955, Learning Rate: 9.91939305095002e-05, Gradient Norm: 4.565223217010498)
+Step... (925 | Loss: 4.
Epoch ... (1/12):   0% 0/12 [2:33:52<?, ?it/s]
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Training...:  42% 1825/4393 [2:33:45<3:55:59,  5.51s/it]176679611206055, Learning Rate: 9.914342808770016e-05, Gradient Norm: 2.110710859298706)
+Step... (950 | Loss: 3.143075704574585, Learning Rate: 9.909292566590011e-05, Gradient Norm: 4.653733730316162)
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+Step... (1175 | Loss: 3.5336978435516357, Learning Rate: 9.863838204182684e-05, Gradient Norm: 2.5669169425964355)
+Step... (1200 | Loss: 2.0677130222320557, Learning Rate: 9.858787234406918e-05, Gradient Norm: 8.548190116882324)
+Step... (1225 | Loss: 3.1775474548339844, Learning Rate: 9.853736992226914e-05, Gradient Norm: 2.488553047180176)
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+Step... (1475 | Loss: 0.74889075756073, Learning Rate: 9.803232387639582e-05, Gradient Norm: 2.2315526008605957)
+Step... (1500 | Loss: 0.7395172119140625, Learning Rate: 9.798181417863816e-05, Gradient Norm: 3.811149835586548)
+Step... (1525 | Loss: 0.6412907838821411, Learning Rate: 9.793131175683811e-05, Gradient Norm: 1.9883421659469604)
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+Step... (1575 | Loss: 0.5415894985198975, Learning Rate: 9.78302996372804e-05, Gradient Norm: 1.5578198432922363)
+Step... (1600 | Loss: 0.7398551106452942, Learning Rate: 9.777979721548036e-05, Gradient Norm: 4.611051082611084)
+Step... (1625 | Loss: 0.4547487199306488, Learning Rate: 9.772929479368031e-05, Gradient Norm: 1.6650770902633667)
+Step... (1650 | Loss: 0.49022048711776733, Learning Rate: 9.767878509592265e-05, Gradient Norm: 3.387274980545044)
+Step... (1675 | Loss: 0.5778527855873108, Learning Rate: 9.76282826741226e-05, Gradient Norm: 1.6570004224777222)
+Step... (1700 | Loss: 0.7402584552764893, Learning Rate: 9.757778025232255e-05, Gradient Norm: 4.378363132476807)
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+Step... (1750 | Loss: 0.8252610564231873, Learning Rate: 9.747676813276485e-05, Gradient Norm: 3.8003933429718018)
+Step... (1775 | Loss: 0.46149566769599915, Learning Rate: 9.74262657109648e-05, Gradient Norm: 1.3506320714950562)
+Step... (1800 | Loss: 0.564339280128479, Learning Rate: 9.737575601320714e-05, Gradient Norm: 3.2737865447998047)
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Training...:  42% 1826/4393 [2:33:51<3:53:28,  5.46s/it]
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Training...:  42% 1827/4393 [2:33:56<3:49:45,  5.37s/it]
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Training...:  42% 1849/4393 [2:35:13<1:13:56,  1.74s/it]
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+
Training...:  42% 1849/4393 [2:35:14<1:13:56,  1.74s/it]
Epoch ... (1/12):   0% 0/12 [2:35:21<?, ?it/s]
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Training...:  42% 1850/4393 [2:35:14<1:07:43,  1.60s/it]
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Training...:  42% 1851/4393 [2:35:20<2:04:04,  2.93s/it]
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Training...:  42% 1860/4393 [2:36:14<4:02:18,  5.74s/it]
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Training...:  42% 1861/4393 [2:36:19<4:00:10,  5.69s/it]
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Training...:  42% 1862/4393 [2:36:25<4:01:02,  5.71s/it]
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Training...:  43% 1874/4393 [2:37:30<3:41:41,  5.28s/it]
+
                                                        

                                              

+
Training...:  43% 1874/4393 [2:37:36<3:41:41,  5.28s/it]
Epoch ... (1/12):   0% 0/12 [2:37:43<?, ?it/s]
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Training...:  43% 1875/4393 [2:37:36<3:49:40,  5.47s/it]
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Training...:  43% 1876/4393 [2:37:41<3:45:13,  5.37s/it]
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Training...:  43% 1877/4393 [2:37:46<3:40:35,  5.26s/it]
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Training...:  43% 1878/4393 [2:37:51<3:36:56,  5.18s/it]
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Training...:  43% 1879/4393 [2:37:56<3:34:06,  5.11s/it]
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Training...:  43% 1880/4393 [2:38:01<3:31:31,  5.05s/it]
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Training...:  43% 1881/4393 [2:38:06<3:27:38,  4.96s/it]
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Training...:  43% 1882/4393 [2:38:11<3:24:52,  4.90s/it]
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Training...:  43% 1883/4393 [2:38:15<3:21:24,  4.81s/it]
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Training...:  43% 1884/4393 [2:38:20<3:18:39,  4.75s/it]
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Training...:  43% 1885/4393 [2:38:24<3:14:58,  4.66s/it]
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Training...:  43% 1886/4393 [2:38:29<3:11:16,  4.58s/it]
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Training...:  43% 1887/4393 [2:38:33<3:05:47,  4.45s/it]
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Training...:  43% 1888/4393 [2:38:37<3:00:50,  4.33s/it]
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Training...:  43% 1889/4393 [2:38:41<2:54:48,  4.19s/it]
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Training...:  43% 1890/4393 [2:38:44<2:46:37,  3.99s/it]
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Training...:  43% 1891/4393 [2:38:48<2:37:46,  3.78s/it]
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Training...:  43% 1892/4393 [2:38:51<2:28:36,  3.57s/it]
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Training...:  43% 1893/4393 [2:38:54<2:19:20,  3.34s/it]
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Training...:  43% 1894/4393 [2:38:56<2:09:33,  3.11s/it]
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Training...:  43% 1895/4393 [2:38:58<1:59:16,  2.86s/it]
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Training...:  43% 1896/4393 [2:39:01<1:49:35,  2.63s/it]
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Training...:  43% 1897/4393 [2:39:02<1:39:23,  2.39s/it]
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Training...:  43% 1898/4393 [2:39:04<1:28:34,  2.13s/it]
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Training...:  43% 1899/4393 [2:39:05<1:17:38,  1.87s/it]
+
                                                        

                                              

+
Training...:  43% 1899/4393 [2:39:06<1:17:38,  1.87s/it]
Epoch ... (1/12):   0% 0/12 [2:39:14<?, ?it/s]
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Training...:  43% 1900/4393 [2:39:06<1:10:20,  1.69s/it]
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Training...:  43% 1901/4393 [2:39:13<2:05:14,  3.02s/it]
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Training...:  43% 1902/4393 [2:39:19<2:45:29,  3.99s/it]
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Training...:  43% 1903/4393 [2:39:25<3:08:53,  4.55s/it]
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Training...:  43% 1904/4393 [2:39:31<3:27:05,  4.99s/it]
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Training...:  43% 1905/4393 [2:39:37<3:38:16,  5.26s/it]
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Training...:  43% 1906/4393 [2:39:43<3:46:24,  5.46s/it]
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Training...:  43% 1907/4393 [2:39:48<3:50:23,  5.56s/it]
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Training...:  43% 1908/4393 [2:39:54<3:54:10,  5.65s/it]
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Training...:  43% 1909/4393 [2:40:00<3:54:10,  5.66s/it]
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Training...:  43% 1910/4393 [2:40:06<3:55:30,  5.69s/it]
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Training...:  44% 1911/4393 [2:40:11<3:54:50,  5.68s/it]
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Training...:  44% 1912/4393 [2:40:17<3:54:55,  5.68s/it]
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Training...:  44% 1913/4393 [2:40:22<3:53:12,  5.64s/it]
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Training...:  44% 1914/4393 [2:40:28<3:54:06,  5.67s/it]
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Training...:  44% 1915/4393 [2:40:34<3:51:53,  5.61s/it]
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Training...:  44% 1916/4393 [2:40:39<3:51:45,  5.61s/it]
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Training...:  44% 1917/4393 [2:40:45<3:49:23,  5.56s/it]
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Training...:  44% 1918/4393 [2:40:50<3:48:28,  5.54s/it]
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Training...:  44% 1919/4393 [2:40:56<3:45:56,  5.48s/it]
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Training...:  44% 1920/4393 [2:41:01<3:44:55,  5.46s/it]
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Training...:  44% 1921/4393 [2:41:06<3:42:33,  5.40s/it]
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Training...:  44% 1922/4393 [2:41:12<3:41:33,  5.38s/it]
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Training...:  44% 1923/4393 [2:41:17<3:39:38,  5.34s/it]
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Training...:  44% 1924/4393 [2:41:22<3:39:49,  5.34s/it]
+
                                                        

                                              

+
Training...:  44% 1924/4393 [2:41:28<3:39:49,  5.34s/it]
Epoch ... (1/12):   0% 0/12 [2:41:35<?, ?it/s]
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Training...:  44% 1925/4393 [2:41:28<3:46:12,  5.50s/it]
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Training...:  44% 1926/4393 [2:41:33<3:41:44,  5.39s/it]
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Training...:  44% 1927/4393 [2:41:38<3:36:26,  5.27s/it]
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Training...:  44% 1928/4393 [2:41:43<3:33:44,  5.20s/it]
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Training...:  44% 1929/4393 [2:41:48<3:30:08,  5.12s/it]
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Training...:  44% 1930/4393 [2:41:53<3:26:16,  5.02s/it]
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Training...:  44% 1931/4393 [2:41:58<3:22:09,  4.93s/it]
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Training...:  44% 1932/4393 [2:42:02<3:20:09,  4.88s/it]
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Training...:  44% 1933/4393 [2:42:07<3:15:42,  4.77s/it]
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Training...:  44% 1934/4393 [2:42:11<3:12:20,  4.69s/it]
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Training...:  44% 1938/4393 [2:42:28<2:52:00,  4.20s/it]
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Training...:  44% 1939/4393 [2:42:32<2:44:33,  4.02s/it]
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Training...:  44% 1940/4393 [2:42:35<2:36:46,  3.83s/it]
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Training...:  44% 1941/4393 [2:42:38<2:28:11,  3.63s/it]
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Training...:  44% 1944/4393 [2:42:46<2:00:38,  2.96s/it]
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Training...:  44% 1945/4393 [2:42:48<1:50:49,  2.72s/it]
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Training...:  44% 1949/4393 [2:42:55<1:13:26,  1.80s/it]
+
                                                        

                                              

+
Training...:  44% 1949/4393 [2:42:56<1:13:26,  1.80s/it]
Epoch ... (1/12):   0% 0/12 [2:43:03<?, ?it/s]
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Training...:  44% 1950/4393 [2:42:56<1:06:52,  1.64s/it]
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+
                                                        

                                              

+
Training...:  45% 1974/4393 [2:45:17<3:33:15,  5.29s/it]
Epoch ... (1/12):   0% 0/12 [2:45:24<?, ?it/s]
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Training...:  45% 1983/4393 [2:45:56<3:11:56,  4.78s/it]
+
Training...:  45% 1984/4393 [2:46:01<3:09:43,  4.73s/it]
+
Training...:  45% 1985/4393 [2:46:05<3:06:12,  4.64s/it]
+
Training...:  45% 1986/4393 [2:46:09<3:03:37,  4.58s/it]
+
Training...:  45% 1987/4393 [2:46:14<2:59:21,  4.47s/it]
+
Training...:  45% 1988/4393 [2:46:18<2:52:33,  4.31s/it]
+
Training...:  45% 1989/4393 [2:46:21<2:45:16,  4.13s/it]
+
Training...:  45% 1990/4393 [2:46:25<2:37:02,  3.92s/it]
+
Training...:  45% 1991/4393 [2:46:28<2:28:15,  3.70s/it]
+
Training...:  45% 1992/4393 [2:46:31<2:18:48,  3.47s/it]
+
Training...:  45% 1993/4393 [2:46:33<2:08:50,  3.22s/it]
+
Training...:  45% 1994/4393 [2:46:36<1:59:18,  2.98s/it]
+
Training...:  45% 1995/4393 [2:46:38<1:49:27,  2.74s/it]
+
Training...:  45% 1996/4393 [2:46:40<1:39:33,  2.49s/it]
+
Training...:  45% 1997/4393 [2:46:42<1:29:18,  2.24s/it]
+
Training...:  45% 1998/4393 [2:46:43<1:19:26,  1.99s/it]
+
Training...:  46% 1999/4393 [2:46:44<1:09:34,  1.74s/it]
+
                                                        

                                              

+
Training...:  46% 1999/4393 [2:46:45<1:09:34,  1.74s/it]
Epoch ... (1/12):   0% 0/12 [2:46:53<?, ?it/s]
+
Training...:  46% 2000/4393 [2:46:45<1:03:51,  1.60s/it]
+
Training...:  46% 2001/4393 [2:46:52<1:56:57,  2.93s/it]
+
Training...:  46% 2002/4393 [2:46:58<2:35:01,  3.89s/it]
+
Training...:  46% 2003/4393 [2:47:04<2:59:39,  4.51s/it]
+
Training...:  46% 2004/4393 [2:47:10<3:17:24,  4.96s/it]
+
Training...:  46% 2005/4393 [2:47:15<3:27:33,  5.21s/it]
+
Training...:  46% 2006/4393 [2:47:21<3:36:39,  5.45s/it]
+
Training...:  46% 2007/4393 [2:47:27<3:40:47,  5.55s/it]
+
Training...:  46% 2008/4393 [2:47:33<3:44:03,  5.64s/it]
+
Training...:  46% 2009/4393 [2:47:39<3:44:35,  5.65s/it]
+
Training...:  46% 2010/4393 [2:47:45<3:46:17,  5.70s/it]
+
Training...:  46% 2011/4393 [2:47:50<3:45:54,  5.69s/it]
+
Training...:  46% 2012/4393 [2:47:56<3:46:04,  5.70s/it]
+
Training...:  46% 2013/4393 [2:48:01<3:44:22,  5.66s/it]
+
Training...:  46% 2014/4393 [2:48:07<3:43:50,  5.65s/it]
+
Training...:  46% 2015/4393 [2:48:13<3:41:52,  5.60s/it]
+
Training...:  46% 2016/4393 [2:48:18<3:41:14,  5.58s/it]
+
Training...:  46% 2017/4393 [2:48:24<3:40:49,  5.58s/it]
+
Training...:  46% 2018/4393 [2:48:29<3:40:48,  5.58s/it]
+
Training...:  46% 2019/4393 [2:48:35<3:37:12,  5.49s/it]
+
Training...:  46% 2020/4393 [2:48:40<3:35:16,  5.44s/it]
+
Training...:  46% 2021/4393 [2:48:45<3:32:20,  5.37s/it]
+
Training...:  46% 2022/4393 [2:48:50<3:30:53,  5.34s/it]
+
Training...:  46% 2023/4393 [2:48:55<3:28:43,  5.28s/it]
+
Training...:  46% 2024/4393 [2:49:01<3:27:25,  5.25s/it]
+
                                                        

                                              

+
Training...:  46% 2024/4393 [2:49:07<3:27:25,  5.25s/it]
Epoch ... (1/12):   0% 0/12 [2:49:14<?, ?it/s]
+
Training...:  46% 2025/4393 [2:49:07<3:34:48,  5.44s/it]
+
Training...:  46% 2026/4393 [2:49:12<3:31:05,  5.35s/it]
+
Training...:  46% 2027/4393 [2:49:17<3:25:19,  5.21s/it]
+
Training...:  46% 2028/4393 [2:49:21<3:21:42,  5.12s/it]
+
Training...:  46% 2029/4393 [2:49:26<3:17:29,  5.01s/it]
+
Training...:  46% 2030/4393 [2:49:31<3:14:22,  4.94s/it]
+
Training...:  46% 2031/4393 [2:49:36<3:12:36,  4.89s/it]
+
Training...:  46% 2032/4393 [2:49:40<3:09:52,  4.83s/it]
+
Training...:  46% 2033/4393 [2:49:45<3:06:19,  4.74s/it]
+
Training...:  46% 2034/4393 [2:49:49<3:02:46,  4.65s/it]
+
Training...:  46% 2035/4393 [2:49:54<2:59:09,  4.56s/it]
+
Training...:  46% 2036/4393 [2:49:58<2:54:43,  4.45s/it]
+
Training...:  46% 2037/4393 [2:50:02<2:49:42,  4.32s/it]
+
Training...:  46% 2038/4393 [2:50:06<2:44:06,  4.18s/it]
+
Training...:  46% 2039/4393 [2:50:10<2:37:57,  4.03s/it]
+
Training...:  46% 2040/4393 [2:50:13<2:30:22,  3.83s/it]
+
Training...:  46% 2041/4393 [2:50:16<2:22:16,  3.63s/it]
+
Training...:  46% 2042/4393 [2:50:19<2:13:56,  3.42s/it]
+
Training...:  47% 2043/4393 [2:50:22<2:05:49,  3.21s/it]
+
Training...:  47% 2044/4393 [2:50:24<1:57:22,  3.00s/it]
+
Training...:  47% 2045/4393 [2:50:26<1:48:11,  2.76s/it]
+
Training...:  47% 2046/4393 [2:50:28<1:38:49,  2.53s/it]
+
Training...:  47% 2047/4393 [2:50:30<1:29:18,  2.28s/it]
+
Training...:  47% 2048/4393 [2:50:32<1:19:32,  2.04s/it]
+
Training...:  47% 2049/4393 [2:50:33<1:09:15,  1.77s/it]
+
                                                        

                                              

+
Training...:  47% 2049/4393 [2:50:34<1:09:15,  1.77s/it]
Epoch ... (1/12):   0% 0/12 [2:50:41<?, ?it/s]
+
Training...:  47% 2050/4393 [2:50:34<1:02:59,  1.61s/it]
+
Training...:  47% 2051/4393 [2:50:40<1:56:11,  2.98s/it]
+
Training...:  47% 2052/4393 [2:50:47<2:36:32,  4.01s/it]
+
Training...:  47% 2053/4393 [2:50:52<2:58:42,  4.58s/it]
+
Training...:  47% 2054/4393 [2:50:58<3:15:14,  5.01s/it]
+
Training...:  47% 2055/4393 [2:51:04<3:24:29,  5.25s/it]
+
Training...:  47% 2056/4393 [2:51:10<3:32:17,  5.45s/it]
+
Training...:  47% 2057/4393 [2:51:16<3:35:19,  5.53s/it]
+
Training...:  47% 2058/4393 [2:51:22<3:38:53,  5.62s/it]
+
Training...:  47% 2059/4393 [2:51:28<3:40:02,  5.66s/it]
+
Training...:  47% 2060/4393 [2:51:33<3:41:14,  5.69s/it]
+
Training...:  47% 2061/4393 [2:51:39<3:40:08,  5.66s/it]
+
Training...:  47% 2062/4393 [2:51:45<3:41:04,  5.69s/it]
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Training...:  47% 2063/4393 [2:51:50<3:40:20,  5.67s/it]
+
Training...:  47% 2064/4393 [2:51:56<3:39:52,  5.66s/it]
+
Training...:  47% 2065/4393 [2:52:01<3:37:15,  5.60s/it]
+
Training...:  47% 2066/4393 [2:52:07<3:36:52,  5.59s/it]
+
Training...:  47% 2067/4393 [2:52:12<3:34:36,  5.54s/it]
+
Training...:  47% 2068/4393 [2:52:18<3:33:50,  5.52s/it]
+
Training...:  47% 2069/4393 [2:52:23<3:32:19,  5.48s/it]
+
Training...:  47% 2070/4393 [2:52:29<3:33:30,  5.51s/it]
+
Training...:  47% 2071/4393 [2:52:34<3:31:32,  5.47s/it]
+
Training...:  47% 2072/4393 [2:52:40<3:30:26,  5.44s/it]
+
Training...:  47% 2073/4393 [2:52:45<3:27:15,  5.36s/it]
+
Training...:  47% 2074/4393 [2:52:50<3:25:27,  5.32s/it]
+
                                                        

                                              

+
Training...:  47% 2074/4393 [2:52:56<3:25:27,  5.32s/it]
Epoch ... (1/12):   0% 0/12 [2:53:03<?, ?it/s]
+
Training...:  47% 2075/4393 [2:52:56<3:33:19,  5.52s/it]
+
Training...:  47% 2076/4393 [2:53:01<3:29:03,  5.41s/it]
+
Training...:  47% 2077/4393 [2:53:06<3:24:58,  5.31s/it]
+
Training...:  47% 2078/4393 [2:53:11<3:22:48,  5.26s/it]
+
Training...:  47% 2079/4393 [2:53:16<3:18:49,  5.16s/it]
+
Training...:  47% 2080/4393 [2:53:21<3:15:23,  5.07s/it]
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Training...:  47% 2081/4393 [2:53:26<3:12:14,  4.99s/it]
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Training...:  47% 2082/4393 [2:53:31<3:09:38,  4.92s/it]
+
Training...:  47% 2083/4393 [2:53:35<3:06:16,  4.84s/it]
+
Training...:  47% 2084/4393 [2:53:40<3:03:02,  4.76s/it]
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Training...:  47% 2085/4393 [2:53:44<2:59:30,  4.67s/it]
+
Training...:  47% 2086/4393 [2:53:49<2:55:52,  4.57s/it]
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Training...:  48% 2087/4393 [2:53:53<2:51:27,  4.46s/it]
+
Training...:  48% 2088/4393 [2:53:57<2:46:26,  4.33s/it]
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Training...:  48% 2089/4393 [2:54:01<2:40:24,  4.18s/it]
+
Training...:  48% 2090/4393 [2:54:04<2:34:37,  4.03s/it]
+
Training...:  48% 2091/4393 [2:54:08<2:26:59,  3.83s/it]
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Training...:  48% 2092/4393 [2:54:11<2:19:44,  3.64s/it]
+
Training...:  48% 2093/4393 [2:54:14<2:10:42,  3.41s/it]
+
Training...:  48% 2094/4393 [2:54:16<2:01:30,  3.17s/it]
+
Training...:  48% 2095/4393 [2:54:19<1:51:20,  2.91s/it]
+
Training...:  48% 2096/4393 [2:54:21<1:41:01,  2.64s/it]
+
Training...:  48% 2097/4393 [2:54:22<1:30:34,  2.37s/it]
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Training...:  48% 2098/4393 [2:54:24<1:20:30,  2.10s/it]
+
Training...:  48% 2099/4393 [2:54:25<1:10:25,  1.84s/it]
+
                                                        

                                              

+
Training...:  48% 2099/4393 [2:54:26<1:10:25,  1.84s/it]
Epoch ... (1/12):   0% 0/12 [2:54:34<?, ?it/s]
+
Training...:  48% 2100/4393 [2:54:26<1:03:47,  1.67s/it]
+
Training...:  48% 2101/4393 [2:54:33<1:54:55,  3.01s/it]
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Training...:  48% 2102/4393 [2:54:39<2:31:25,  3.97s/it]
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Training...:  48% 2103/4393 [2:54:45<2:55:43,  4.60s/it]
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Training...:  48% 2104/4393 [2:54:51<3:12:25,  5.04s/it]
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Training...:  48% 2105/4393 [2:54:57<3:20:53,  5.27s/it]
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Training...:  48% 2106/4393 [2:55:03<3:27:30,  5.44s/it]
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Training...:  48% 2107/4393 [2:55:08<3:32:10,  5.57s/it]
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Training...:  48% 2108/4393 [2:55:14<3:34:25,  5.63s/it]
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Training...:  48% 2109/4393 [2:55:20<3:34:36,  5.64s/it]
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Training...:  48% 2110/4393 [2:55:26<3:35:13,  5.66s/it]
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Training...:  48% 2111/4393 [2:55:31<3:35:00,  5.65s/it]
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Training...:  48% 2112/4393 [2:55:37<3:35:50,  5.68s/it]
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Training...:  48% 2113/4393 [2:55:43<3:34:59,  5.66s/it]
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Training...:  48% 2114/4393 [2:55:48<3:34:22,  5.64s/it]
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Training...:  48% 2115/4393 [2:55:54<3:32:16,  5.59s/it]
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Training...:  48% 2116/4393 [2:55:59<3:31:48,  5.58s/it]
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Training...:  48% 2117/4393 [2:56:05<3:30:52,  5.56s/it]
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Training...:  48% 2118/4393 [2:56:10<3:29:26,  5.52s/it]
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Training...:  48% 2119/4393 [2:56:16<3:27:39,  5.48s/it]
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Training...:  48% 2120/4393 [2:56:21<3:25:50,  5.43s/it]
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Training...:  48% 2121/4393 [2:56:26<3:23:00,  5.36s/it]
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Training...:  48% 2122/4393 [2:56:31<3:22:55,  5.36s/it]
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Training...:  48% 2123/4393 [2:56:37<3:20:48,  5.31s/it]
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Training...:  48% 2124/4393 [2:56:42<3:19:15,  5.27s/it]
+
                                                        

                                              

+
Training...:  48% 2124/4393 [2:56:48<3:19:15,  5.27s/it]
Epoch ... (1/12):   0% 0/12 [2:56:55<?, ?it/s]
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Training...:  48% 2125/4393 [2:56:48<3:26:08,  5.45s/it]
+
Training...:  48% 2126/4393 [2:56:53<3:22:33,  5.36s/it]
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Training...:  48% 2127/4393 [2:56:58<3:18:53,  5.27s/it]
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Training...:  48% 2128/4393 [2:57:03<3:17:05,  5.22s/it]
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Training...:  48% 2129/4393 [2:57:08<3:16:03,  5.20s/it]
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Training...:  48% 2130/4393 [2:57:13<3:13:08,  5.12s/it]
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Training...:  49% 2131/4393 [2:57:18<3:08:15,  4.99s/it]
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Training...:  49% 2132/4393 [2:57:22<3:05:11,  4.91s/it]
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Training...:  49% 2133/4393 [2:57:27<3:00:56,  4.80s/it]
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Training...:  49% 2134/4393 [2:57:32<2:57:45,  4.72s/it]
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Training...:  49% 2135/4393 [2:57:36<2:53:47,  4.62s/it]
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Training...:  49% 2136/4393 [2:57:40<2:49:17,  4.50s/it]
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Training...:  49% 2137/4393 [2:57:44<2:43:57,  4.36s/it]
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Training...:  49% 2138/4393 [2:57:48<2:37:38,  4.19s/it]
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Training...:  49% 2139/4393 [2:57:52<2:31:55,  4.04s/it]
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Training...:  49% 2140/4393 [2:57:55<2:24:04,  3.84s/it]
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Training...:  49% 2141/4393 [2:57:58<2:16:19,  3.63s/it]
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Training...:  49% 2142/4393 [2:58:01<2:07:25,  3.40s/it]
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Training...:  49% 2143/4393 [2:58:04<1:58:27,  3.16s/it]
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Training...:  49% 2144/4393 [2:58:06<1:49:33,  2.92s/it]
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Training...:  49% 2145/4393 [2:58:08<1:40:22,  2.68s/it]
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Training...:  49% 2146/4393 [2:58:10<1:31:31,  2.44s/it]
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Training...:  49% 2147/4393 [2:58:12<1:23:10,  2.22s/it]
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Training...:  49% 2148/4393 [2:58:13<1:14:22,  1.99s/it]
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Training...:  49% 2149/4393 [2:58:14<1:05:37,  1.75s/it]
+
                                                        

                                              

+
Training...:  49% 2149/4393 [2:58:16<1:05:37,  1.75s/it]
Epoch ... (1/12):   0% 0/12 [2:58:23<?, ?it/s]
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Training...:  49% 2150/4393 [2:58:16<1:00:21,  1.61s/it]
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Training...:  49% 2151/4393 [2:58:22<1:49:14,  2.92s/it]
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Training...:  49% 2152/4393 [2:58:28<2:24:48,  3.88s/it]
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Training...:  49% 2153/4393 [2:58:34<2:47:12,  4.48s/it]
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Training...:  49% 2154/4393 [2:58:40<3:03:40,  4.92s/it]
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Training...:  49% 2155/4393 [2:58:45<3:13:12,  5.18s/it]
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Training...:  49% 2156/4393 [2:58:51<3:21:50,  5.41s/it]
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Training...:  49% 2157/4393 [2:58:57<3:25:51,  5.52s/it]
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Training...:  49% 2158/4393 [2:59:03<3:28:41,  5.60s/it]
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Training...:  49% 2159/4393 [2:59:09<3:29:02,  5.61s/it]
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Training...:  49% 2160/4393 [2:59:14<3:31:21,  5.68s/it]
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Training...:  49% 2161/4393 [2:59:20<3:32:42,  5.72s/it]
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Training...:  49% 2162/4393 [2:59:26<3:32:26,  5.71s/it]
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Training...:  49% 2163/4393 [2:59:31<3:30:32,  5.66s/it]
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Training...:  49% 2164/4393 [2:59:37<3:29:53,  5.65s/it]
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Training...:  49% 2165/4393 [2:59:42<3:27:09,  5.58s/it]
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Training...:  49% 2166/4393 [2:59:48<3:26:20,  5.56s/it]
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Training...:  49% 2167/4393 [2:59:53<3:24:50,  5.52s/it]
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Training...:  49% 2168/4393 [2:59:59<3:24:20,  5.51s/it]
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Training...:  49% 2169/4393 [3:00:04<3:22:07,  5.45s/it]
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Training...:  49% 2170/4393 [3:00:10<3:20:53,  5.42s/it]
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Training...:  49% 2171/4393 [3:00:15<3:19:14,  5.38s/it]
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Training...:  49% 2172/4393 [3:00:20<3:17:57,  5.35s/it]
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Training...:  49% 2173/4393 [3:00:25<3:16:14,  5.30s/it]
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Training...:  49% 2174/4393 [3:00:30<3:14:37,  5.26s/it]
+
                                                        

                                              

+
Training...:  49% 2174/4393 [3:00:36<3:14:37,  5.26s/it]
Epoch ... (1/12):   0% 0/12 [3:00:44<?, ?it/s]
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Training...:  50% 2175/4393 [3:00:36<3:21:13,  5.44s/it]
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Training...:  50% 2176/4393 [3:00:41<3:16:52,  5.33s/it]
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Training...:  50% 2177/4393 [3:00:46<3:12:17,  5.21s/it]
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Training...:  50% 2178/4393 [3:00:51<3:10:04,  5.15s/it]
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Training...:  50% 2179/4393 [3:00:56<3:07:19,  5.08s/it]
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Training...:  50% 2180/4393 [3:01:01<3:05:27,  5.03s/it]
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Training...:  50% 2181/4393 [3:01:06<3:02:39,  4.95s/it]
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Training...:  50% 2182/4393 [3:01:11<3:00:48,  4.91s/it]
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Training...:  50% 2184/4393 [3:01:20<2:54:02,  4.73s/it]
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Training...:  50% 2185/4393 [3:01:24<2:50:49,  4.64s/it]
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Training...:  50% 2187/4393 [3:01:33<2:42:41,  4.43s/it]
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Epoch ... (1/12):   0% 0/12 [3:02:12<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [3:04:33<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [3:06:01<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [3:08:23<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [3:09:54<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [3:12:16<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [3:13:44<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [3:16:04<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [3:17:32<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [3:19:54<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [3:21:25<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [3:23:46<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [3:25:16<?, ?it/s]
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Training...:  57% 2500/4393 [3:25:09<53:26,  1.69s/it]
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Epoch ... (1/12):   0% 0/12 [3:27:37<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [3:29:06<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [3:32:56<?, ?it/s]
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Training...:  60% 2624/4393 [3:35:10<2:35:35,  5.28s/it]
Epoch ... (1/12):   0% 0/12 [3:35:17<?, ?it/s]
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Training...:  60% 2625/4393 [3:35:10<2:41:35,  5.48s/it]
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Training...:  60% 2649/4393 [3:36:38<50:40,  1.74s/it]
Epoch ... (1/12):   0% 0/12 [3:36:45<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [3:39:06<?, ?it/s]
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Training...:  61% 2675/4393 [3:38:59<2:36:03,  5.45s/it]
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Epoch ... (1/12):   0% 0/12 [3:40:34<?, ?it/s]
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Training...:  61% 2700/4393 [3:40:27<46:47,  1.66s/it]
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Epoch ... (1/12):   0% 0/12 [3:42:55<?, ?it/s]
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Training...:  62% 2725/4393 [3:42:48<2:32:07,  5.47s/it]
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Epoch ... (1/12):   0% 0/12 [3:44:25<?, ?it/s]
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Training...:  63% 2750/4393 [3:44:18<46:42,  1.71s/it]
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Epoch ... (1/12):   0% 0/12 [3:46:46<?, ?it/s]
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Training...:  64% 2799/4393 [3:48:08<47:12,  1.78s/it]
Epoch ... (1/12):   0% 0/12 [3:48:15<?, ?it/s]
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Training...:  64% 2800/4393 [3:48:08<43:08,  1.62s/it]
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Training...:  64% 2824/4393 [3:50:30<2:18:07,  5.28s/it]
Epoch ... (1/12):   0% 0/12 [3:50:37<?, ?it/s]
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Training...:  64% 2825/4393 [3:50:30<2:22:41,  5.46s/it]
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Training...:  65% 2849/4393 [3:51:58<46:02,  1.79s/it]
Epoch ... (1/12):   0% 0/12 [3:52:05<?, ?it/s]
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Training...:  65% 2850/4393 [3:51:58<42:18,  1.64s/it]
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Training...:  65% 2874/4393 [3:54:19<2:13:43,  5.28s/it]
Epoch ... (1/12):   0% 0/12 [3:54:26<?, ?it/s]
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Training...:  65% 2875/4393 [3:54:19<2:18:11,  5.46s/it]
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Training...:  65% 2876/4393 [3:54:24<2:15:08,  5.34s/it]
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Training...:  66% 2896/4393 [3:55:42<1:04:22,  2.58s/it]
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Training...:  66% 2897/4393 [3:55:44<57:57,  2.32s/it]  
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Training...:  66% 2899/4393 [3:55:47<45:21,  1.82s/it]
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Training...:  66% 2899/4393 [3:55:48<45:21,  1.82s/it]
Epoch ... (1/12):   0% 0/12 [3:55:55<?, ?it/s]
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Training...:  66% 2900/4393 [3:55:48<41:20,  1.66s/it]
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Training...:  66% 2901/4393 [3:55:54<1:14:25,  2.99s/it]
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Training...:  66% 2902/4393 [3:56:00<1:37:31,  3.92s/it]
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Training...:  67% 2924/4393 [3:58:03<2:08:39,  5.25s/it]
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Training...:  67% 2924/4393 [3:58:09<2:08:39,  5.25s/it]
Epoch ... (1/12):   0% 0/12 [3:58:16<?, ?it/s]
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Training...:  67% 2925/4393 [3:58:09<2:13:32,  5.46s/it]
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Training...:  67% 2929/4393 [3:58:29<2:03:54,  5.08s/it]
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Training...:  67% 2949/4393 [3:59:36<42:19,  1.76s/it]
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Epoch ... (1/12):   0% 0/12 [3:59:45<?, ?it/s]
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Training...:  67% 2950/4393 [3:59:38<38:57,  1.62s/it]
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Epoch ... (1/12):   0% 0/12 [4:02:06<?, ?it/s]
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Training...:  68% 2975/4393 [4:01:59<2:08:58,  5.46s/it]
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+
                                                      

                                              

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Epoch ... (1/12):   0% 0/12 [4:03:35<?, ?it/s]
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Training...:  68% 3000/4393 [4:03:27<37:47,  1.63s/it]
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Epoch ... (1/12):   0% 0/12 [4:05:56<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [4:07:24<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [4:09:45<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [4:11:14<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [4:13:36<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [4:15:06<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [4:17:27<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [4:18:54<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [4:21:16<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [4:22:44<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [4:25:06<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [4:26:34<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [4:28:55<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [4:30:23<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [4:32:44<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [4:34:12<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [4:36:34<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [4:38:05<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [4:40:26<?, ?it/s]
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Training...:  80% 3499/4393 [4:41:47<26:44,  1.79s/it]
Epoch ... (1/12):   0% 0/12 [4:41:54<?, ?it/s]
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Training...:  80% 3524/4393 [4:44:09<1:17:00,  5.32s/it]
Epoch ... (1/12):   0% 0/12 [4:44:16<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [4:45:46<?, ?it/s]
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Training...:  81% 3574/4393 [4:48:00<1:12:11,  5.29s/it]
Epoch ... (1/12):   0% 0/12 [4:48:07<?, ?it/s]
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Training...:  82% 3599/4393 [4:49:30<24:47,  1.87s/it]Step... (1825 | Loss: 0.45814454555511475, Learning Rate: 9.732525359140709e-05, Gradient Norm: 1.927361011505127)
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Epoch ... (1/12):   0% 0/12 [4:49:37<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [4:51:59<?, ?it/s]
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Training...:  83% 3649/4393 [4:53:20<22:17,  1.80s/it]
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Training...:  84% 3674/4393 [4:55:43<1:03:41,  5.31s/it]
Epoch ... (1/12):   0% 0/12 [4:55:50<?, ?it/s]
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Training...:  85% 3749/4393 [5:01:05<20:32,  1.91s/it]
Epoch ... (1/12):   0% 0/12 [5:01:12<?, ?it/s]
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Training...:  86% 3774/4393 [5:03:27<54:50,  5.32s/it]
Epoch ... (1/12):   0% 0/12 [5:03:34<?, ?it/s]
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Training...:  86% 3799/4393 [5:04:55<17:17,  1.75s/it]
Epoch ... (1/12):   0% 0/12 [5:05:02<?, ?it/s]
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Training...:  87% 3800/4393 [5:04:55<15:53,  1.61s/it]
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+
Training...:  87% 3824/4393 [5:07:17<49:45,  5.25s/it]
Epoch ... (1/12):   0% 0/12 [5:07:24<?, ?it/s]
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Training...:  87% 3825/4393 [5:07:17<52:10,  5.51s/it]
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Training...:  88% 3849/4393 [5:08:44<16:06,  1.78s/it]
+
                                                      

                                              

+
Training...:  88% 3849/4393 [5:08:45<16:06,  1.78s/it]
Epoch ... (1/12):   0% 0/12 [5:08:52<?, ?it/s]
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Training...:  88% 3850/4393 [5:08:45<14:49,  1.64s/it]
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Training...:  88% 3851/4393 [5:08:51<26:50,  2.97s/it]
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Training...:  88% 3856/4393 [5:09:21<48:40,  5.44s/it]
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Training...:  88% 3861/4393 [5:09:50<51:07,  5.77s/it]
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Training...:  88% 3874/4393 [5:11:07<45:45,  5.29s/it]
Epoch ... (1/12):   0% 0/12 [5:11:14<?, ?it/s]
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Training...:  88% 3875/4393 [5:11:07<47:35,  5.51s/it]
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Training...:  88% 3876/4393 [5:11:12<46:51,  5.44s/it]
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Training...:  88% 3881/4393 [5:11:37<42:39,  5.00s/it]
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Training...:  89% 3891/4393 [5:12:18<31:35,  3.78s/it]
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Training...:  89% 3899/4393 [5:12:37<15:39,  1.90s/it]
Epoch ... (1/12):   0% 0/12 [5:12:44<?, ?it/s]
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Training...:  89% 3900/4393 [5:12:37<14:16,  1.74s/it]
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+
                                                      

                                              

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Training...:  89% 3924/4393 [5:14:58<40:50,  5.22s/it]
Epoch ... (1/12):   0% 0/12 [5:15:05<?, ?it/s]
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+
                                                      

                                              

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Training...:  90% 3949/4393 [5:16:28<13:56,  1.89s/it]
Epoch ... (1/12):   0% 0/12 [5:16:35<?, ?it/s]
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Training...:  90% 3950/4393 [5:16:28<12:39,  1.71s/it]
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+
Training...:  90% 3974/4393 [5:18:50<36:54,  5.28s/it]
Epoch ... (1/12):   0% 0/12 [5:18:57<?, ?it/s]
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Training...:  90% 3975/4393 [5:18:50<38:44,  5.56s/it]
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+
                                                      

                                              

+
Training...:  91% 3999/4393 [5:20:19<11:44,  1.79s/it]
Epoch ... (1/12):   0% 0/12 [5:20:26<?, ?it/s]
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Training...:  91% 4000/4393 [5:20:19<10:45,  1.64s/it]
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Training...:  91% 4001/4393 [5:20:25<19:28,  2.98s/it]
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+
                                                      

                                              

+
Training...:  92% 4024/4393 [5:22:41<33:28,  5.44s/it]
Epoch ... (1/12):   0% 0/12 [5:22:49<?, ?it/s]
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Training...:  92% 4025/4393 [5:22:41<34:29,  5.62s/it]
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Training...:  92% 4026/4393 [5:22:47<33:30,  5.48s/it]
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Training...:  92% 4041/4393 [5:23:53<22:37,  3.86s/it]
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Training...:  92% 4042/4393 [5:23:56<21:12,  3.63s/it]
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Training...:  92% 4048/4393 [5:24:09<11:52,  2.06s/it]
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Training...:  92% 4049/4393 [5:24:10<10:25,  1.82s/it]
+
                                                      

                                              

+
Training...:  92% 4049/4393 [5:24:12<10:25,  1.82s/it]
Epoch ... (1/12):   0% 0/12 [5:24:19<?, ?it/s]
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Training...:  92% 4050/4393 [5:24:12<09:32,  1.67s/it]
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Training...:  92% 4051/4393 [5:24:18<17:15,  3.03s/it]
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Training...:  92% 4052/4393 [5:24:24<22:30,  3.96s/it]
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Training...:  92% 4053/4393 [5:24:30<25:51,  4.56s/it]
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Training...:  92% 4054/4393 [5:24:36<28:07,  4.98s/it]
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Training...:  92% 4055/4393 [5:24:42<29:37,  5.26s/it]
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Training...:  92% 4056/4393 [5:24:48<30:41,  5.46s/it]
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Training...:  92% 4057/4393 [5:24:54<31:24,  5.61s/it]
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Training...:  92% 4058/4393 [5:24:59<31:37,  5.67s/it]
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Training...:  92% 4059/4393 [5:25:05<31:43,  5.70s/it]
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Training...:  92% 4061/4393 [5:25:17<32:10,  5.81s/it]
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Training...:  92% 4062/4393 [5:25:23<31:48,  5.77s/it]
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Training...:  92% 4063/4393 [5:25:28<31:28,  5.72s/it]
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Training...:  93% 4071/4393 [5:26:12<28:53,  5.38s/it]
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Training...:  93% 4072/4393 [5:26:17<28:32,  5.33s/it]
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Training...:  93% 4074/4393 [5:26:28<27:57,  5.26s/it]
+
                                                      

                                              

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Training...:  93% 4074/4393 [5:26:34<27:57,  5.26s/it]
Epoch ... (1/12):   0% 0/12 [5:26:41<?, ?it/s]
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Training...:  93% 4075/4393 [5:26:34<29:03,  5.48s/it]
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Training...:  93% 4076/4393 [5:26:39<28:19,  5.36s/it]
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Training...:  93% 4078/4393 [5:26:49<27:15,  5.19s/it]
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Training...:  93% 4080/4393 [5:26:59<26:18,  5.04s/it]
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Training...:  93% 4081/4393 [5:27:03<25:47,  4.96s/it]
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Training...:  93% 4082/4393 [5:27:08<25:33,  4.93s/it]
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Training...:  93% 4083/4393 [5:27:13<24:58,  4.83s/it]
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Training...:  93% 4085/4393 [5:27:22<24:12,  4.71s/it]
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Training...:  93% 4086/4393 [5:27:26<23:28,  4.59s/it]
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Training...:  93% 4087/4393 [5:27:30<22:38,  4.44s/it]
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Training...:  93% 4088/4393 [5:27:34<21:44,  4.28s/it]
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Training...:  93% 4090/4393 [5:27:41<19:38,  3.89s/it]
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Training...:  93% 4091/4393 [5:27:44<18:30,  3.68s/it]
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Training...:  93% 4092/4393 [5:27:47<17:17,  3.45s/it]
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Training...:  93% 4093/4393 [5:27:50<16:03,  3.21s/it]
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Training...:  93% 4094/4393 [5:27:52<14:50,  2.98s/it]
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Training...:  93% 4096/4393 [5:27:57<12:27,  2.52s/it]
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Training...:  93% 4097/4393 [5:27:58<11:12,  2.27s/it]
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Training...:  93% 4098/4393 [5:28:00<09:56,  2.02s/it]
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Training...:  93% 4099/4393 [5:28:01<08:47,  1.79s/it]
+
                                                      

                                              

+
Training...:  93% 4099/4393 [5:28:02<08:47,  1.79s/it]
Epoch ... (1/12):   0% 0/12 [5:28:10<?, ?it/s]
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Training...:  93% 4100/4393 [5:28:02<08:04,  1.65s/it]
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Training...:  93% 4101/4393 [5:28:09<14:33,  2.99s/it]
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Training...:  93% 4106/4393 [5:28:39<26:13,  5.48s/it]
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Training...:  94% 4108/4393 [5:28:50<26:54,  5.67s/it]
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+
                                                      

                                              

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Training...:  94% 4124/4393 [5:30:25<24:17,  5.42s/it]
Epoch ... (1/12):   0% 0/12 [5:30:32<?, ?it/s]
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Training...:  94% 4125/4393 [5:30:25<25:03,  5.61s/it]
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+
                                                      

                                              

+
Training...:  94% 4149/4393 [5:31:57<07:44,  1.90s/it]
Epoch ... (1/12):   0% 0/12 [5:32:04<?, ?it/s]
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Training...:  94% 4150/4393 [5:31:57<07:02,  1.74s/it]
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Training...:  95% 4174/4393 [5:34:20<19:25,  5.32s/it]
Epoch ... (1/12):   0% 0/12 [5:34:27<?, ?it/s]
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Training...:  96% 4199/4393 [5:35:50<05:55,  1.83s/it]
Epoch ... (1/12):   0% 0/12 [5:35:57<?, ?it/s]
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Training...:  96% 4224/4393 [5:38:12<14:55,  5.30s/it]
Epoch ... (1/12):   0% 0/12 [5:38:20<?, ?it/s]
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Training...:  97% 4249/4393 [5:39:42<04:28,  1.86s/it]
Epoch ... (1/12):   0% 0/12 [5:39:49<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [5:42:12<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [5:43:44<?, ?it/s]
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Epoch ... (1/12):   0% 0/12 [5:46:06<?, ?it/s]
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Training...: 100% 4393/4393 [5:50:50<00:00,  4.79s/it]
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Epoch ... (1/12):   8% 1/12 [5:50:57<64:20:33, 21057.59s/it]
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Training...:   0% 0/4393 [00:00<?, ?it/s]Step... (3600 | Loss: 0.2603316903114319, Learning Rate: 9.373939246870577e-05, Gradient Norm: 1.8977075815200806)
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Epoch ... (1/12):   8% 1/12 [6:05:20<64:20:33, 21057.59s/it]
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Epoch ... (1/12):   8% 1/12 [6:09:08<64:20:33, 21057.59s/it]
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Epoch ... (1/12):   8% 1/12 [6:10:45<64:20:33, 21057.59s/it]
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Epoch ... (1/12):   8% 1/12 [6:12:57<64:20:33, 21057.59s/it]
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Epoch ... (1/12):   8% 1/12 [6:14:34<64:20:33, 21057.59s/it]
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Epoch ... (1/12):   8% 1/12 [6:16:47<64:20:33, 21057.59s/it]
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Epoch ... (1/12):   8% 1/12 [6:18:26<64:20:33, 21057.59s/it]
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Epoch ... (1/12):   8% 1/12 [6:20:39<64:20:33, 21057.59s/it]
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Training...:   9% 406/4393 [31:16<5:54:49,  5.34s/it]
Epoch ... (1/12):   8% 1/12 [6:22:16<64:20:33, 21057.59s/it]
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Training...:  10% 431/4393 [33:29<5:23:42,  4.90s/it]
Epoch ... (1/12):   8% 1/12 [6:24:29<64:20:33, 21057.59s/it]
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Training...:  10% 456/4393 [35:06<5:54:20,  5.40s/it]
Epoch ... (1/12):   8% 1/12 [6:26:06<64:20:33, 21057.59s/it]
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Training...:  11% 481/4393 [37:19<5:21:28,  4.93s/it]
Epoch ... (1/12):   8% 1/12 [6:28:18<64:20:33, 21057.59s/it]
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Training...:  11% 482/4393 [37:19<5:31:40,  5.09s/it]
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+
                                                     

                                                            

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Training...:  12% 506/4393 [38:57<5:47:52,  5.37s/it]
Epoch ... (1/12):   8% 1/12 [6:29:56<64:20:33, 21057.59s/it]
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Training...:  12% 507/4393 [38:57<6:11:19,  5.73s/it]
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+
                                                     

                                                            

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Training...:  12% 531/4393 [41:09<5:15:56,  4.91s/it]
Epoch ... (1/12):   8% 1/12 [6:32:09<64:20:33, 21057.59s/it]
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Training...:  12% 532/4393 [41:09<5:25:32,  5.06s/it]
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+
                                                     

                                                            

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Training...:  13% 556/4393 [42:46<5:41:56,  5.35s/it]
Epoch ... (1/12):   8% 1/12 [6:33:45<64:20:33, 21057.59s/it]
+
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+
                                                     

                                                            

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Training...:  13% 581/4393 [44:59<5:09:19,  4.87s/it]
Epoch ... (1/12):   8% 1/12 [6:35:58<64:20:33, 21057.59s/it]
+
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Training...:  14% 606/4393 [46:34<5:38:57,  5.37s/it]
Epoch ... (1/12):   8% 1/12 [6:37:34<64:20:33, 21057.59s/it]
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Training...:  14% 607/4393 [46:34<6:01:25,  5.73s/it]
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Training...:  14% 631/4393 [48:47<5:08:13,  4.92s/it]
Epoch ... (1/12):   8% 1/12 [6:39:47<64:20:33, 21057.59s/it]
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Training...:  14% 632/4393 [48:47<5:17:54,  5.07s/it]
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Training...:  15% 656/4393 [50:25<5:36:06,  5.40s/it]
Epoch ... (1/12):   8% 1/12 [6:41:24<64:20:33, 21057.59s/it]
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Training...:  16% 681/4393 [52:38<5:04:12,  4.92s/it]
Epoch ... (1/12):   8% 1/12 [6:43:37<64:20:33, 21057.59s/it]
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Training...:  16% 682/4393 [52:38<5:14:31,  5.09s/it]
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+
                                                     

                                                            

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Training...:  16% 706/4393 [54:15<5:33:33,  5.43s/it]
Epoch ... (1/12):   8% 1/12 [6:45:15<64:20:33, 21057.59s/it]
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Training...:  16% 707/4393 [54:15<5:58:05,  5.83s/it]
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+
                                                     

                                                            

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Training...:  17% 731/4393 [56:28<4:59:15,  4.90s/it]
Epoch ... (1/12):   8% 1/12 [6:47:28<64:20:33, 21057.59s/it]
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Training...:  17% 732/4393 [56:28<5:08:34,  5.06s/it]
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Epoch ... (1/12):   8% 1/12 [6:49:04<64:20:33, 21057.59s/it]
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Training...:  17% 757/4393 [58:05<5:50:47,  5.79s/it]
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+
                                                       

                                                            

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Training...:  18% 781/4393 [1:00:19<4:57:54,  4.95s/it]
Epoch ... (1/12):   8% 1/12 [6:51:18<64:20:33, 21057.59s/it]
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Training...:  18% 782/4393 [1:00:19<5:07:12,  5.10s/it]
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Epoch ... (1/12):   8% 1/12 [6:52:54<64:20:33, 21057.59s/it]
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Training...:  18% 807/4393 [1:01:55<5:44:05,  5.76s/it]
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Training...:  18% 808/4393 [1:02:01<5:42:21,  5.73s/it]
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Training...:  18% 809/4393 [1:02:06<5:42:54,  5.74s/it]
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Training...:  18% 810/4393 [1:02:12<5:40:59,  5.71s/it]
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Training...:  19% 831/4393 [1:04:02<4:49:18,  4.87s/it]
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Training...:  19% 831/4393 [1:04:07<4:49:18,  4.87s/it]
Epoch ... (1/12):   8% 1/12 [6:55:07<64:20:33, 21057.59s/it]
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Training...:  19% 832/4393 [1:04:07<5:00:03,  5.06s/it]
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Training...:  19% 856/4393 [1:05:37<5:19:06,  5.41s/it]
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Training...:  19% 856/4393 [1:05:44<5:19:06,  5.41s/it]
Epoch ... (1/12):   8% 1/12 [6:56:43<64:20:33, 21057.59s/it]
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Training...:  20% 857/4393 [1:05:44<5:40:59,  5.79s/it]
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Training...:  20% 881/4393 [1:07:51<4:48:29,  4.93s/it]
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Epoch ... (1/12):   8% 1/12 [6:58:56<64:20:33, 21057.59s/it]
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Training...:  20% 882/4393 [1:07:57<4:58:05,  5.09s/it]
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Training...:  21% 906/4393 [1:09:35<5:11:14,  5.36s/it]
Epoch ... (1/12):   8% 1/12 [7:00:34<64:20:33, 21057.59s/it]
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Training...:  21% 907/4393 [1:09:35<5:33:03,  5.73s/it]
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Epoch ... (1/12):   8% 1/12 [7:02:47<64:20:33, 21057.59s/it]
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Training...:  21% 932/4393 [1:11:48<4:50:22,  5.03s/it]
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Epoch ... (1/12):   8% 1/12 [7:04:25<64:20:33, 21057.59s/it]
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+
                                                       

                                                            

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Epoch ... (1/12):   8% 1/12 [7:06:37<64:20:33, 21057.59s/it]
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Training...:  22% 982/4393 [1:15:38<4:44:47,  5.01s/it]
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+
                                                        

                                                            

+
Training...:  23% 1006/4393 [1:17:14<5:04:11,  5.39s/it]
Epoch ... (1/12):   8% 1/12 [7:08:13<64:20:33, 21057.59s/it]
+
Training...:  23% 1007/4393 [1:17:14<5:24:41,  5.75s/it]
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Training...:  23% 1008/4393 [1:17:19<5:23:31,  5.73s/it]
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Training...:  23% 1011/4393 [1:17:37<5:25:06,  5.77s/it]
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Training...:  23% 1024/4393 [1:18:47<5:00:49,  5.36s/it]
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Training...:  23% 1031/4393 [1:19:22<4:34:08,  4.89s/it]
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+
Training...:  23% 1031/4393 [1:19:27<4:34:08,  4.89s/it]
Epoch ... (1/12):   8% 1/12 [7:10:27<64:20:33, 21057.59s/it]
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Training...:  23% 1032/4393 [1:19:27<4:42:49,  5.05s/it]
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+
Training...:  24% 1056/4393 [1:21:03<4:57:36,  5.35s/it]
Epoch ... (1/12):   8% 1/12 [7:12:02<64:20:33, 21057.59s/it]
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Training...:  24% 1057/4393 [1:21:03<5:18:45,  5.73s/it]
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Training...:  25% 1079/4393 [1:23:01<4:38:09,  5.04s/it]
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Training...:  25% 1080/4393 [1:23:06<4:34:07,  4.96s/it]
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Training...:  25% 1081/4393 [1:23:10<4:30:36,  4.90s/it]
+
                                                        

                                                            

+
Training...:  25% 1081/4393 [1:23:16<4:30:36,  4.90s/it]
Epoch ... (1/12):   8% 1/12 [7:14:15<64:20:33, 21057.59s/it]
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Training...:  25% 1082/4393 [1:23:16<4:40:04,  5.08s/it]
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Training...:  25% 1083/4393 [1:23:21<4:33:08,  4.95s/it]
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Training...:  25% 1088/4393 [1:23:42<3:58:52,  4.34s/it]
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Training...:  25% 1089/4393 [1:23:46<3:51:40,  4.21s/it]
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Training...:  25% 1091/4393 [1:23:53<3:30:26,  3.82s/it]
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Training...:  25% 1092/4393 [1:23:56<3:18:29,  3.61s/it]
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Training...:  25% 1095/4393 [1:24:04<2:37:52,  2.87s/it]
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Training...:  25% 1096/4393 [1:24:06<2:22:58,  2.60s/it]
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Training...:  25% 1097/4393 [1:24:07<2:08:07,  2.33s/it]
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Training...:  25% 1098/4393 [1:24:09<1:53:57,  2.08s/it]
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Training...:  25% 1099/4393 [1:24:10<1:39:41,  1.82s/it]
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Training...:  25% 1100/4393 [1:24:11<1:25:55,  1.57s/it]
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Training...:  25% 1101/4393 [1:24:17<2:39:29,  2.91s/it]
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Training...:  25% 1102/4393 [1:24:23<3:31:46,  3.86s/it]
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Training...:  25% 1103/4393 [1:24:29<4:04:35,  4.46s/it]
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Training...:  25% 1104/4393 [1:24:35<4:27:23,  4.88s/it]
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Training...:  25% 1105/4393 [1:24:41<4:42:50,  5.16s/it]
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Training...:  25% 1106/4393 [1:24:46<4:53:27,  5.36s/it]
+
                                                        

                                                            

+
Training...:  25% 1106/4393 [1:24:53<4:53:27,  5.36s/it]
Epoch ... (1/12):   8% 1/12 [7:15:53<64:20:33, 21057.59s/it]
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Training...:  25% 1107/4393 [1:24:53<5:14:10,  5.74s/it]
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Training...:  25% 1108/4393 [1:24:59<5:12:58,  5.72s/it]
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Training...:  25% 1109/4393 [1:25:04<5:12:41,  5.71s/it]
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Training...:  25% 1110/4393 [1:25:10<5:10:36,  5.68s/it]
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Training...:  25% 1111/4393 [1:25:16<5:11:29,  5.69s/it]
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Training...:  25% 1112/4393 [1:25:22<5:15:37,  5.77s/it]
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Training...:  25% 1113/4393 [1:25:27<5:12:49,  5.72s/it]
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Training...:  25% 1114/4393 [1:25:33<5:08:53,  5.65s/it]
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Training...:  25% 1115/4393 [1:25:38<5:06:54,  5.62s/it]
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Training...:  25% 1116/4393 [1:25:44<5:04:39,  5.58s/it]
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Training...:  25% 1117/4393 [1:25:49<5:02:15,  5.54s/it]
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Training...:  25% 1118/4393 [1:25:55<4:58:58,  5.48s/it]
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Training...:  25% 1120/4393 [1:26:05<4:54:39,  5.40s/it]
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Training...:  26% 1121/4393 [1:26:11<4:52:50,  5.37s/it]
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Training...:  26% 1122/4393 [1:26:16<4:50:55,  5.34s/it]
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Training...:  26% 1123/4393 [1:26:21<4:48:12,  5.29s/it]
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Training...:  26% 1124/4393 [1:26:26<4:46:11,  5.25s/it]
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Training...:  26% 1125/4393 [1:26:31<4:43:40,  5.21s/it]
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Training...:  26% 1126/4393 [1:26:36<4:42:30,  5.19s/it]
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Training...:  26% 1127/4393 [1:26:42<4:41:49,  5.18s/it]
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Training...:  26% 1128/4393 [1:26:47<4:40:51,  5.16s/it]
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Training...:  26% 1129/4393 [1:26:52<4:36:26,  5.08s/it]
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Training...:  26% 1130/4393 [1:26:56<4:31:40,  5.00s/it]
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Training...:  26% 1131/4393 [1:27:01<4:27:27,  4.92s/it]
+
                                                        

                                                            

+
Training...:  26% 1131/4393 [1:27:07<4:27:27,  4.92s/it]
Epoch ... (1/12):   8% 1/12 [7:18:06<64:20:33, 21057.59s/it]
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Training...:  26% 1132/4393 [1:27:07<4:35:17,  5.07s/it]
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Training...:  26% 1133/4393 [1:27:11<4:28:14,  4.94s/it]
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Training...:  26% 1134/4393 [1:27:16<4:21:12,  4.81s/it]
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Training...:  26% 1135/4393 [1:27:20<4:15:22,  4.70s/it]
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Training...:  26% 1136/4393 [1:27:24<4:08:22,  4.58s/it]
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Training...:  26% 1137/4393 [1:27:29<4:02:56,  4.48s/it]
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Training...:  26% 1138/4393 [1:27:33<3:55:25,  4.34s/it]
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Training...:  26% 1139/4393 [1:27:36<3:46:00,  4.17s/it]
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Training...:  26% 1140/4393 [1:27:40<3:35:26,  3.97s/it]
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Training...:  26% 1141/4393 [1:27:43<3:23:42,  3.76s/it]
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Training...:  26% 1142/4393 [1:27:46<3:11:49,  3.54s/it]
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Training...:  26% 1143/4393 [1:27:49<2:59:47,  3.32s/it]
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Training...:  26% 1144/4393 [1:27:52<2:47:24,  3.09s/it]
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Training...:  26% 1145/4393 [1:27:54<2:33:54,  2.84s/it]
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Training...:  26% 1146/4393 [1:27:56<2:21:38,  2.62s/it]
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Training...:  26% 1147/4393 [1:27:58<2:08:11,  2.37s/it]
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Training...:  26% 1148/4393 [1:27:59<1:54:58,  2.13s/it]
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Training...:  26% 1149/4393 [1:28:01<1:40:15,  1.85s/it]
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Training...:  26% 1150/4393 [1:28:02<1:25:26,  1.58s/it]
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Training...:  26% 1151/4393 [1:28:08<2:40:58,  2.98s/it]
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Training...:  26% 1152/4393 [1:28:14<3:31:51,  3.92s/it]
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Training...:  26% 1153/4393 [1:28:20<4:04:13,  4.52s/it]
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Training...:  26% 1154/4393 [1:28:26<4:25:41,  4.92s/it]
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Training...:  26% 1155/4393 [1:28:32<4:40:29,  5.20s/it]
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Training...:  26% 1156/4393 [1:28:37<4:48:51,  5.35s/it]
+
                                                        

                                                            

+
Training...:  26% 1156/4393 [1:28:44<4:48:51,  5.35s/it]
Epoch ... (1/12):   8% 1/12 [7:19:43<64:20:33, 21057.59s/it]
+
Training...:  26% 1157/4393 [1:28:44<5:08:43,  5.72s/it]
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Training...:  26% 1158/4393 [1:28:50<5:09:33,  5.74s/it]
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Training...:  26% 1159/4393 [1:28:55<5:08:36,  5.73s/it]
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Training...:  26% 1160/4393 [1:29:01<5:07:21,  5.70s/it]
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Training...:  26% 1161/4393 [1:29:07<5:09:15,  5.74s/it]
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Training...:  26% 1162/4393 [1:29:13<5:09:26,  5.75s/it]
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Training...:  26% 1163/4393 [1:29:18<5:06:52,  5.70s/it]
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Training...:  26% 1164/4393 [1:29:24<5:03:56,  5.65s/it]
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Training...:  27% 1165/4393 [1:29:29<5:01:32,  5.60s/it]
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Training...:  27% 1166/4393 [1:29:35<4:58:13,  5.54s/it]
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Training...:  27% 1175/4393 [1:30:22<4:37:50,  5.18s/it]
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Training...:  27% 1181/4393 [1:30:52<4:26:20,  4.98s/it]
+
                                                        

                                                            

+
Training...:  27% 1181/4393 [1:30:57<4:26:20,  4.98s/it]
Epoch ... (1/12):   8% 1/12 [7:21:57<64:20:33, 21057.59s/it]
+
Training...:  27% 1182/4393 [1:30:57<4:32:43,  5.10s/it]
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Training...:  27% 1183/4393 [1:31:02<4:25:15,  4.96s/it]
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Training...:  27% 1189/4393 [1:31:27<3:37:39,  4.08s/it]
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Training...:  27% 1190/4393 [1:31:30<3:26:28,  3.87s/it]
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Training...:  27% 1191/4393 [1:31:33<3:15:33,  3.66s/it]
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Training...:  27% 1192/4393 [1:31:36<3:03:28,  3.44s/it]
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Training...:  27% 1202/4393 [1:32:03<3:28:18,  3.92s/it]
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Training...:  27% 1203/4393 [1:32:09<4:02:39,  4.56s/it]
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Training...:  27% 1204/4393 [1:32:15<4:23:30,  4.96s/it]
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+
                                                        

                                                            

+
Training...:  27% 1206/4393 [1:32:33<4:46:37,  5.40s/it]
Epoch ... (1/12):   8% 1/12 [7:23:33<64:20:33, 21057.59s/it]
+
Training...:  27% 1207/4393 [1:32:33<5:07:01,  5.78s/it]
+
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Training...:  28% 1209/4393 [1:32:45<5:05:43,  5.76s/it]
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Training...:  28% 1231/4393 [1:34:47<4:19:38,  4.93s/it]
Epoch ... (1/12):   8% 1/12 [7:25:46<64:20:33, 21057.59s/it]
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Training...:  28% 1232/4393 [1:34:47<4:26:28,  5.06s/it]
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Training...:  29% 1256/4393 [1:36:23<4:42:48,  5.41s/it]
Epoch ... (1/12):   8% 1/12 [7:27:22<64:20:33, 21057.59s/it]
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Training...:  29% 1257/4393 [1:36:23<5:04:45,  5.83s/it]
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+
Training...:  29% 1281/4393 [1:38:36<4:16:04,  4.94s/it]
Epoch ... (1/12):   8% 1/12 [7:29:35<64:20:33, 21057.59s/it]
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Training...:  29% 1282/4393 [1:38:36<4:23:43,  5.09s/it]
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Training...:  30% 1296/4393 [1:39:25<2:18:13,  2.68s/it]
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Training...:  30% 1298/4393 [1:39:29<1:53:17,  2.20s/it]
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Training...:  30% 1299/4393 [1:39:30<1:39:37,  1.93s/it]
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Training...:  30% 1301/4393 [1:39:37<2:35:09,  3.01s/it]
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Training...:  30% 1302/4393 [1:39:44<3:25:05,  3.98s/it]
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Training...:  30% 1304/4393 [1:39:56<4:18:18,  5.02s/it]
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Training...:  30% 1305/4393 [1:40:02<4:32:34,  5.30s/it]
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Training...:  30% 1306/4393 [1:40:07<4:40:37,  5.45s/it]
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+
Training...:  30% 1306/4393 [1:40:14<4:40:37,  5.45s/it]
Epoch ... (1/12):   8% 1/12 [7:31:13<64:20:33, 21057.59s/it]
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Training...:  30% 1307/4393 [1:40:14<4:58:28,  5.80s/it]
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Training...:  30% 1308/4393 [1:40:20<4:57:14,  5.78s/it]
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Training...:  30% 1310/4393 [1:40:31<4:54:41,  5.74s/it]
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Training...:  30% 1321/4393 [1:41:32<4:36:44,  5.41s/it]
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Training...:  30% 1329/4393 [1:42:12<4:16:48,  5.03s/it]
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Training...:  30% 1331/4393 [1:42:22<4:10:18,  4.90s/it]
+
                                                        

                                                            

+
Training...:  30% 1331/4393 [1:42:27<4:10:18,  4.90s/it]
Epoch ... (1/12):   8% 1/12 [7:33:27<64:20:33, 21057.59s/it]
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Training...:  30% 1332/4393 [1:42:28<4:18:33,  5.07s/it]
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Training...:  30% 1333/4393 [1:42:32<4:12:34,  4.95s/it]
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Training...:  30% 1334/4393 [1:42:37<4:06:44,  4.84s/it]
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Training...:  30% 1339/4393 [1:42:58<3:36:08,  4.25s/it]
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Training...:  31% 1340/4393 [1:43:02<3:25:59,  4.05s/it]
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Training...:  31% 1341/4393 [1:43:05<3:13:40,  3.81s/it]
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Training...:  31% 1347/4393 [1:43:19<1:55:56,  2.28s/it]
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Training...:  31% 1351/4393 [1:43:29<2:26:44,  2.89s/it]
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Training...:  31% 1352/4393 [1:43:35<3:15:14,  3.85s/it]
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+
                                                        

                                                            

+
Training...:  31% 1356/4393 [1:44:05<4:30:52,  5.35s/it]
Epoch ... (1/12):   8% 1/12 [7:35:04<64:20:33, 21057.59s/it]
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Training...:  31% 1357/4393 [1:44:05<4:50:51,  5.75s/it]
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+
                                                        

                                                            

+
Training...:  31% 1381/4393 [1:46:18<4:08:11,  4.94s/it]
Epoch ... (1/12):   8% 1/12 [7:37:17<64:20:33, 21057.59s/it]
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Training...:  31% 1382/4393 [1:46:18<4:14:51,  5.08s/it]
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+
                                                        

                                                            

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Training...:  32% 1406/4393 [1:47:54<4:28:52,  5.40s/it]
Epoch ... (1/12):   8% 1/12 [7:38:53<64:20:33, 21057.59s/it]
+
Training...:  32% 1407/4393 [1:47:54<4:46:50,  5.76s/it]
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Training...:  33% 1431/4393 [1:50:06<4:02:41,  4.92s/it]
Epoch ... (1/12):   8% 1/12 [7:41:06<64:20:33, 21057.59s/it]
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Training...:  33% 1432/4393 [1:50:06<4:10:54,  5.08s/it]
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Training...:  33% 1456/4393 [1:51:45<4:25:38,  5.43s/it]
Epoch ... (1/12):   8% 1/12 [7:42:45<64:20:33, 21057.59s/it]
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Training...:  33% 1457/4393 [1:51:45<4:47:13,  5.87s/it]
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Training...:  34% 1481/4393 [1:53:58<3:55:57,  4.86s/it]
Epoch ... (1/12):   8% 1/12 [7:44:57<64:20:33, 21057.59s/it]
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Training...:  34% 1482/4393 [1:53:58<4:03:08,  5.01s/it]
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Training...:  34% 1506/4393 [1:55:34<4:18:00,  5.36s/it]
Epoch ... (1/12):   8% 1/12 [7:46:34<64:20:33, 21057.59s/it]
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Training...:  34% 1507/4393 [1:55:34<4:35:42,  5.73s/it]
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Training...:  35% 1531/4393 [1:57:48<3:55:25,  4.94s/it]
Epoch ... (1/12):   8% 1/12 [7:48:47<64:20:33, 21057.59s/it]
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Training...:  35% 1532/4393 [1:57:48<4:02:19,  5.08s/it]
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Training...:  35% 1556/4393 [1:59:25<4:17:20,  5.44s/it]
Epoch ... (1/12):   8% 1/12 [7:50:25<64:20:33, 21057.59s/it]
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Training...:  35% 1557/4393 [1:59:25<4:34:21,  5.80s/it]
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+
                                                        

                                                            

+
Training...:  36% 1581/4393 [2:01:39<3:52:56,  4.97s/it]
Epoch ... (1/12):   8% 1/12 [7:52:39<64:20:33, 21057.59s/it]
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Training...:  36% 1582/4393 [2:01:39<4:01:21,  5.15s/it]
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+
                                                        

                                                            

+
Training...:  37% 1606/4393 [2:03:18<4:16:11,  5.52s/it]
Epoch ... (1/12):   8% 1/12 [7:54:18<64:20:33, 21057.59s/it]
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Training...:  37% 1607/4393 [2:03:18<4:31:49,  5.85s/it]
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Training...:  37% 1631/4393 [2:05:32<3:46:36,  4.92s/it]
Epoch ... (1/12):   8% 1/12 [7:56:31<64:20:33, 21057.59s/it]
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Training...:  38% 1656/4393 [2:07:08<4:09:13,  5.46s/it]
Epoch ... (1/12):   8% 1/12 [7:58:07<64:20:33, 21057.59s/it]
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Training...:  38% 1657/4393 [2:07:08<4:24:31,  5.80s/it]
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Training...:  38% 1681/4393 [2:09:22<3:46:24,  5.01s/it]
Epoch ... (1/12):   8% 1/12 [8:00:22<64:20:33, 21057.59s/it]
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Training...:  38% 1682/4393 [2:09:22<3:54:28,  5.19s/it]
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Training...:  39% 1706/4393 [2:11:01<4:03:42,  5.44s/it]
Epoch ... (1/12):   8% 1/12 [8:02:01<64:20:33, 21057.59s/it]
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Training...:  39% 1707/4393 [2:11:01<4:19:57,  5.81s/it]
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Training...:  39% 1731/4393 [2:13:14<3:37:14,  4.90s/it]
Epoch ... (1/12):   8% 1/12 [8:04:14<64:20:33, 21057.59s/it]
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Training...:  39% 1732/4393 [2:13:14<3:45:00,  5.07s/it]
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Training...:  40% 1756/4393 [2:14:52<3:59:00,  5.44s/it]
Epoch ... (1/12):   8% 1/12 [8:05:51<64:20:33, 21057.59s/it]
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Training...:  40% 1757/4393 [2:14:52<4:17:22,  5.86s/it]Step... (4400 | Loss: 0.15398074686527252, Learning Rate: 9.212323493557051e-05, Gradient Norm: 0.7512454986572266)
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Training...:  40% 1758/4393 [2:14:58<4:18:34,  5.89s/it]
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Training...:  41% 1781/4393 [2:17:05<3:34:30,  4.93s/it]
Epoch ... (1/12):   8% 1/12 [8:08:05<64:20:33, 21057.59s/it]
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Training...:  41% 1806/4393 [2:18:44<3:53:28,  5.42s/it]
Epoch ... (1/12):   8% 1/12 [8:09:43<64:20:33, 21057.59s/it]
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Training...:  41% 1807/4393 [2:18:44<4:09:07,  5.78s/it]
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Training...:  42% 1831/4393 [2:20:58<3:32:33,  4.98s/it]
Epoch ... (1/12):   8% 1/12 [8:11:57<64:20:33, 21057.59s/it]
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Training...:  42% 1832/4393 [2:20:58<3:38:41,  5.12s/it]
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Training...:  42% 1856/4393 [2:22:36<3:49:34,  5.43s/it]
Epoch ... (1/12):   8% 1/12 [8:13:35<64:20:33, 21057.59s/it]
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Training...:  42% 1857/4393 [2:22:36<4:04:31,  5.79s/it]
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Training...:  43% 1881/4393 [2:24:43<3:23:17,  4.86s/it]
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Training...:  43% 1881/4393 [2:24:48<3:23:17,  4.86s/it]
Epoch ... (1/12):   8% 1/12 [8:15:48<64:20:33, 21057.59s/it]
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Training...:  43% 1882/4393 [2:24:48<3:29:28,  5.01s/it]
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Training...:  43% 1906/4393 [2:26:22<3:42:52,  5.38s/it]
Epoch ... (1/12):   8% 1/12 [8:17:22<64:20:33, 21057.59s/it]
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Training...:  43% 1907/4393 [2:26:22<3:59:34,  5.78s/it]
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Training...:  44% 1930/4393 [2:28:26<3:24:28,  4.98s/it]
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Training...:  44% 1931/4393 [2:28:31<3:21:56,  4.92s/it]
+
                                                        

                                                            

+
Training...:  44% 1931/4393 [2:28:36<3:21:56,  4.92s/it]
Epoch ... (1/12):   8% 1/12 [8:19:36<64:20:33, 21057.59s/it]
+
Training...:  44% 1932/4393 [2:28:36<3:27:39,  5.06s/it]
+
Training...:  44% 1933/4393 [2:28:41<3:21:41,  4.92s/it]
+
Training...:  44% 1934/4393 [2:28:45<3:16:35,  4.80s/it]
+
Training...:  44% 1935/4393 [2:28:50<3:11:41,  4.68s/it]
+
Training...:  44% 1936/4393 [2:28:54<3:06:10,  4.55s/it]
+
Training...:  44% 1937/4393 [2:28:58<3:00:33,  4.41s/it]
+
Training...:  44% 1938/4393 [2:29:02<2:54:09,  4.26s/it]
+
Training...:  44% 1939/4393 [2:29:06<2:47:11,  4.09s/it]
+
Training...:  44% 1940/4393 [2:29:09<2:40:22,  3.92s/it]
+
Training...:  44% 1941/4393 [2:29:13<2:32:50,  3.74s/it]
+
Training...:  44% 1942/4393 [2:29:16<2:23:26,  3.51s/it]
+
Training...:  44% 1943/4393 [2:29:18<2:13:32,  3.27s/it]
+
Training...:  44% 1944/4393 [2:29:21<2:03:30,  3.03s/it]
+
Training...:  44% 1945/4393 [2:29:23<1:53:26,  2.78s/it]
+
Training...:  44% 1946/4393 [2:29:25<1:43:40,  2.54s/it]
+
Training...:  44% 1947/4393 [2:29:27<1:33:03,  2.28s/it]
+
Training...:  44% 1948/4393 [2:29:28<1:22:15,  2.02s/it]
+
Training...:  44% 1949/4393 [2:29:29<1:11:54,  1.77s/it]
+
Training...:  44% 1950/4393 [2:29:30<1:01:48,  1.52s/it]
+
Training...:  44% 1951/4393 [2:29:36<1:57:59,  2.90s/it]
+
Training...:  44% 1952/4393 [2:29:42<2:38:36,  3.90s/it]
+
Training...:  44% 1953/4393 [2:29:48<3:04:02,  4.53s/it]
+
Training...:  44% 1954/4393 [2:29:54<3:21:45,  4.96s/it]
+
Training...:  45% 1955/4393 [2:30:00<3:33:17,  5.25s/it]
+
Training...:  45% 1956/4393 [2:30:06<3:39:45,  5.41s/it]
+
                                                        

                                                            

+
Training...:  45% 1956/4393 [2:30:13<3:39:45,  5.41s/it]
Epoch ... (1/12):   8% 1/12 [8:21:12<64:20:33, 21057.59s/it]
+
Training...:  45% 1957/4393 [2:30:13<3:55:09,  5.79s/it]
+
Training...:  45% 1958/4393 [2:30:19<3:55:35,  5.81s/it]
+
Training...:  45% 1959/4393 [2:30:25<3:57:57,  5.87s/it]
+
Training...:  45% 1960/4393 [2:30:31<3:57:33,  5.86s/it]
+
Training...:  45% 1961/4393 [2:30:36<3:55:27,  5.81s/it]
+
Training...:  45% 1962/4393 [2:30:42<3:52:26,  5.74s/it]
+
Training...:  45% 1963/4393 [2:30:47<3:50:58,  5.70s/it]
+
Training...:  45% 1964/4393 [2:30:53<3:48:25,  5.64s/it]
+
Training...:  45% 1965/4393 [2:30:58<3:46:51,  5.61s/it]
+
Training...:  45% 1966/4393 [2:31:04<3:45:09,  5.57s/it]
+
Training...:  45% 1967/4393 [2:31:09<3:43:48,  5.54s/it]
+
Training...:  45% 1968/4393 [2:31:15<3:42:14,  5.50s/it]
+
Training...:  45% 1969/4393 [2:31:20<3:40:45,  5.46s/it]
+
Training...:  45% 1970/4393 [2:31:26<3:39:24,  5.43s/it]
+
Training...:  45% 1971/4393 [2:31:31<3:37:46,  5.39s/it]
+
Training...:  45% 1972/4393 [2:31:36<3:35:48,  5.35s/it]
+
Training...:  45% 1973/4393 [2:31:41<3:34:37,  5.32s/it]
+
Training...:  45% 1974/4393 [2:31:47<3:33:08,  5.29s/it]
+
Training...:  45% 1975/4393 [2:31:52<3:31:34,  5.25s/it]
+
Training...:  45% 1976/4393 [2:31:57<3:31:59,  5.26s/it]
+
Training...:  45% 1977/4393 [2:32:02<3:30:19,  5.22s/it]
+
Training...:  45% 1978/4393 [2:32:07<3:27:18,  5.15s/it]
+
Training...:  45% 1979/4393 [2:32:12<3:25:18,  5.10s/it]
+
Training...:  45% 1980/4393 [2:32:17<3:22:26,  5.03s/it]
+
Training...:  45% 1981/4393 [2:32:22<3:20:47,  4.99s/it]
+
                                                        

                                                            

+
Training...:  45% 1981/4393 [2:32:27<3:20:47,  4.99s/it]
Epoch ... (1/12):   8% 1/12 [8:23:27<64:20:33, 21057.59s/it]
+
Training...:  45% 1982/4393 [2:32:27<3:26:54,  5.15s/it]
+
Training...:  45% 1983/4393 [2:32:32<3:21:51,  5.03s/it]
+
Training...:  45% 1984/4393 [2:32:37<3:17:17,  4.91s/it]
+
Training...:  45% 1985/4393 [2:32:41<3:13:00,  4.81s/it]
+
Training...:  45% 1986/4393 [2:32:46<3:08:25,  4.70s/it]
+
Training...:  45% 1987/4393 [2:32:50<3:03:46,  4.58s/it]
+
Training...:  45% 1988/4393 [2:32:54<2:57:34,  4.43s/it]
+
Training...:  45% 1989/4393 [2:32:58<2:51:45,  4.29s/it]
+
Training...:  45% 1990/4393 [2:33:02<2:44:11,  4.10s/it]
+
Training...:  45% 1991/4393 [2:33:05<2:36:12,  3.90s/it]
+
Training...:  45% 1992/4393 [2:33:08<2:27:31,  3.69s/it]
+
Training...:  45% 1993/4393 [2:33:11<2:18:15,  3.46s/it]
+
Training...:  45% 1994/4393 [2:33:14<2:09:01,  3.23s/it]
+
Training...:  45% 1995/4393 [2:33:16<1:58:14,  2.96s/it]
+
Training...:  45% 1996/4393 [2:33:18<1:47:06,  2.68s/it]
+
Training...:  45% 1997/4393 [2:33:20<1:35:45,  2.40s/it]
+
Training...:  45% 1998/4393 [2:33:22<1:24:06,  2.11s/it]
+
Training...:  46% 1999/4393 [2:33:23<1:13:09,  1.83s/it]
+
Training...:  46% 2000/4393 [2:33:24<1:02:28,  1.57s/it]
+
Training...:  46% 2001/4393 [2:33:30<2:00:15,  3.02s/it]
+
Training...:  46% 2002/4393 [2:33:36<2:38:41,  3.98s/it]
+
Training...:  46% 2003/4393 [2:33:42<3:02:19,  4.58s/it]
+
Training...:  46% 2004/4393 [2:33:48<3:17:49,  4.97s/it]
+
Training...:  46% 2005/4393 [2:33:54<3:28:01,  5.23s/it]
+
Training...:  46% 2006/4393 [2:34:00<3:34:51,  5.40s/it]
+
                                                        

                                                            

+
Training...:  46% 2006/4393 [2:34:06<3:34:51,  5.40s/it]
Epoch ... (1/12):   8% 1/12 [8:25:06<64:20:33, 21057.59s/it]
+
Training...:  46% 2007/4393 [2:34:06<3:49:26,  5.77s/it]
+
Training...:  46% 2008/4393 [2:34:12<3:49:31,  5.77s/it]
+
Training...:  46% 2009/4393 [2:34:18<3:50:01,  5.79s/it]
+
Training...:  46% 2010/4393 [2:34:24<3:48:27,  5.75s/it]
+
Training...:  46% 2011/4393 [2:34:29<3:47:10,  5.72s/it]
+
Training...:  46% 2012/4393 [2:34:35<3:45:31,  5.68s/it]
+
Training...:  46% 2013/4393 [2:34:41<3:44:28,  5.66s/it]
+
Training...:  46% 2014/4393 [2:34:46<3:42:40,  5.62s/it]
+
Training...:  46% 2015/4393 [2:34:52<3:42:02,  5.60s/it]
+
Training...:  46% 2016/4393 [2:34:57<3:42:59,  5.63s/it]
+
Training...:  46% 2017/4393 [2:35:03<3:42:26,  5.62s/it]
+
Training...:  46% 2018/4393 [2:35:08<3:39:32,  5.55s/it]
+
Training...:  46% 2019/4393 [2:35:14<3:36:48,  5.48s/it]
+
Training...:  46% 2020/4393 [2:35:19<3:34:17,  5.42s/it]
+
Training...:  46% 2021/4393 [2:35:24<3:33:12,  5.39s/it]
+
Training...:  46% 2022/4393 [2:35:29<3:30:16,  5.32s/it]
+
Training...:  46% 2023/4393 [2:35:35<3:28:44,  5.28s/it]
+
Training...:  46% 2024/4393 [2:35:40<3:26:27,  5.23s/it]
+
Training...:  46% 2025/4393 [2:35:45<3:25:38,  5.21s/it]
+
Training...:  46% 2026/4393 [2:35:50<3:23:32,  5.16s/it]
+
Training...:  46% 2027/4393 [2:35:55<3:21:28,  5.11s/it]
+
Training...:  46% 2028/4393 [2:36:00<3:19:57,  5.07s/it]
+
Training...:  46% 2029/4393 [2:36:05<3:17:42,  5.02s/it]
+
Training...:  46% 2030/4393 [2:36:10<3:14:56,  4.95s/it]
+
Training...:  46% 2031/4393 [2:36:14<3:13:15,  4.91s/it]
+
                                                        

                                                            

+
Training...:  46% 2031/4393 [2:36:20<3:13:15,  4.91s/it]
Epoch ... (1/12):   8% 1/12 [8:27:19<64:20:33, 21057.59s/it]
+
Training...:  46% 2032/4393 [2:36:20<3:19:37,  5.07s/it]
+
Training...:  46% 2033/4393 [2:36:24<3:14:29,  4.94s/it]
+
Training...:  46% 2034/4393 [2:36:29<3:10:24,  4.84s/it]
+
Training...:  46% 2035/4393 [2:36:34<3:06:45,  4.75s/it]
+
Training...:  46% 2036/4393 [2:36:38<3:00:04,  4.58s/it]
+
Training...:  46% 2037/4393 [2:36:42<2:52:55,  4.40s/it]
+
Training...:  46% 2038/4393 [2:36:46<2:45:56,  4.23s/it]
+
Training...:  46% 2039/4393 [2:36:49<2:38:57,  4.05s/it]
+
Training...:  46% 2040/4393 [2:36:53<2:30:39,  3.84s/it]
+
Training...:  46% 2041/4393 [2:36:56<2:22:08,  3.63s/it]
+
Training...:  46% 2042/4393 [2:36:59<2:13:21,  3.40s/it]
+
Training...:  47% 2043/4393 [2:37:01<2:04:06,  3.17s/it]
+
Training...:  47% 2044/4393 [2:37:04<1:53:58,  2.91s/it]
+
Training...:  47% 2045/4393 [2:37:06<1:45:01,  2.68s/it]
+
Training...:  47% 2046/4393 [2:37:08<1:35:24,  2.44s/it]
+
Training...:  47% 2047/4393 [2:37:09<1:26:02,  2.20s/it]
+
Training...:  47% 2048/4393 [2:37:11<1:16:45,  1.96s/it]
+
Training...:  47% 2049/4393 [2:37:12<1:07:27,  1.73s/it]
+
Training...:  47% 2050/4393 [2:37:13<58:00,  1.49s/it]  
+
Training...:  47% 2051/4393 [2:37:19<1:51:47,  2.86s/it]
+
Training...:  47% 2052/4393 [2:37:25<2:29:35,  3.83s/it]
+
Training...:  47% 2053/4393 [2:37:31<2:53:49,  4.46s/it]
+
Training...:  47% 2054/4393 [2:37:37<3:10:00,  4.87s/it]
+
Training...:  47% 2055/4393 [2:37:43<3:22:24,  5.19s/it]
+
Training...:  47% 2056/4393 [2:37:48<3:30:24,  5.40s/it]
+
                                                        

                                                            

+
Training...:  47% 2056/4393 [2:37:55<3:30:24,  5.40s/it]
Epoch ... (1/12):   8% 1/12 [8:28:55<64:20:33, 21057.59s/it]
+
Training...:  47% 2057/4393 [2:37:55<3:45:27,  5.79s/it]
+
Training...:  47% 2058/4393 [2:38:01<3:45:27,  5.79s/it]
+
Training...:  47% 2059/4393 [2:38:07<3:44:35,  5.77s/it]
+
Training...:  47% 2060/4393 [2:38:13<3:45:16,  5.79s/it]
+
Training...:  47% 2061/4393 [2:38:18<3:44:50,  5.79s/it]
+
Training...:  47% 2062/4393 [2:38:24<3:41:30,  5.70s/it]
+
Training...:  47% 2063/4393 [2:38:29<3:40:24,  5.68s/it]
+
Training...:  47% 2064/4393 [2:38:35<3:38:18,  5.62s/it]
+
Training...:  47% 2065/4393 [2:38:40<3:37:04,  5.59s/it]
+
Training...:  47% 2066/4393 [2:38:46<3:35:00,  5.54s/it]
+
Training...:  47% 2067/4393 [2:38:51<3:34:23,  5.53s/it]
+
Training...:  47% 2068/4393 [2:38:57<3:32:18,  5.48s/it]
+
Training...:  47% 2069/4393 [2:39:02<3:31:39,  5.46s/it]
+
Training...:  47% 2070/4393 [2:39:08<3:30:08,  5.43s/it]
+
Training...:  47% 2071/4393 [2:39:13<3:29:18,  5.41s/it]
+
Training...:  47% 2072/4393 [2:39:18<3:26:50,  5.35s/it]
+
Training...:  47% 2073/4393 [2:39:23<3:25:13,  5.31s/it]
+
Training...:  47% 2074/4393 [2:39:28<3:23:34,  5.27s/it]
+
Training...:  47% 2075/4393 [2:39:34<3:21:58,  5.23s/it]
+
Training...:  47% 2076/4393 [2:39:39<3:22:24,  5.24s/it]
+
Training...:  47% 2077/4393 [2:39:44<3:21:50,  5.23s/it]
+
Training...:  47% 2078/4393 [2:39:49<3:18:44,  5.15s/it]
+
Training...:  47% 2079/4393 [2:39:54<3:16:29,  5.09s/it]
+
Training...:  47% 2080/4393 [2:39:59<3:13:39,  5.02s/it]
+
Training...:  47% 2081/4393 [2:40:04<3:10:58,  4.96s/it]
+
                                                        

                                                            

+
Training...:  47% 2081/4393 [2:40:09<3:10:58,  4.96s/it]
Epoch ... (1/12):   8% 1/12 [8:31:09<64:20:33, 21057.59s/it]
+
Training...:  47% 2082/4393 [2:40:09<3:16:30,  5.10s/it]
+
Training...:  47% 2083/4393 [2:40:14<3:12:25,  5.00s/it]
+
Training...:  47% 2084/4393 [2:40:18<3:08:00,  4.89s/it]
+
Training...:  47% 2085/4393 [2:40:23<3:03:50,  4.78s/it]
+
Training...:  47% 2086/4393 [2:40:27<2:58:34,  4.64s/it]
+
Training...:  48% 2087/4393 [2:40:32<2:53:24,  4.51s/it]
+
Training...:  48% 2088/4393 [2:40:36<2:48:11,  4.38s/it]
+
Training...:  48% 2089/4393 [2:40:40<2:42:29,  4.23s/it]
+
Training...:  48% 2090/4393 [2:40:43<2:35:43,  4.06s/it]
+
Training...:  48% 2091/4393 [2:40:47<2:28:34,  3.87s/it]
+
Training...:  48% 2092/4393 [2:40:50<2:20:51,  3.67s/it]
+
Training...:  48% 2093/4393 [2:40:53<2:12:05,  3.45s/it]
+
Training...:  48% 2094/4393 [2:40:55<2:02:29,  3.20s/it]
+
Training...:  48% 2095/4393 [2:40:58<1:52:28,  2.94s/it]
+
Training...:  48% 2096/4393 [2:41:00<1:42:28,  2.68s/it]
+
Training...:  48% 2097/4393 [2:41:02<1:32:06,  2.41s/it]
+
Training...:  48% 2098/4393 [2:41:03<1:22:06,  2.15s/it]
+
Training...:  48% 2099/4393 [2:41:04<1:11:44,  1.88s/it]
+
Training...:  48% 2100/4393 [2:41:05<1:00:57,  1.60s/it]
+
Training...:  48% 2101/4393 [2:41:11<1:54:02,  2.99s/it]
+
Training...:  48% 2102/4393 [2:41:18<2:31:16,  3.96s/it]
+
Training...:  48% 2103/4393 [2:41:24<2:54:06,  4.56s/it]
+
Training...:  48% 2104/4393 [2:41:30<3:09:41,  4.97s/it]
+
Training...:  48% 2105/4393 [2:41:35<3:19:38,  5.24s/it]
+
Training...:  48% 2106/4393 [2:41:41<3:26:16,  5.41s/it]
+
                                                        

                                                            

+
Training...:  48% 2106/4393 [2:41:48<3:26:16,  5.41s/it]
Epoch ... (1/12):   8% 1/12 [8:32:47<64:20:33, 21057.59s/it]
+
Training...:  48% 2107/4393 [2:41:48<3:40:54,  5.80s/it]
+
Training...:  48% 2108/4393 [2:41:54<3:41:11,  5.81s/it]
+
Training...:  48% 2109/4393 [2:42:00<3:40:31,  5.79s/it]
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Training...:  48% 2110/4393 [2:42:05<3:38:52,  5.75s/it]
+
Training...:  48% 2111/4393 [2:42:11<3:37:21,  5.71s/it]
+
Training...:  48% 2112/4393 [2:42:16<3:36:01,  5.68s/it]
+
Training...:  48% 2113/4393 [2:42:22<3:37:20,  5.72s/it]
+
Training...:  48% 2114/4393 [2:42:28<3:36:51,  5.71s/it]
+
Training...:  48% 2115/4393 [2:42:33<3:34:11,  5.64s/it]
+
Training...:  48% 2116/4393 [2:42:39<3:32:01,  5.59s/it]
+
Training...:  48% 2117/4393 [2:42:44<3:30:23,  5.55s/it]
+
Training...:  48% 2118/4393 [2:42:50<3:27:59,  5.49s/it]
+
Training...:  48% 2119/4393 [2:42:55<3:26:41,  5.45s/it]
+
Training...:  48% 2120/4393 [2:43:00<3:24:46,  5.41s/it]
+
Training...:  48% 2121/4393 [2:43:06<3:23:37,  5.38s/it]
+
Training...:  48% 2122/4393 [2:43:11<3:22:18,  5.35s/it]
+
Training...:  48% 2123/4393 [2:43:16<3:20:14,  5.29s/it]
+
Training...:  48% 2124/4393 [2:43:21<3:18:17,  5.24s/it]
+
Training...:  48% 2125/4393 [2:43:26<3:17:00,  5.21s/it]
+
Training...:  48% 2126/4393 [2:43:31<3:15:41,  5.18s/it]
+
Training...:  48% 2127/4393 [2:43:36<3:13:42,  5.13s/it]
+
Training...:  48% 2128/4393 [2:43:41<3:11:25,  5.07s/it]
+
Training...:  48% 2129/4393 [2:43:46<3:09:58,  5.03s/it]
+
Training...:  48% 2130/4393 [2:43:51<3:07:13,  4.96s/it]
+
Training...:  49% 2131/4393 [2:43:56<3:06:42,  4.95s/it]
+
                                                        

                                                            

+
Training...:  49% 2131/4393 [2:44:02<3:06:42,  4.95s/it]
Epoch ... (1/12):   8% 1/12 [8:35:01<64:20:33, 21057.59s/it]
+
Training...:  49% 2132/4393 [2:44:02<3:12:26,  5.11s/it]
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Epoch ... (1/12):   8% 1/12 [8:36:38<64:20:33, 21057.59s/it]
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Training...:  50% 2181/4393 [2:47:53<3:03:09,  4.97s/it]
Epoch ... (1/12):   8% 1/12 [8:38:53<64:20:33, 21057.59s/it]
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Training...:  50% 2206/4393 [2:49:32<3:17:11,  5.41s/it]
Epoch ... (1/12):   8% 1/12 [8:40:31<64:20:33, 21057.59s/it]
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Training...:  50% 2207/4393 [2:49:32<3:30:56,  5.79s/it]
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Training...:  51% 2231/4393 [2:51:47<2:58:53,  4.96s/it]
Epoch ... (1/12):   8% 1/12 [8:42:46<64:20:33, 21057.59s/it]
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Training...:  51% 2232/4393 [2:51:47<3:03:51,  5.10s/it]
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Training...:  51% 2256/4393 [2:53:24<3:14:30,  5.46s/it]
Epoch ... (1/12):   8% 1/12 [8:44:24<64:20:33, 21057.59s/it]
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Training...:  51% 2257/4393 [2:53:24<3:26:57,  5.81s/it]
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Training...:  52% 2281/4393 [2:55:37<2:51:59,  4.89s/it]
Epoch ... (1/12):   8% 1/12 [8:46:37<64:20:33, 21057.59s/it]
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Training...:  52% 2282/4393 [2:55:37<2:57:53,  5.06s/it]
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Training...:  52% 2306/4393 [2:57:13<3:07:42,  5.40s/it]
Epoch ... (1/12):   8% 1/12 [8:48:13<64:20:33, 21057.59s/it]
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Training...:  53% 2331/4393 [2:59:28<2:51:06,  4.98s/it]
Epoch ... (1/12):   8% 1/12 [8:50:27<64:20:33, 21057.59s/it]
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Training...:  53% 2332/4393 [2:59:28<2:56:01,  5.12s/it]
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Epoch ... (1/12):   8% 1/12 [8:52:06<64:20:33, 21057.59s/it]
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Training...:  54% 2381/4393 [3:03:21<2:46:08,  4.95s/it]
Epoch ... (1/12):   8% 1/12 [8:54:20<64:20:33, 21057.59s/it]
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Training...:  55% 2406/4393 [3:05:00<3:01:14,  5.47s/it]
Epoch ... (1/12):   8% 1/12 [8:56:00<64:20:33, 21057.59s/it]
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Training...:  55% 2431/4393 [3:07:14<2:42:18,  4.96s/it]
Epoch ... (1/12):   8% 1/12 [8:58:14<64:20:33, 21057.59s/it]
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Training...:  55% 2432/4393 [3:07:14<2:47:50,  5.14s/it]
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Training...:  56% 2456/4393 [3:08:56<2:57:06,  5.49s/it]
Epoch ... (1/12):   8% 1/12 [8:59:55<64:20:33, 21057.59s/it]
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Training...:  56% 2457/4393 [3:08:56<3:10:43,  5.91s/it]
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Training...:  56% 2481/4393 [3:11:10<2:36:37,  4.91s/it]
Epoch ... (1/12):   8% 1/12 [9:02:09<64:20:33, 21057.59s/it]
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Epoch ... (1/12):   8% 1/12 [9:03:46<64:20:33, 21057.59s/it]
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Training...:  58% 2531/4393 [3:15:01<2:33:37,  4.95s/it]
Epoch ... (1/12):   8% 1/12 [9:06:00<64:20:33, 21057.59s/it]
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Training...:  58% 2556/4393 [3:16:37<2:45:24,  5.40s/it]
Epoch ... (1/12):   8% 1/12 [9:07:36<64:20:33, 21057.59s/it]
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Training...:  58% 2557/4393 [3:16:37<2:56:23,  5.76s/it]
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Training...:  59% 2581/4393 [3:18:51<2:32:02,  5.03s/it]
Epoch ... (1/12):   8% 1/12 [9:09:51<64:20:33, 21057.59s/it]
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Training...:  59% 2606/4393 [3:20:35<2:52:03,  5.78s/it]
Epoch ... (1/12):   8% 1/12 [9:11:35<64:20:33, 21057.59s/it]
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Training...:  59% 2607/4393 [3:20:35<3:04:58,  6.21s/it]
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Training...:  60% 2631/4393 [3:22:50<2:27:30,  5.02s/it]
Epoch ... (1/12):   8% 1/12 [9:13:49<64:20:33, 21057.59s/it]
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Training...:  60% 2632/4393 [3:22:50<2:31:23,  5.16s/it]
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Training...:  60% 2656/4393 [3:24:27<2:35:25,  5.37s/it]
Epoch ... (1/12):   8% 1/12 [9:15:26<64:20:33, 21057.59s/it]
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Training...:  60% 2657/4393 [3:24:27<2:46:16,  5.75s/it]
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Training...:  61% 2681/4393 [3:26:41<2:20:28,  4.92s/it]
Epoch ... (1/12):   8% 1/12 [9:17:40<64:20:33, 21057.59s/it]
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Training...:  61% 2682/4393 [3:26:41<2:24:42,  5.07s/it]
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Training...:  62% 2706/4393 [3:28:18<2:31:32,  5.39s/it]
Epoch ... (1/12):   8% 1/12 [9:19:18<64:20:33, 21057.59s/it]
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Training...:  62% 2707/4393 [3:28:18<2:41:46,  5.76s/it]
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+
                                                        

                                                            

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Training...:  62% 2731/4393 [3:30:32<2:16:21,  4.92s/it]
Epoch ... (1/12):   8% 1/12 [9:21:31<64:20:33, 21057.59s/it]
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Training...:  62% 2732/4393 [3:30:32<2:21:07,  5.10s/it]
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Training...:  63% 2751/4393 [3:31:32<1:19:11,  2.89s/it]
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Training...:  63% 2756/4393 [3:32:08<2:26:58,  5.39s/it]
Epoch ... (1/12):   8% 1/12 [9:23:07<64:20:33, 21057.59s/it]
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Training...:  63% 2757/4393 [3:32:08<2:36:54,  5.75s/it]
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Training...:  63% 2781/4393 [3:34:23<2:14:55,  5.02s/it]
Epoch ... (1/12):   8% 1/12 [9:25:22<64:20:33, 21057.59s/it]
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Training...:  63% 2782/4393 [3:34:23<2:20:14,  5.22s/it]
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Training...:  64% 2798/4393 [3:35:16<55:30,  2.09s/it]  
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Training...:  64% 2801/4393 [3:35:25<1:17:50,  2.93s/it]
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Training...:  64% 2802/4393 [3:35:31<1:42:58,  3.88s/it]
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Training...:  64% 2805/4393 [3:35:49<2:19:49,  5.28s/it]
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Training...:  64% 2806/4393 [3:35:54<2:23:33,  5.43s/it]
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Training...:  64% 2806/4393 [3:36:01<2:23:33,  5.43s/it]
Epoch ... (1/12):   8% 1/12 [9:27:00<64:20:33, 21057.59s/it]
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Training...:  64% 2807/4393 [3:36:01<2:32:48,  5.78s/it]
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Training...:  64% 2808/4393 [3:36:07<2:32:34,  5.78s/it]
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Training...:  64% 2830/4393 [3:38:05<2:09:40,  4.98s/it]
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Training...:  64% 2831/4393 [3:38:09<2:07:58,  4.92s/it]
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Training...:  64% 2831/4393 [3:38:15<2:07:58,  4.92s/it]
Epoch ... (1/12):   8% 1/12 [9:29:14<64:20:33, 21057.59s/it]
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Training...:  64% 2832/4393 [3:38:15<2:11:52,  5.07s/it]
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Training...:  65% 2856/4393 [3:39:45<2:17:43,  5.38s/it]
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Training...:  65% 2856/4393 [3:39:52<2:17:43,  5.38s/it]
Epoch ... (1/12):   8% 1/12 [9:30:51<64:20:33, 21057.59s/it]
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Training...:  65% 2857/4393 [3:39:52<2:28:07,  5.79s/it]
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Training...:  66% 2881/4393 [3:42:01<2:04:53,  4.96s/it]
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Training...:  66% 2881/4393 [3:42:06<2:04:53,  4.96s/it]
Epoch ... (1/12):   8% 1/12 [9:33:06<64:20:33, 21057.59s/it]
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Training...:  66% 2882/4393 [3:42:06<2:09:03,  5.12s/it]
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Training...:  66% 2906/4393 [3:43:39<2:14:17,  5.42s/it]
+
                                                        

                                                            

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Training...:  66% 2906/4393 [3:43:46<2:14:17,  5.42s/it]
Epoch ... (1/12):   8% 1/12 [9:34:45<64:20:33, 21057.59s/it]
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Training...:  66% 2907/4393 [3:43:46<2:24:14,  5.82s/it]
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+
                                                        

                                                            

+
Training...:  67% 2931/4393 [3:46:00<2:00:25,  4.94s/it]
Epoch ... (1/12):   8% 1/12 [9:36:59<64:20:33, 21057.59s/it]
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Training...:  67% 2932/4393 [3:46:00<2:04:03,  5.09s/it]
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Training...:  67% 2956/4393 [3:47:37<2:08:11,  5.35s/it]
Epoch ... (1/12):   8% 1/12 [9:38:36<64:20:33, 21057.59s/it]
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Training...:  67% 2957/4393 [3:47:37<2:17:47,  5.76s/it]
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Training...:  68% 2981/4393 [3:49:51<1:55:57,  4.93s/it]
Epoch ... (1/12):   8% 1/12 [9:40:50<64:20:33, 21057.59s/it]
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Training...:  68% 2982/4393 [3:49:51<1:59:40,  5.09s/it]
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Training...:  68% 3006/4393 [3:51:27<2:05:38,  5.44s/it]
Epoch ... (1/12):   8% 1/12 [9:42:27<64:20:33, 21057.59s/it]
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Training...:  68% 3007/4393 [3:51:27<2:13:59,  5.80s/it]
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Training...:  69% 3031/4393 [3:53:42<1:53:00,  4.98s/it]
Epoch ... (1/12):   8% 1/12 [9:44:41<64:20:33, 21057.59s/it]
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Training...:  69% 3032/4393 [3:53:42<1:56:41,  5.14s/it]
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Training...:  70% 3056/4393 [3:55:21<2:00:21,  5.40s/it]
Epoch ... (1/12):   8% 1/12 [9:46:20<64:20:33, 21057.59s/it]
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Training...:  70% 3057/4393 [3:55:21<2:08:44,  5.78s/it]
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Training...:  70% 3081/4393 [3:57:36<1:49:16,  5.00s/it]
Epoch ... (1/12):   8% 1/12 [9:48:35<64:20:33, 21057.59s/it]
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Training...:  70% 3082/4393 [3:57:36<1:52:46,  5.16s/it]
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Epoch ... (1/12):   8% 1/12 [9:50:14<64:20:33, 21057.59s/it]
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+
                                                        

                                                            

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Training...:  71% 3131/4393 [4:01:29<1:44:53,  4.99s/it]
Epoch ... (1/12):   8% 1/12 [9:52:28<64:20:33, 21057.59s/it]
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Training...:  71% 3132/4393 [4:01:29<1:47:53,  5.13s/it]
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Training...:  72% 3156/4393 [4:03:07<1:51:36,  5.41s/it]
Epoch ... (1/12):   8% 1/12 [9:54:06<64:20:33, 21057.59s/it]
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Epoch ... (1/12):   8% 1/12 [9:56:20<64:20:33, 21057.59s/it]
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Training...:  73% 3206/4393 [4:06:57<1:46:18,  5.37s/it]
Epoch ... (1/12):   8% 1/12 [9:57:57<64:20:33, 21057.59s/it]
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Training...:  74% 3231/4393 [4:09:11<1:36:02,  4.96s/it]
Epoch ... (1/12):   8% 1/12 [10:00:11<64:20:33, 21057.59s/it]
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Training...:  74% 3232/4393 [4:09:11<1:38:34,  5.09s/it]
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Training...:  74% 3256/4393 [4:10:49<1:43:43,  5.47s/it]
Epoch ... (1/12):   8% 1/12 [10:01:49<64:20:33, 21057.59s/it]
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Training...:  75% 3281/4393 [4:13:03<1:32:16,  4.98s/it]
Epoch ... (1/12):   8% 1/12 [10:04:03<64:20:33, 21057.59s/it]
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Training...:  75% 3306/4393 [4:14:41<1:37:40,  5.39s/it]
Epoch ... (1/12):   8% 1/12 [10:05:41<64:20:33, 21057.59s/it]
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Training...:  76% 3331/4393 [4:16:56<1:27:14,  4.93s/it]
Epoch ... (1/12):   8% 1/12 [10:07:55<64:20:33, 21057.59s/it]
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Epoch ... (1/12):   8% 1/12 [10:09:31<64:20:33, 21057.59s/it]
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Epoch ... (1/12):   8% 1/12 [10:11:45<64:20:33, 21057.59s/it]
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Training...:  78% 3406/4393 [4:22:23<1:29:16,  5.43s/it]
Epoch ... (1/12):   8% 1/12 [10:13:23<64:20:33, 21057.59s/it]
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Training...:  78% 3431/4393 [4:24:37<1:19:20,  4.95s/it]
Epoch ... (1/12):   8% 1/12 [10:15:37<64:20:33, 21057.59s/it]
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Training...:  79% 3456/4393 [4:26:15<1:23:48,  5.37s/it]
Epoch ... (1/12):   8% 1/12 [10:17:14<64:20:33, 21057.59s/it]
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Training...:  79% 3481/4393 [4:28:29<1:15:27,  4.96s/it]
Epoch ... (1/12):   8% 1/12 [10:19:29<64:20:33, 21057.59s/it]
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Training...:  80% 3506/4393 [4:30:08<1:19:46,  5.40s/it]
Epoch ... (1/12):   8% 1/12 [10:21:07<64:20:33, 21057.59s/it]
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Training...:  80% 3507/4393 [4:30:08<1:25:21,  5.78s/it]Step... (6150 | Loss: 0.10419484227895737, Learning Rate: 8.858787623466924e-05, Gradient Norm: 0.6518651843070984)
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Training...:  83% 3631/4393 [4:40:04<1:03:28,  5.00s/it]
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Training...:  84% 3681/4393 [4:43:56<58:55,  4.97s/it]
Epoch ... (1/12):   8% 1/12 [10:34:56<64:20:33, 21057.59s/it]
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Training...:  84% 3682/4393 [4:43:56<1:01:02,  5.15s/it]
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Training...:  84% 3706/4393 [4:45:33<1:02:06,  5.42s/it]
Epoch ... (1/12):   8% 1/12 [10:36:32<64:20:33, 21057.59s/it]
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Training...:  84% 3707/4393 [4:45:33<1:06:16,  5.80s/it]
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Training...:  85% 3731/4393 [4:47:47<54:13,  4.92s/it]
Epoch ... (1/12):   8% 1/12 [10:38:47<64:20:33, 21057.59s/it]
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Training...:  85% 3732/4393 [4:47:47<55:48,  5.07s/it]
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+
Training...:  85% 3756/4393 [4:49:23<57:02,  5.37s/it]
Epoch ... (1/12):   8% 1/12 [10:40:23<64:20:33, 21057.59s/it]
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Training...:  86% 3757/4393 [4:49:23<1:01:08,  5.77s/it]
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Training...:  86% 3781/4393 [4:51:32<50:29,  4.95s/it]
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Training...:  86% 3781/4393 [4:51:38<50:29,  4.95s/it]
Epoch ... (1/12):   8% 1/12 [10:42:37<64:20:33, 21057.59s/it]
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Training...:  86% 3782/4393 [4:51:38<51:56,  5.10s/it]
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Training...:  86% 3783/4393 [4:51:42<50:29,  4.97s/it]
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Training...:  87% 3806/4393 [4:53:09<52:52,  5.40s/it]
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Training...:  87% 3806/4393 [4:53:15<52:52,  5.40s/it]
Epoch ... (1/12):   8% 1/12 [10:44:15<64:20:33, 21057.59s/it]
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Training...:  87% 3807/4393 [4:53:15<56:21,  5.77s/it]
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Training...:  87% 3808/4393 [4:53:21<56:34,  5.80s/it]
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Training...:  87% 3831/4393 [4:55:24<46:54,  5.01s/it]
+
                                                      

                                                             

+
Training...:  87% 3831/4393 [4:55:30<46:54,  5.01s/it]
Epoch ... (1/12):   8% 1/12 [10:46:29<64:20:33, 21057.59s/it]
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Training...:  87% 3832/4393 [4:55:30<48:11,  5.15s/it]
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Training...:  88% 3856/4393 [4:57:01<48:52,  5.46s/it]
+
                                                      

                                                             

+
Training...:  88% 3856/4393 [4:57:08<48:52,  5.46s/it]
Epoch ... (1/12):   8% 1/12 [10:48:07<64:20:33, 21057.59s/it]
+
Training...:  88% 3857/4393 [4:57:08<52:14,  5.85s/it]
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Training...:  88% 3873/4393 [4:58:37<46:26,  5.36s/it]
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Training...:  88% 3874/4393 [4:58:42<45:51,  5.30s/it]
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Training...:  88% 3875/4393 [4:58:47<45:16,  5.24s/it]
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Training...:  88% 3878/4393 [4:59:02<44:04,  5.14s/it]
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Training...:  88% 3879/4393 [4:59:07<43:36,  5.09s/it]
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Training...:  88% 3880/4393 [4:59:12<42:59,  5.03s/it]
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Training...:  88% 3881/4393 [4:59:17<42:20,  4.96s/it]
+
                                                      

                                                             

+
Training...:  88% 3881/4393 [4:59:22<42:20,  4.96s/it]
Epoch ... (1/12):   8% 1/12 [10:50:22<64:20:33, 21057.59s/it]
+
Training...:  88% 3882/4393 [4:59:22<43:21,  5.09s/it]
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Training...:  88% 3883/4393 [4:59:27<42:04,  4.95s/it]
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Training...:  89% 3891/4393 [4:59:59<31:15,  3.74s/it]
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Training...:  89% 3896/4393 [5:00:11<21:26,  2.59s/it]
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Training...:  89% 3897/4393 [5:00:13<19:30,  2.36s/it]
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Training...:  89% 3900/4393 [5:00:17<12:57,  1.58s/it]
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Training...:  89% 3902/4393 [5:00:29<31:41,  3.87s/it]
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Training...:  89% 3903/4393 [5:00:35<36:59,  4.53s/it]
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Training...:  89% 3906/4393 [5:00:53<43:57,  5.42s/it]
+
                                                      

                                                             

+
Training...:  89% 3906/4393 [5:00:59<43:57,  5.42s/it]
Epoch ... (1/12):   8% 1/12 [10:51:59<64:20:33, 21057.59s/it]
+
Training...:  89% 3907/4393 [5:00:59<46:44,  5.77s/it]
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Training...:  89% 3908/4393 [5:01:05<46:40,  5.77s/it]
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Training...:  89% 3920/4393 [5:02:13<43:12,  5.48s/it]
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Training...:  89% 3925/4393 [5:02:39<41:30,  5.32s/it]
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Training...:  89% 3926/4393 [5:02:45<41:11,  5.29s/it]
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Training...:  89% 3927/4393 [5:02:50<40:46,  5.25s/it]
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Training...:  89% 3928/4393 [5:02:55<40:11,  5.19s/it]
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Training...:  89% 3929/4393 [5:03:00<39:50,  5.15s/it]
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Training...:  89% 3931/4393 [5:03:10<38:35,  5.01s/it]
+
                                                      

                                                             

+
Training...:  89% 3931/4393 [5:03:15<38:35,  5.01s/it]
Epoch ... (1/12):   8% 1/12 [10:54:15<64:20:33, 21057.59s/it]
+
Training...:  90% 3932/4393 [5:03:15<39:37,  5.16s/it]
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Training...:  90% 3933/4393 [5:03:20<38:24,  5.01s/it]
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Training...:  90% 3934/4393 [5:03:24<37:14,  4.87s/it]
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Training...:  90% 3935/4393 [5:03:29<36:10,  4.74s/it]
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Training...:  90% 3936/4393 [5:03:33<35:12,  4.62s/it]
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Training...:  90% 3939/4393 [5:03:45<32:07,  4.24s/it]
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Training...:  90% 3940/4393 [5:03:49<30:37,  4.06s/it]
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Training...:  90% 3941/4393 [5:03:52<28:48,  3.82s/it]
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Training...:  90% 3944/4393 [5:04:00<22:53,  3.06s/it]
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Training...:  90% 3945/4393 [5:04:03<20:55,  2.80s/it]
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Training...:  90% 3946/4393 [5:04:05<18:59,  2.55s/it]
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Training...:  90% 3947/4393 [5:04:06<17:06,  2.30s/it]
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Training...:  90% 3948/4393 [5:04:08<15:13,  2.05s/it]
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Training...:  90% 3949/4393 [5:04:09<13:22,  1.81s/it]
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Training...:  90% 3950/4393 [5:04:10<11:27,  1.55s/it]
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Training...:  90% 3951/4393 [5:04:16<21:26,  2.91s/it]
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Training...:  90% 3952/4393 [5:04:22<28:39,  3.90s/it]
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Training...:  90% 3953/4393 [5:04:28<33:17,  4.54s/it]
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Training...:  90% 3954/4393 [5:04:34<36:14,  4.95s/it]
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Training...:  90% 3955/4393 [5:04:40<38:13,  5.24s/it]
+
Training...:  90% 3956/4393 [5:04:46<39:23,  5.41s/it]
+
                                                      

                                                             

+
Training...:  90% 3956/4393 [5:04:53<39:23,  5.41s/it]
Epoch ... (1/12):   8% 1/12 [10:55:52<64:20:33, 21057.59s/it]
+
Training...:  90% 3957/4393 [5:04:53<41:57,  5.77s/it]
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Training...:  90% 3958/4393 [5:04:58<41:47,  5.76s/it]
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Training...:  90% 3959/4393 [5:05:04<41:47,  5.78s/it]
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Training...:  90% 3960/4393 [5:05:10<41:39,  5.77s/it]
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Training...:  90% 3961/4393 [5:05:16<41:32,  5.77s/it]
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Training...:  90% 3962/4393 [5:05:21<41:26,  5.77s/it]
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Training...:  90% 3964/4393 [5:05:33<40:55,  5.72s/it]
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Training...:  90% 3965/4393 [5:05:39<41:00,  5.75s/it]
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Training...:  90% 3967/4393 [5:05:50<39:59,  5.63s/it]
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Training...:  90% 3968/4393 [5:05:55<39:27,  5.57s/it]
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Training...:  90% 3969/4393 [5:06:00<39:03,  5.53s/it]
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Training...:  90% 3970/4393 [5:06:06<38:36,  5.48s/it]
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Training...:  90% 3971/4393 [5:06:11<38:11,  5.43s/it]
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Training...:  90% 3972/4393 [5:06:16<37:48,  5.39s/it]
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Training...:  90% 3973/4393 [5:06:22<37:30,  5.36s/it]
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Training...:  90% 3975/4393 [5:06:32<36:40,  5.27s/it]
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Training...:  91% 3976/4393 [5:06:37<36:11,  5.21s/it]
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Training...:  91% 3977/4393 [5:06:42<35:49,  5.17s/it]
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Training...:  91% 3978/4393 [5:06:47<35:26,  5.12s/it]
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Training...:  91% 3979/4393 [5:06:52<35:11,  5.10s/it]
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Training...:  91% 3980/4393 [5:06:57<35:01,  5.09s/it]
+
Training...:  91% 3981/4393 [5:07:02<34:22,  5.01s/it]
+
                                                      

                                                             

+
Training...:  91% 3981/4393 [5:07:08<34:22,  5.01s/it]
Epoch ... (1/12):   8% 1/12 [10:58:07<64:20:33, 21057.59s/it]
+
Training...:  91% 3982/4393 [5:07:08<35:09,  5.13s/it]
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Training...:  91% 3983/4393 [5:07:12<33:57,  4.97s/it]
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Training...:  91% 3984/4393 [5:07:17<32:58,  4.84s/it]
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Training...:  91% 3985/4393 [5:07:21<32:02,  4.71s/it]
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Training...:  91% 3986/4393 [5:07:25<30:58,  4.57s/it]
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Training...:  91% 3987/4393 [5:07:30<30:08,  4.45s/it]
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Training...:  91% 3988/4393 [5:07:33<29:00,  4.30s/it]
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Training...:  91% 3989/4393 [5:07:37<27:47,  4.13s/it]
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Training...:  91% 3990/4393 [5:07:41<26:35,  3.96s/it]
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Training...:  91% 3991/4393 [5:07:44<25:12,  3.76s/it]
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Training...:  91% 3992/4393 [5:07:47<23:51,  3.57s/it]
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Training...:  91% 3993/4393 [5:07:50<22:18,  3.35s/it]
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Training...:  91% 3994/4393 [5:07:53<20:39,  3.11s/it]
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Training...:  91% 3995/4393 [5:07:55<18:58,  2.86s/it]
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Training...:  91% 3996/4393 [5:07:57<17:19,  2.62s/it]
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Training...:  91% 3997/4393 [5:07:59<15:43,  2.38s/it]
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Training...:  91% 3998/4393 [5:08:00<14:00,  2.13s/it]
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Training...:  91% 3999/4393 [5:08:01<12:08,  1.85s/it]
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Training...:  91% 4000/4393 [5:08:02<10:19,  1.58s/it]
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Training...:  91% 4001/4393 [5:08:09<19:09,  2.93s/it]
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Training...:  91% 4002/4393 [5:08:15<25:20,  3.89s/it]
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Training...:  91% 4003/4393 [5:08:21<29:22,  4.52s/it]
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Training...:  91% 4004/4393 [5:08:27<32:29,  5.01s/it]
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Training...:  91% 4005/4393 [5:08:33<34:44,  5.37s/it]
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Training...:  91% 4006/4393 [5:08:39<35:27,  5.50s/it]
+
                                                      

                                                             

+
Training...:  91% 4006/4393 [5:08:45<35:27,  5.50s/it]
Epoch ... (1/12):   8% 1/12 [10:59:45<64:20:33, 21057.59s/it]
+
Training...:  91% 4007/4393 [5:08:45<37:32,  5.84s/it]
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Training...:  91% 4008/4393 [5:08:51<37:16,  5.81s/it]
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Training...:  91% 4009/4393 [5:08:57<37:00,  5.78s/it]
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Training...:  91% 4010/4393 [5:09:03<36:50,  5.77s/it]
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Training...:  91% 4011/4393 [5:09:08<36:38,  5.75s/it]
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Training...:  91% 4012/4393 [5:09:14<36:23,  5.73s/it]
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Training...:  91% 4013/4393 [5:09:20<36:09,  5.71s/it]
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Training...:  91% 4014/4393 [5:09:25<35:57,  5.69s/it]
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Training...:  91% 4015/4393 [5:09:31<35:42,  5.67s/it]
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Training...:  91% 4017/4393 [5:09:42<35:25,  5.65s/it]
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Training...:  91% 4018/4393 [5:09:48<34:53,  5.58s/it]
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Training...:  92% 4020/4393 [5:09:58<34:02,  5.48s/it]
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Training...:  92% 4021/4393 [5:10:04<33:39,  5.43s/it]
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Training...:  92% 4022/4393 [5:10:09<33:19,  5.39s/it]
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Training...:  92% 4023/4393 [5:10:14<33:06,  5.37s/it]
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Training...:  92% 4024/4393 [5:10:20<32:40,  5.31s/it]
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Training...:  92% 4025/4393 [5:10:25<32:19,  5.27s/it]
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Training...:  92% 4026/4393 [5:10:30<32:01,  5.24s/it]
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Training...:  92% 4027/4393 [5:10:35<31:47,  5.21s/it]
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Training...:  92% 4028/4393 [5:10:40<31:22,  5.16s/it]
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Training...:  92% 4029/4393 [5:10:45<31:04,  5.12s/it]
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Training...:  92% 4030/4393 [5:10:50<30:30,  5.04s/it]
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Training...:  92% 4031/4393 [5:10:55<30:03,  4.98s/it]
+
                                                      

                                                             

+
Training...:  92% 4031/4393 [5:11:00<30:03,  4.98s/it]
Epoch ... (1/12):   8% 1/12 [11:02:00<64:20:33, 21057.59s/it]
+
Training...:  92% 4032/4393 [5:11:00<30:50,  5.13s/it]
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Training...:  92% 4033/4393 [5:11:05<29:51,  4.98s/it]
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Training...:  92% 4034/4393 [5:11:10<29:02,  4.85s/it]
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Training...:  92% 4035/4393 [5:11:14<28:10,  4.72s/it]
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Training...:  92% 4036/4393 [5:11:18<27:25,  4.61s/it]
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Training...:  92% 4037/4393 [5:11:22<26:34,  4.48s/it]
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Training...:  92% 4040/4393 [5:11:34<23:41,  4.03s/it]
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Training...:  92% 4041/4393 [5:11:37<22:22,  3.81s/it]
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Training...:  92% 4042/4393 [5:11:40<20:53,  3.57s/it]
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Training...:  92% 4044/4393 [5:11:45<17:44,  3.05s/it]
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Training...:  92% 4048/4393 [5:11:53<11:48,  2.05s/it]
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Training...:  92% 4049/4393 [5:11:54<10:21,  1.81s/it]
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Training...:  92% 4050/4393 [5:11:55<08:53,  1.55s/it]
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Training...:  92% 4051/4393 [5:12:01<16:31,  2.90s/it]
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Training...:  92% 4052/4393 [5:12:07<22:01,  3.87s/it]
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Training...:  92% 4053/4393 [5:12:13<25:38,  4.53s/it]
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Training...:  92% 4056/4393 [5:12:31<30:16,  5.39s/it]
+
                                                      

                                                             

+
Training...:  92% 4056/4393 [5:12:37<30:16,  5.39s/it]
Epoch ... (1/12):   8% 1/12 [11:03:37<64:20:33, 21057.59s/it]
+
Training...:  92% 4057/4393 [5:12:37<32:19,  5.77s/it]
+
Training...:  92% 4058/4393 [5:12:43<32:12,  5.77s/it]
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Training...:  92% 4059/4393 [5:12:49<32:06,  5.77s/it]
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Training...:  92% 4061/4393 [5:13:00<31:48,  5.75s/it]
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Training...:  92% 4062/4393 [5:13:06<31:28,  5.71s/it]
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Training...:  93% 4072/4393 [5:14:01<28:38,  5.35s/it]
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Training...:  93% 4081/4393 [5:14:46<25:46,  4.96s/it]
+
                                                      

                                                             

+
Training...:  93% 4081/4393 [5:14:52<25:46,  4.96s/it]
Epoch ... (1/12):   8% 1/12 [11:05:51<64:20:33, 21057.59s/it]
+
Training...:  93% 4082/4393 [5:14:52<26:27,  5.10s/it]
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Training...:  93% 4083/4393 [5:14:56<25:37,  4.96s/it]
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Training...:  93% 4106/4393 [5:16:28<25:52,  5.41s/it]
Epoch ... (1/12):   8% 1/12 [11:07:27<64:20:33, 21057.59s/it]
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Training...:  93% 4107/4393 [5:16:28<27:31,  5.78s/it]
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Training...:  94% 4131/4393 [5:18:43<22:08,  5.07s/it]
Epoch ... (1/12):   8% 1/12 [11:09:43<64:20:33, 21057.59s/it]
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Training...:  94% 4132/4393 [5:18:43<22:33,  5.18s/it]
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Training...:  95% 4156/4393 [5:20:21<21:11,  5.36s/it]
Epoch ... (1/12):   8% 1/12 [11:11:21<64:20:33, 21057.59s/it]
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Training...:  95% 4157/4393 [5:20:21<22:38,  5.76s/it]
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Training...:  95% 4181/4393 [5:22:36<17:34,  4.97s/it]
Epoch ... (1/12):   8% 1/12 [11:13:35<64:20:33, 21057.59s/it]
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Training...:  95% 4182/4393 [5:22:36<18:00,  5.12s/it]
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+
Training...:  96% 4206/4393 [5:24:13<17:01,  5.46s/it]
Epoch ... (1/12):   8% 1/12 [11:15:12<64:20:33, 21057.59s/it]
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Training...:  96% 4207/4393 [5:24:13<18:06,  5.84s/it]
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+
                                                      

                                                             

+
Training...:  96% 4231/4393 [5:26:27<13:21,  4.95s/it]
Epoch ... (1/12):   8% 1/12 [11:17:27<64:20:33, 21057.59s/it]
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Training...:  96% 4232/4393 [5:26:27<13:43,  5.12s/it]
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+
                                                      

                                                             

+
Training...:  97% 4256/4393 [5:28:04<12:20,  5.40s/it]
Epoch ... (1/12):   8% 1/12 [11:19:04<64:20:33, 21057.59s/it]
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Training...:  97% 4257/4393 [5:28:04<13:05,  5.77s/it]
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+
                                                      

                                                             

+
Training...:  97% 4281/4393 [5:30:20<09:19,  5.00s/it]
Epoch ... (1/12):   8% 1/12 [11:21:19<64:20:33, 21057.59s/it]
+
Training...:  97% 4282/4393 [5:30:20<09:29,  5.13s/it]
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Training...:  98% 4306/4393 [5:31:58<07:51,  5.42s/it]
Epoch ... (1/12):   8% 1/12 [11:22:57<64:20:33, 21057.59s/it]
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Training...:  99% 4331/4393 [5:34:13<05:12,  5.04s/it]
Epoch ... (1/12):   8% 1/12 [11:25:13<64:20:33, 21057.59s/it]
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Training...:  99% 4356/4393 [5:35:50<03:19,  5.38s/it]
Epoch ... (1/12):   8% 1/12 [11:26:50<64:20:33, 21057.59s/it]
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Training...: 100% 4381/4393 [5:38:02<00:56,  4.73s/it]
Epoch ... (1/12):   8% 1/12 [11:29:01<64:20:33, 21057.59s/it]
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Training...: 100% 4382/4393 [5:38:02<00:52,  4.80s/it]
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Training...: 100% 4393/4393 [5:38:31<00:00,  4.62s/it]
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Epoch ... (1/12):  17% 2/12 [11:29:30<57:16:35, 20619.59s/it]Step... (7900 | Loss: 0.13779808580875397, Learning Rate: 8.505252480972558e-05, Gradient Norm: 0.7253138422966003)
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Epoch ... (1/12):  17% 2/12 [11:36:53<57:16:35, 20619.59s/it]
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Epoch ... (1/12):  17% 2/12 [11:38:42<57:16:35, 20619.59s/it]
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Epoch ... (1/12):  17% 2/12 [11:40:47<57:16:35, 20619.59s/it]
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Training...:   4% 163/4393 [13:02<6:37:43,  5.64s/it]
Epoch ... (1/12):  17% 2/12 [11:42:34<57:16:35, 20619.59s/it]
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Training...:   4% 188/4393 [15:07<5:05:04,  4.35s/it]
Epoch ... (1/12):  17% 2/12 [11:44:40<57:16:35, 20619.59s/it]
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Training...:   5% 213/4393 [16:56<6:34:27,  5.66s/it]
Epoch ... (1/12):  17% 2/12 [11:46:28<57:16:35, 20619.59s/it]
+
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Epoch ... (1/12):  17% 2/12 [11:48:33<57:16:35, 20619.59s/it]
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Epoch ... (1/12):  17% 2/12 [11:50:22<57:16:35, 20619.59s/it]
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Training...:   7% 288/4393 [22:54<4:52:10,  4.27s/it]
Epoch ... (1/12):  17% 2/12 [11:52:26<57:16:35, 20619.59s/it]
+
Training...:   7% 289/4393 [22:54<4:54:18,  4.30s/it]
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Training...:   7% 313/4393 [24:41<6:23:22,  5.64s/it]
Epoch ... (1/12):  17% 2/12 [11:54:13<57:16:35, 20619.59s/it]
+
Training...:   7% 314/4393 [24:41<6:37:29,  5.85s/it]
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Training...:   8% 338/4393 [26:45<4:47:12,  4.25s/it]
Epoch ... (1/12):  17% 2/12 [11:56:17<57:16:35, 20619.59s/it]
+
Training...:   8% 339/4393 [26:45<4:48:52,  4.28s/it]
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Training...:   8% 363/4393 [28:32<6:22:02,  5.69s/it]
Epoch ... (1/12):  17% 2/12 [11:58:05<57:16:35, 20619.59s/it]
+
Training...:   8% 364/4393 [28:32<6:37:20,  5.92s/it]
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+
                                                     

                                                             

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Training...:   9% 388/4393 [30:37<4:48:28,  4.32s/it]
Epoch ... (1/12):  17% 2/12 [12:00:10<57:16:35, 20619.59s/it]
+
Training...:   9% 389/4393 [30:37<4:49:04,  4.33s/it]
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Training...:   9% 390/4393 [30:41<4:34:11,  4.11s/it]
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+
                                                     

                                                             

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Training...:   9% 413/4393 [32:25<6:14:27,  5.65s/it]
Epoch ... (1/12):  17% 2/12 [12:01:58<57:16:35, 20619.59s/it]
+
Training...:   9% 414/4393 [32:25<6:28:38,  5.86s/it]
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+
                                                     

                                                             

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Training...:  10% 438/4393 [34:29<4:39:05,  4.23s/it]
Epoch ... (1/12):  17% 2/12 [12:04:01<57:16:35, 20619.59s/it]
+
Training...:  10% 439/4393 [34:29<4:40:07,  4.25s/it]
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+
                                                     

                                                             

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Training...:  11% 463/4393 [36:15<6:11:36,  5.67s/it]
Epoch ... (1/12):  17% 2/12 [12:05:48<57:16:35, 20619.59s/it]
+
Training...:  11% 464/4393 [36:15<6:24:44,  5.88s/it]
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+
                                                     

                                                             

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Training...:  11% 488/4393 [38:19<4:35:18,  4.23s/it]
Epoch ... (1/12):  17% 2/12 [12:07:51<57:16:35, 20619.59s/it]
+
Training...:  11% 489/4393 [38:19<4:33:45,  4.21s/it]
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Training...:  12% 513/4393 [40:06<6:05:34,  5.65s/it]
Epoch ... (1/12):  17% 2/12 [12:09:38<57:16:35, 20619.59s/it]
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Training...:  12% 514/4393 [40:06<6:18:57,  5.86s/it]
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Training...:  12% 538/4393 [42:10<4:32:58,  4.25s/it]
Epoch ... (1/12):  17% 2/12 [12:11:42<57:16:35, 20619.59s/it]
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Training...:  12% 539/4393 [42:10<4:32:54,  4.25s/it]
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Training...:  13% 563/4393 [43:57<6:00:48,  5.65s/it]
Epoch ... (1/12):  17% 2/12 [12:13:29<57:16:35, 20619.59s/it]
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Training...:  13% 564/4393 [43:57<6:15:22,  5.88s/it]
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Training...:  13% 588/4393 [46:02<4:33:57,  4.32s/it]
Epoch ... (1/12):  17% 2/12 [12:15:34<57:16:35, 20619.59s/it]
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Training...:  13% 589/4393 [46:02<4:38:07,  4.39s/it]
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+
                                                     

                                                             

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Training...:  14% 613/4393 [47:50<6:02:24,  5.75s/it]
Epoch ... (1/12):  17% 2/12 [12:17:22<57:16:35, 20619.59s/it]
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Training...:  14% 614/4393 [47:50<6:14:31,  5.95s/it]
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+
                                                     

                                                             

+
Training...:  15% 638/4393 [49:54<4:27:40,  4.28s/it]
Epoch ... (1/12):  17% 2/12 [12:19:27<57:16:35, 20619.59s/it]
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Training...:  15% 639/4393 [49:54<4:29:21,  4.31s/it]
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+
                                                     

                                                             

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Training...:  15% 663/4393 [51:43<5:53:42,  5.69s/it]
Epoch ... (1/12):  17% 2/12 [12:21:15<57:16:35, 20619.59s/it]
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Training...:  15% 664/4393 [51:43<6:07:19,  5.91s/it]
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+
                                                     

                                                             

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Training...:  16% 688/4393 [53:47<4:24:41,  4.29s/it]
Epoch ... (1/12):  17% 2/12 [12:23:19<57:16:35, 20619.59s/it]
+
Training...:  16% 689/4393 [53:47<4:25:45,  4.30s/it]
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+
                                                     

                                                             

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Training...:  16% 713/4393 [55:35<5:48:49,  5.69s/it]
Epoch ... (1/12):  17% 2/12 [12:25:07<57:16:35, 20619.59s/it]
+
Training...:  16% 714/4393 [55:35<6:01:40,  5.90s/it]
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Training...:  17% 738/4393 [57:39<4:17:21,  4.22s/it]
Epoch ... (1/12):  17% 2/12 [12:27:11<57:16:35, 20619.59s/it]
+
Training...:  17% 739/4393 [57:39<4:18:58,  4.25s/it]
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+
Training...:  17% 763/4393 [59:27<5:50:26,  5.79s/it]
Epoch ... (1/12):  17% 2/12 [12:28:59<57:16:35, 20619.59s/it]
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Training...:  17% 764/4393 [59:27<6:03:49,  6.02s/it]
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Training...:  17% 765/4393 [59:32<5:57:06,  5.91s/it]
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+
Training...:  18% 788/4393 [1:01:32<4:19:46,  4.32s/it]
Epoch ... (1/12):  17% 2/12 [12:31:04<57:16:35, 20619.59s/it]
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Training...:  18% 789/4393 [1:01:32<4:20:22,  4.33s/it]
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Training...:  18% 790/4393 [1:01:35<4:07:17,  4.12s/it]
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Training...:  19% 813/4393 [1:03:14<5:37:10,  5.65s/it]
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+
Training...:  19% 813/4393 [1:03:21<5:37:10,  5.65s/it]
Epoch ... (1/12):  17% 2/12 [12:32:53<57:16:35, 20619.59s/it]
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Training...:  19% 814/4393 [1:03:21<5:51:05,  5.89s/it]
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Training...:  19% 815/4393 [1:03:26<5:45:20,  5.79s/it]
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Training...:  19% 819/4393 [1:03:48<5:28:35,  5.52s/it]
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+
Training...:  19% 838/4393 [1:05:25<4:14:54,  4.30s/it]
Epoch ... (1/12):  17% 2/12 [12:34:57<57:16:35, 20619.59s/it]
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Training...:  19% 839/4393 [1:05:25<4:15:39,  4.32s/it]
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Training...:  19% 840/4393 [1:05:28<4:02:46,  4.10s/it]
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Training...:  20% 863/4393 [1:07:13<5:34:48,  5.69s/it]
Epoch ... (1/12):  17% 2/12 [12:36:45<57:16:35, 20619.59s/it]
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Training...:  20% 864/4393 [1:07:13<5:47:29,  5.91s/it]
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+
                                                       

                                                             

+
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Epoch ... (1/12):  17% 2/12 [12:38:51<57:16:35, 20619.59s/it]
+
Training...:  20% 889/4393 [1:09:19<4:18:25,  4.43s/it]
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+
                                                       

                                                             

+
Training...:  21% 913/4393 [1:11:09<5:30:27,  5.70s/it]
Epoch ... (1/12):  17% 2/12 [12:40:41<57:16:35, 20619.59s/it]
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Training...:  21% 914/4393 [1:11:09<5:43:57,  5.93s/it]
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Training...:  21% 928/4393 [1:12:24<4:56:22,  5.13s/it]
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Training...:  21% 929/4393 [1:12:29<4:53:39,  5.09s/it]
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Training...:  21% 930/4393 [1:12:33<4:49:50,  5.02s/it]
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Training...:  21% 931/4393 [1:12:38<4:47:20,  4.98s/it]
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Training...:  21% 932/4393 [1:12:43<4:44:17,  4.93s/it]
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Training...:  21% 933/4393 [1:12:48<4:39:53,  4.85s/it]
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Training...:  21% 934/4393 [1:12:52<4:35:18,  4.78s/it]
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Training...:  21% 935/4393 [1:12:57<4:30:04,  4.69s/it]
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Training...:  21% 936/4393 [1:13:01<4:23:59,  4.58s/it]
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Training...:  21% 937/4393 [1:13:06<4:18:47,  4.49s/it]
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Training...:  21% 938/4393 [1:13:10<4:11:25,  4.37s/it]
+
                                                       

                                                             

+
Training...:  21% 938/4393 [1:13:14<4:11:25,  4.37s/it]
Epoch ... (1/12):  17% 2/12 [12:42:46<57:16:35, 20619.59s/it]
+
Training...:  21% 939/4393 [1:13:14<4:11:18,  4.37s/it]
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Training...:  21% 940/4393 [1:13:17<3:56:03,  4.10s/it]
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Training...:  21% 941/4393 [1:13:21<3:42:09,  3.86s/it]
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Training...:  21% 942/4393 [1:13:24<3:26:20,  3.59s/it]
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Training...:  21% 943/4393 [1:13:26<3:11:52,  3.34s/it]
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Training...:  21% 944/4393 [1:13:29<2:57:02,  3.08s/it]
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Training...:  22% 945/4393 [1:13:31<2:42:16,  2.82s/it]
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Training...:  22% 946/4393 [1:13:33<2:28:27,  2.58s/it]
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Training...:  22% 947/4393 [1:13:35<2:14:11,  2.34s/it]
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Training...:  22% 948/4393 [1:13:36<2:00:22,  2.10s/it]
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Training...:  22% 949/4393 [1:13:38<1:46:44,  1.86s/it]
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Training...:  22% 950/4393 [1:13:39<1:32:40,  1.62s/it]
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Training...:  22% 951/4393 [1:13:45<2:50:24,  2.97s/it]
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Training...:  22% 952/4393 [1:13:51<3:45:36,  3.93s/it]
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Training...:  22% 953/4393 [1:13:57<4:21:53,  4.57s/it]
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Training...:  22% 954/4393 [1:14:03<4:45:28,  4.98s/it]
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Training...:  22% 955/4393 [1:14:09<5:02:43,  5.28s/it]
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Training...:  22% 956/4393 [1:14:15<5:16:42,  5.53s/it]
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Training...:  22% 957/4393 [1:14:21<5:24:49,  5.67s/it]
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Training...:  22% 958/4393 [1:14:27<5:26:33,  5.70s/it]
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Training...:  22% 959/4393 [1:14:33<5:27:54,  5.73s/it]
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Training...:  22% 960/4393 [1:14:38<5:26:58,  5.71s/it]
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Training...:  22% 961/4393 [1:14:44<5:26:00,  5.70s/it]
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Training...:  22% 962/4393 [1:14:50<5:24:33,  5.68s/it]
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Training...:  22% 963/4393 [1:14:55<5:23:15,  5.65s/it]
+
                                                       

                                                             

+
Training...:  22% 963/4393 [1:15:02<5:23:15,  5.65s/it]
Epoch ... (1/12):  17% 2/12 [12:44:34<57:16:35, 20619.59s/it]
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Training...:  22% 964/4393 [1:15:02<5:35:22,  5.87s/it]
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Training...:  22% 965/4393 [1:15:07<5:29:53,  5.77s/it]
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Training...:  22% 966/4393 [1:15:13<5:25:26,  5.70s/it]
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Training...:  22% 967/4393 [1:15:18<5:21:36,  5.63s/it]
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Training...:  22% 968/4393 [1:15:24<5:18:06,  5.57s/it]
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Training...:  22% 969/4393 [1:15:29<5:21:12,  5.63s/it]
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Training...:  22% 970/4393 [1:15:35<5:16:50,  5.55s/it]
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Training...:  22% 971/4393 [1:15:40<5:13:23,  5.49s/it]
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Training...:  22% 972/4393 [1:15:45<5:09:43,  5.43s/it]
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Training...:  22% 973/4393 [1:15:51<5:06:26,  5.38s/it]
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Training...:  22% 974/4393 [1:15:56<5:02:38,  5.31s/it]
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Training...:  22% 975/4393 [1:16:01<4:59:27,  5.26s/it]
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Training...:  22% 976/4393 [1:16:06<4:56:50,  5.21s/it]
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Training...:  22% 977/4393 [1:16:11<4:53:44,  5.16s/it]
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Training...:  22% 978/4393 [1:16:16<4:51:04,  5.11s/it]
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Training...:  22% 979/4393 [1:16:21<4:48:28,  5.07s/it]
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Training...:  22% 980/4393 [1:16:26<4:44:52,  5.01s/it]
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Training...:  22% 981/4393 [1:16:31<4:41:29,  4.95s/it]
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Training...:  22% 982/4393 [1:16:35<4:36:26,  4.86s/it]
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Training...:  22% 983/4393 [1:16:40<4:32:03,  4.79s/it]
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Training...:  22% 984/4393 [1:16:45<4:29:57,  4.75s/it]
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Training...:  22% 985/4393 [1:16:49<4:24:29,  4.66s/it]
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Training...:  22% 986/4393 [1:16:53<4:16:50,  4.52s/it]
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Training...:  22% 987/4393 [1:16:57<4:08:39,  4.38s/it]
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Training...:  22% 988/4393 [1:17:01<3:59:16,  4.22s/it]
+
                                                       

                                                             

+
Training...:  22% 988/4393 [1:17:05<3:59:16,  4.22s/it]
Epoch ... (1/12):  17% 2/12 [12:46:38<57:16:35, 20619.59s/it]
+
Training...:  23% 989/4393 [1:17:05<3:57:55,  4.19s/it]
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Training...:  23% 990/4393 [1:17:09<3:43:02,  3.93s/it]
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Training...:  23% 991/4393 [1:17:12<3:28:24,  3.68s/it]
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Training...:  23% 992/4393 [1:17:15<3:13:55,  3.42s/it]
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Training...:  23% 993/4393 [1:17:17<2:59:08,  3.16s/it]
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Training...:  23% 994/4393 [1:17:19<2:44:01,  2.90s/it]
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Training...:  23% 995/4393 [1:17:22<2:30:00,  2.65s/it]
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Training...:  23% 996/4393 [1:17:23<2:16:46,  2.42s/it]
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Training...:  23% 997/4393 [1:17:25<2:03:45,  2.19s/it]
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Training...:  23% 998/4393 [1:17:26<1:50:49,  1.96s/it]
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Training...:  23% 999/4393 [1:17:28<1:37:40,  1.73s/it]
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Training...:  23% 1000/4393 [1:17:29<1:24:31,  1.49s/it]
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Training...:  23% 1001/4393 [1:17:35<2:43:11,  2.89s/it]
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Training...:  23% 1002/4393 [1:17:41<3:38:15,  3.86s/it]
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Training...:  23% 1003/4393 [1:17:47<4:15:03,  4.51s/it]
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Training...:  23% 1004/4393 [1:17:53<4:40:35,  4.97s/it]
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Training...:  23% 1005/4393 [1:17:59<4:58:02,  5.28s/it]
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Training...:  23% 1006/4393 [1:18:05<5:12:16,  5.53s/it]
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Training...:  23% 1007/4393 [1:18:11<5:22:05,  5.71s/it]
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Training...:  23% 1008/4393 [1:18:17<5:25:11,  5.76s/it]
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Training...:  23% 1009/4393 [1:18:23<5:26:08,  5.78s/it]
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Training...:  23% 1010/4393 [1:18:29<5:27:07,  5.80s/it]
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Training...:  23% 1011/4393 [1:18:34<5:25:22,  5.77s/it]
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Training...:  23% 1012/4393 [1:18:40<5:23:40,  5.74s/it]
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Training...:  23% 1013/4393 [1:18:46<5:22:20,  5.72s/it]
+
                                                        

                                                             

+
Training...:  23% 1013/4393 [1:18:52<5:22:20,  5.72s/it]
Epoch ... (1/12):  17% 2/12 [12:48:25<57:16:35, 20619.59s/it]
+
Training...:  23% 1014/4393 [1:18:52<5:34:01,  5.93s/it]
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Training...:  23% 1015/4393 [1:18:58<5:27:31,  5.82s/it]
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Training...:  23% 1016/4393 [1:19:03<5:21:23,  5.71s/it]
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Training...:  23% 1017/4393 [1:19:09<5:17:34,  5.64s/it]
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Training...:  23% 1018/4393 [1:19:14<5:14:16,  5.59s/it]
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Training...:  23% 1019/4393 [1:19:20<5:15:24,  5.61s/it]
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Training...:  23% 1020/4393 [1:19:25<5:11:19,  5.54s/it]
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Training...:  23% 1021/4393 [1:19:31<5:07:44,  5.48s/it]
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Training...:  23% 1022/4393 [1:19:36<5:04:24,  5.42s/it]
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Training...:  23% 1023/4393 [1:19:41<5:02:12,  5.38s/it]
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Training...:  23% 1024/4393 [1:19:46<4:58:49,  5.32s/it]
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Training...:  23% 1025/4393 [1:19:52<4:56:36,  5.28s/it]
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Training...:  23% 1027/4393 [1:20:02<4:51:32,  5.20s/it]
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Training...:  23% 1028/4393 [1:20:07<4:48:31,  5.14s/it]
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Training...:  23% 1029/4393 [1:20:12<4:44:58,  5.08s/it]
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Training...:  23% 1030/4393 [1:20:17<4:41:18,  5.02s/it]
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Training...:  23% 1031/4393 [1:20:22<4:39:35,  4.99s/it]
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Training...:  23% 1032/4393 [1:20:26<4:36:14,  4.93s/it]
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Training...:  24% 1033/4393 [1:20:31<4:32:29,  4.87s/it]
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Training...:  24% 1034/4393 [1:20:36<4:29:07,  4.81s/it]
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Training...:  24% 1035/4393 [1:20:40<4:25:57,  4.75s/it]
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Training...:  24% 1036/4393 [1:20:45<4:19:33,  4.64s/it]
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Training...:  24% 1037/4393 [1:20:49<4:12:51,  4.52s/it]
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Training...:  24% 1038/4393 [1:20:53<4:06:34,  4.41s/it]
+
                                                        

                                                             

+
Training...:  24% 1038/4393 [1:20:58<4:06:34,  4.41s/it]
Epoch ... (1/12):  17% 2/12 [12:50:30<57:16:35, 20619.59s/it]
+
Training...:  24% 1039/4393 [1:20:58<4:10:07,  4.47s/it]
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Training...:  24% 1040/4393 [1:21:01<3:57:08,  4.24s/it]
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Training...:  24% 1041/4393 [1:21:05<3:43:18,  4.00s/it]
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Training...:  24% 1042/4393 [1:21:08<3:30:44,  3.77s/it]
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Training...:  24% 1043/4393 [1:21:11<3:17:14,  3.53s/it]
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Training...:  24% 1044/4393 [1:21:14<3:04:12,  3.30s/it]
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Training...:  24% 1045/4393 [1:21:16<2:50:26,  3.05s/it]
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Training...:  24% 1046/4393 [1:21:19<2:36:06,  2.80s/it]
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Training...:  24% 1047/4393 [1:21:20<2:21:36,  2.54s/it]
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Training...:  24% 1048/4393 [1:21:22<2:05:53,  2.26s/it]
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Training...:  24% 1049/4393 [1:21:23<1:49:55,  1.97s/it]
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Training...:  24% 1050/4393 [1:21:24<1:33:52,  1.68s/it]
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Training...:  24% 1051/4393 [1:21:30<2:48:07,  3.02s/it]
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Training...:  24% 1053/4393 [1:21:43<4:14:45,  4.58s/it]
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Training...:  24% 1054/4393 [1:21:49<4:36:47,  4.97s/it]
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Training...:  24% 1055/4393 [1:21:54<4:51:57,  5.25s/it]
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Training...:  24% 1056/4393 [1:22:00<5:03:20,  5.45s/it]
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Training...:  24% 1057/4393 [1:22:06<5:10:27,  5.58s/it]
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Training...:  24% 1058/4393 [1:22:12<5:13:46,  5.65s/it]
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Training...:  24% 1059/4393 [1:22:18<5:18:01,  5.72s/it]
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Training...:  24% 1060/4393 [1:22:24<5:18:24,  5.73s/it]
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Training...:  24% 1061/4393 [1:22:29<5:19:08,  5.75s/it]
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Training...:  24% 1062/4393 [1:22:35<5:19:35,  5.76s/it]
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Training...:  24% 1063/4393 [1:22:41<5:20:55,  5.78s/it]
+
                                                        

                                                             

+
Training...:  24% 1063/4393 [1:22:48<5:20:55,  5.78s/it]
Epoch ... (1/12):  17% 2/12 [12:52:20<57:16:35, 20619.59s/it]
+
Training...:  24% 1064/4393 [1:22:48<5:34:06,  6.02s/it]
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Training...:  24% 1065/4393 [1:22:53<5:26:53,  5.89s/it]
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Training...:  24% 1066/4393 [1:22:59<5:20:02,  5.77s/it]
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Training...:  24% 1069/4393 [1:23:15<5:07:03,  5.54s/it]
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Training...:  24% 1071/4393 [1:23:26<5:01:28,  5.45s/it]
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Training...:  24% 1072/4393 [1:23:31<4:59:35,  5.41s/it]
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Training...:  24% 1074/4393 [1:23:42<4:54:11,  5.32s/it]
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Training...:  25% 1079/4393 [1:24:07<4:42:30,  5.11s/it]
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Training...:  25% 1080/4393 [1:24:12<4:38:43,  5.05s/it]
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Training...:  25% 1081/4393 [1:24:17<4:34:51,  4.98s/it]
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Training...:  25% 1083/4393 [1:24:26<4:25:49,  4.82s/it]
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Training...:  25% 1084/4393 [1:24:31<4:20:59,  4.73s/it]
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Training...:  25% 1085/4393 [1:24:35<4:15:28,  4.63s/it]
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Training...:  25% 1086/4393 [1:24:39<4:09:06,  4.52s/it]
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Training...:  25% 1088/4393 [1:24:47<3:54:25,  4.26s/it]
+
                                                        

                                                             

+
Training...:  25% 1088/4393 [1:24:52<3:54:25,  4.26s/it]
Epoch ... (1/12):  17% 2/12 [12:54:24<57:16:35, 20619.59s/it]
+
Training...:  25% 1089/4393 [1:24:52<3:55:19,  4.27s/it]
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Training...:  25% 1090/4393 [1:24:55<3:42:22,  4.04s/it]
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Training...:  25% 1091/4393 [1:24:58<3:28:02,  3.78s/it]
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Training...:  25% 1096/4393 [1:25:11<2:19:31,  2.54s/it]
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Training...:  25% 1098/4393 [1:25:14<1:53:02,  2.06s/it]
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Training...:  25% 1112/4393 [1:26:27<5:14:34,  5.75s/it]
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Training...:  25% 1113/4393 [1:26:33<5:12:21,  5.71s/it]
+
                                                        

                                                             

+
Training...:  25% 1113/4393 [1:26:39<5:12:21,  5.71s/it]
Epoch ... (1/12):  17% 2/12 [12:56:11<57:16:35, 20619.59s/it]
+
Training...:  25% 1114/4393 [1:26:39<5:22:47,  5.91s/it]
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Training...:  25% 1115/4393 [1:26:45<5:16:43,  5.80s/it]
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Training...:  26% 1121/4393 [1:27:17<4:56:01,  5.43s/it]
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Training...:  26% 1122/4393 [1:27:22<4:52:50,  5.37s/it]
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+
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Epoch ... (1/12):  17% 2/12 [12:58:15<57:16:35, 20619.59s/it]
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+
                                                        

                                                             

+
Training...:  26% 1163/4393 [1:30:32<5:13:33,  5.82s/it]
Epoch ... (1/12):  17% 2/12 [13:00:04<57:16:35, 20619.59s/it]
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+
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Epoch ... (1/12):  17% 2/12 [13:02:10<57:16:35, 20619.59s/it]
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+
Training...:  28% 1213/4393 [1:34:27<5:03:02,  5.72s/it]
Epoch ... (1/12):  17% 2/12 [13:03:59<57:16:35, 20619.59s/it]Step... (8800 | Loss: 0.0734051838517189, Learning Rate: 8.32343430374749e-05, Gradient Norm: 0.5301979780197144)
+Step... (8825 | Loss: 0.0855112299323082, Learning Rate: 8.318383333971724e-05, Gradient Norm: 0.5376077890396118)
+Step... (8850 | Loss: 0.10068231076002121, Learning Rate: 8.313333091791719e-05, Gradient Norm: 1.0139813423156738)
+Step... (8875 | Loss: 0.06252378225326538, Learning Rate: 8.308282849611714e-05, Gradient Norm: 0.48730936646461487)
+Step... (8900 | Loss: 0.08460403978824615, Learning Rate: 8.303231879835948e-05, Gradient Norm: 0.4558766186237335)
+Step... (8925 | Loss: 0.09871721267700195, Learning Rate: 8.298181637655944e-05, Gradient Norm: 0.6362630128860474)
+Step... (8950 | Loss: 0.11740045249462128, Learning Rate: 8.293131395475939e-05, Gradient Norm: 0.6272518038749695)
+Step... (8975 | Loss: 0.13512758910655975, Learning Rate: 8.288080425700173e-05, Gradient Norm: 0.5893998146057129)
+Step... (9000 | Loss: 0.05581226944923401, Learning Rate: 8.283030183520168e-05, Gradient Norm: 0.48278042674064636)
+Step... (9025 | Loss: 0.08583104610443115, Learning Rate: 8.277979941340163e-05, Gradient Norm: 0.5676160454750061)
+Step... (9050 | Loss: 0.08980081230401993, Learning Rate: 8.272928971564397e-05, Gradient Norm: 0.5202922821044922)
+Step... (9075 | Loss: 0.12204743176698685, Learning Rate: 8.267878729384392e-05, Gradient Norm: 0.6033841967582703)
+Step... (9100 | Loss: 0.07934217900037766, Learning Rate: 8.262828487204388e-05, Gradient Norm: 0.5386624932289124)
+Step... (9125 | Loss: 0.09263342618942261, Learning Rate: 8.257777517428622e-05, Gradient Norm: 0.49038422107696533)
+Step... (9150 | Loss: 0.09809651970863342, Learning Rate: 8.252727275248617e-05, Gradient Norm: 0.5518418550491333)
+Step... (9175 | Loss: 0.08282437175512314, Learning Rate: 8.247677033068612e-05, Gradient Norm: 0.6772879958152771)
+Step... (9200 | Loss: 0.09388335794210434, Learning Rate: 8.242626063292846e-05, Gradient Norm: 0.8972507119178772)
+Step... (9225 | Loss: 0.11405259370803833, Learning Rate: 8.237575821112841e-05, Gradient Norm: 0.5674875974655151)
+Step... (9250 | Loss: 0.09774433821439743, Learning Rate: 8.232525578932837e-05, Gradient Norm: 0.9340572357177734)
+Step... (9275 | Loss: 0.07160411030054092, Learning Rate: 8.22747460915707e-05, Gradient Norm: 0.7407450675964355)
+Step... (9300 | Loss: 0.09505634754896164, Learning Rate: 8.222424366977066e-05, Gradient Norm: 0.6917828917503357)
+Step... (9325 | Loss: 0.12817955017089844, Learning Rate: 8.217374124797061e-05, Gradient Norm: 1.1176568269729614)
+Step... (9350 | Loss: 0.11201604455709457, Learning Rate: 8.212323155021295e-05, Gradient Norm: 0.725493848323822)
+Step... (9375 | Loss: 0.16065749526023865, Learning Rate: 8.20727291284129e-05, Gradient Norm: 0.9583228230476379)
+Step... (9400 | Loss: 0.076621875166893, Learning Rate: 8.202222670661286e-05, Gradient Norm: 0.5754097700119019)
+Step... (9425 | Loss: 0.10213085263967514, Learning Rate: 8.19717170088552e-05, Gradient Norm: 0.740641713142395)
+Step... (9450 | Loss: 0.09721554070711136, Learning Rate: 8.192121458705515e-05, Gradient Norm: 0.5298651456832886)
+Step... (9475 | Loss: 0.08753546327352524, Learning Rate: 8.18707121652551e-05, Gradient Norm: 0.508285403251648)
+Step... (9500 | Loss: 0.07296166568994522, Learning Rate: 8.182020246749744e-05, Gradient Norm: 0.8791430592536926)
+Step... (9525 | Loss: 0.1059366762638092, Learning Rate: 8.176970004569739e-05, Gradient Norm: 0.9860773086547852)
+Step... (9550 | Loss: 0.09692548960447311, Learning Rate: 8.171919034793973e-05, Gradient Norm: 0.4978175759315491)
+Step... (9575 | Loss: 0.11797724664211273, Learning Rate: 8.166868792613968e-05, Gradient Norm: 0.6675213575363159)
+Step... (9600 | Loss: 0.13216069340705872, Learning Rate: 8.161817822838202e-05, Gradient Norm: 19.723308563232422)
+Step... (9625 | Loss: 0.08851467072963715, Learning Rate: 8.156767580658197e-05, Gradient Norm: 0.5197475552558899)
+Step... (9650 | Loss: 0.08510927855968475, Learning Rate: 8.151717338478193e-05, Gradient Norm: 0.6016721725463867)
+Step... (9
+
+
Evaluating ...:   0% 0/85 [00:00<?, ?it/s]675 | Loss: 0.07621399313211441, Learning Rate: 8.146666368702427e-05, Gradient Norm: 0.5809992551803589)
+Step... (9700 | Loss: 0.10198916494846344, Learning Rate: 8.141616126522422e-05, Gradient Norm: 0.552069902420044)
+Step... (9725 | Loss: 0.12253397703170776, Learning Rate: 8.136565156746656e-05, Gradient Norm: 0.5788819789886475)
+Step... (9750 | Loss: 0.07940845936536789, Learning Rate: 8.131514914566651e-05, Gradient Norm: 0.5337548851966858)
+Step... (9775 | Loss: 0.07598816603422165, Learning Rate: 8.126463944790885e-05, Gradient Norm: 0.5351502299308777)
+Step... (9800 | Loss: 0.07974324375391006, Learning Rate: 8.12141370261088e-05, Gradient Norm: 0.4442839026451111)
+Step... (9825 | Loss: 0.07073494046926498, Learning Rate: 8.116362732835114e-05, Gradient Norm: 0.517856776714325)
+Step... (9850 | Loss: 0.08271614462137222, Learning Rate: 8.111312490655109e-05, Gradient Norm: 0.7158600687980652)
+Step... (9875 | Loss: 0.12278185784816742, Learning Rate: 8.106262248475105e-05, Gradient Norm: 0.8880561590194702)
+Step... (9900 | Loss: 0.10471830517053604, Learning Rate: 8.101211278699338e-05, Gradient Norm: 1.0475046634674072)
+Step... (9925 | Loss: 0.07849471271038055, Learning Rate: 8.096161036519334e-05, Gradient Norm: 0.5359142422676086)
+Step... (9950 | Loss: 0.0894748792052269, Learning Rate: 8.091110794339329e-05, Gradient Norm: 0.5352652072906494)
+Step... (9975 | Loss: 0.1043805480003357, Learning Rate: 8.086059824563563e-05, Gradient Norm: 0.7022120952606201)
+Step... (10000 | Loss: 0.12705227732658386, Learning Rate: 8.081009582383558e-05, Gradient Norm: 1.0012972354888916)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:291: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  batch_sizes |= {t.shape[0] for t in jax.tree_leaves(a)}
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:312: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(pad, tree)
+run_flax_speech_recognition_seq2seq.py:1308: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  loss = jax.tree_map(lambda l: l / total_samples, loss)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1127: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  cache = jax.tree_map(
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1213: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  model_outputs["past_key_values"] = jax.tree_map(lambda x: flatten_beam_dim(x), next_cache)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:321: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return out if static_return else jax.tree_map(unpad, out)
+
+
+
Evaluating ...:   1% 1/85 [01:36<2:15:12, 96.58s/it]/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:291: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  batch_sizes |= {t.shape[0] for t in jax.tree_leaves(a)}
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:312: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(pad, tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+run_flax_speech_recognition_seq2seq.py:336: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, t)
+run_flax_speech_recognition_seq2seq.py:1308: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  loss = jax.tree_map(lambda l: l / total_samples, loss)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1127: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  cache = jax.tree_map(
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1213: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  model_outputs["past_key_values"] = jax.tree_map(lambda x: flatten_beam_dim(x), next_cache)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:321: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return out if static_return else jax.tree_map(unpad, out)
+
+
+
Evaluating ...:   2% 2/85 [03:03<2:05:59, 91.08s/it]/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:291: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  batch_sizes |= {t.shape[0] for t in jax.tree_leaves(a)}
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:312: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(pad, tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+run_flax_speech_recognition_seq2seq.py:336: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, t)
+run_flax_speech_recognition_seq2seq.py:1308: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  loss = jax.tree_map(lambda l: l / total_samples, loss)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1127: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  cache = jax.tree_map(
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1213: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  model_outputs["past_key_values"] = jax.tree_map(lambda x: flatten_beam_dim(x), next_cache)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:321: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return out if static_return else jax.tree_map(unpad, out)
+
+
+
Evaluating ...:   4% 3/85 [04:20<1:55:14, 84.32s/it]/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:291: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  batch_sizes |= {t.shape[0] for t in jax.tree_leaves(a)}
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:312: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(pad, tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+run_flax_speech_recognition_seq2seq.py:336: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, t)
+run_flax_speech_recognition_seq2seq.py:1308: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  loss = jax.tree_map(lambda l: l / total_samples, loss)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1127: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  cache = jax.tree_map(
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1213: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  model_outputs["past_key_values"] = jax.tree_map(lambda x: flatten_beam_dim(x), next_cache)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:321: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return out if static_return else jax.tree_map(unpad, out)
+
+
+
Evaluating ...:   5% 4/85 [05:28<1:45:06, 77.85s/it]/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:291: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  batch_sizes |= {t.shape[0] for t in jax.tree_leaves(a)}
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:312: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(pad, tree)
+
+
+
Evaluating ...:   6% 5/85 [05:32<1:08:32, 51.41s/it]/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+run_flax_speech_recognition_seq2seq.py:336: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, t)
+run_flax_speech_recognition_seq2seq.py:1308: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  loss = jax.tree_map(lambda l: l / total_samples, loss)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1127: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  cache = jax.tree_map(
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1213: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  model_outputs["past_key_values"] = jax.tree_map(lambda x: flatten_beam_dim(x), next_cache)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:321: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return out if static_return else jax.tree_map(unpad, out)
+
+
+
Evaluating ...:   7% 6/85 [06:35<1:13:02, 55.48s/it]/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:291: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  batch_sizes |= {t.shape[0] for t in jax.tree_leaves(a)}
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:312: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(pad, tree)
+
+
+
Evaluating ...:   8% 7/85 [06:40<50:20, 38.72s/it]  /home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+run_flax_speech_recognition_seq2seq.py:336: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, t)
+run_flax_speech_recognition_seq2seq.py:1308: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  loss = jax.tree_map(lambda l: l / total_samples, loss)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1127: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  cache = jax.tree_map(
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1213: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  model_outputs["past_key_values"] = jax.tree_map(lambda x: flatten_beam_dim(x), next_cache)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:321: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return out if static_return else jax.tree_map(unpad, out)
+
+
+
Evaluating ...:   9% 8/85 [07:40<58:25, 45.52s/it]/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:291: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  batch_sizes |= {t.shape[0] for t in jax.tree_leaves(a)}
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:312: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(pad, tree)
+
+
+
Evaluating ...:  11% 9/85 [07:44<41:14, 32.56s/it]/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+run_flax_speech_recognition_seq2seq.py:336: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, t)
+run_flax_speech_recognition_seq2seq.py:1308: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  loss = jax.tree_map(lambda l: l / total_samples, loss)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1127: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  cache = jax.tree_map(
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1213: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  model_outputs["past_key_values"] = jax.tree_map(lambda x: flatten_beam_dim(x), next_cache)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:321: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return out if static_return else jax.tree_map(unpad, out)
+
+
+
Evaluating ...:  12% 10/85 [08:41<50:11, 40.15s/it]/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:291: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  batch_sizes |= {t.shape[0] for t in jax.tree_leaves(a)}
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:312: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(pad, tree)
+
+
+
Evaluating ...:  13% 11/85 [08:45<35:43, 28.96s/it]
+
+
Evaluating ...:  14% 12/85 [08:48<25:48, 21.21s/it]/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+run_flax_speech_recognition_seq2seq.py:336: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, t)
+run_flax_speech_recognition_seq2seq.py:1308: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  loss = jax.tree_map(lambda l: l / total_samples, loss)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1127: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  cache = jax.tree_map(
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1213: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  model_outputs["past_key_values"] = jax.tree_map(lambda x: flatten_beam_dim(x), next_cache)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:321: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return out if static_return else jax.tree_map(unpad, out)
+
+
+
Evaluating ...:  15% 13/85 [10:25<53:04, 44.22s/it]/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:291: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  batch_sizes |= {t.shape[0] for t in jax.tree_leaves(a)}
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:312: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(pad, tree)
+
+
+
Evaluating ...:  16% 14/85 [10:28<37:33, 31.74s/it]
+
+
Evaluating ...:  18% 15/85 [10:31<26:57, 23.10s/it]/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+run_flax_speech_recognition_seq2seq.py:336: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, t)
+run_flax_speech_recognition_seq2seq.py:1308: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  loss = jax.tree_map(lambda l: l / total_samples, loss)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1127: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  cache = jax.tree_map(
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1213: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  model_outputs["past_key_values"] = jax.tree_map(lambda x: flatten_beam_dim(x), next_cache)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:321: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return out if static_return else jax.tree_map(unpad, out)
+
+
+
Evaluating ...:  19% 16/85 [11:31<39:08, 34.04s/it]/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:291: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  batch_sizes |= {t.shape[0] for t in jax.tree_leaves(a)}
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:312: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(pad, tree)
+
+
+
Evaluating ...:  20% 17/85 [11:35<28:21, 25.02s/it]
+
+
Evaluating ...:  21% 18/85 [11:38<20:49, 18.65s/it]
+
+
Evaluating ...:  22% 19/85 [11:43<15:41, 14.26s/it]/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+run_flax_speech_recognition_seq2seq.py:336: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, t)
+run_flax_speech_recognition_seq2seq.py:1308: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  loss = jax.tree_map(lambda l: l / total_samples, loss)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1127: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  cache = jax.tree_map(
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1213: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  model_outputs["past_key_values"] = jax.tree_map(lambda x: flatten_beam_dim(x), next_cache)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:321: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return out if static_return else jax.tree_map(unpad, out)
+
+
+
Evaluating ...:  24% 20/85 [12:51<33:06, 30.57s/it]/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:291: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  batch_sizes |= {t.shape[0] for t in jax.tree_leaves(a)}
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:312: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(pad, tree)
+
+
+
Evaluating ...:  25% 21/85 [12:59<25:27, 23.86s/it]
+
+
Evaluating ...:  26% 22/85 [13:08<20:13, 19.26s/it]
+
+
Evaluating ...:  27% 23/85 [13:14<15:57, 15.45s/it]/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+run_flax_speech_recognition_seq2seq.py:336: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, t)
+run_flax_speech_recognition_seq2seq.py:1308: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  loss = jax.tree_map(lambda l: l / total_samples, loss)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1127: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  cache = jax.tree_map(
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1213: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  model_outputs["past_key_values"] = jax.tree_map(lambda x: flatten_beam_dim(x), next_cache)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:321: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return out if static_return else jax.tree_map(unpad, out)
+
+
+
Evaluating ...:  28% 24/85 [14:26<32:48, 32.27s/it]/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:291: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  batch_sizes |= {t.shape[0] for t in jax.tree_leaves(a)}
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:312: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(pad, tree)
+
+
+
Evaluating ...:  29% 25/85 [14:31<24:04, 24.07s/it]
+
+
Evaluating ...:  31% 26/85 [14:35<17:52, 18.17s/it]
+
+
Evaluating ...:  32% 27/85 [14:40<13:32, 14.01s/it]
+
+
Evaluating ...:  33% 28/85 [14:43<10:25, 10.98s/it]
+
+
Evaluating ...:  34% 29/85 [14:48<08:21,  8.96s/it]
+
+
Evaluating ...:  35% 30/85 [14:51<06:39,  7.27s/it]
+
+
Evaluating ...:  36% 31/85 [14:55<05:31,  6.13s/it]
+
+
Evaluating ...:  38% 32/85 [14:58<04:41,  5.32s/it]
+
+
Evaluating ...:  39% 33/85 [15:01<04:07,  4.76s/it]
+
+
Evaluating ...:  40% 34/85 [15:05<03:41,  4.35s/it]
+
+
Evaluating ...:  41% 35/85 [15:09<03:27,  4.16s/it]
+
+
Evaluating ...:  42% 36/85 [15:13<03:22,  4.14s/it]
+
+
Evaluating ...:  44% 37/85 [15:17<03:16,  4.10s/it]
+
+
Evaluating ...:  45% 38/85 [15:20<03:08,  4.01s/it]
+
+
Evaluating ...:  46% 39/85 [15:26<03:26,  4.49s/it]
+
+
Evaluating ...:  47% 40/85 [15:32<03:36,  4.81s/it]
+
+
Evaluating ...:  48% 41/85 [15:38<03:46,  5.15s/it]
+
+
Evaluating ...:  49% 42/85 [15:47<04:31,  6.31s/it]
+
+
Evaluating ...:  51% 43/85 [15:55<04:57,  7.08s/it]/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+run_flax_speech_recognition_seq2seq.py:336: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, t)
+run_flax_speech_recognition_seq2seq.py:1308: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  loss = jax.tree_map(lambda l: l / total_samples, loss)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1127: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  cache = jax.tree_map(
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1213: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  model_outputs["past_key_values"] = jax.tree_map(lambda x: flatten_beam_dim(x), next_cache)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:321: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return out if static_return else jax.tree_map(unpad, out)
+
+
+
Evaluating ...:  52% 44/85 [17:18<20:15, 29.65s/it]/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:291: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  batch_sizes |= {t.shape[0] for t in jax.tree_leaves(a)}
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:312: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(pad, tree)
+
+
+
Evaluating ...:  53% 45/85 [17:23<14:48, 22.22s/it]
+
+
Evaluating ...:  54% 46/85 [17:27<11:02, 16.98s/it]
+
+
Evaluating ...:  55% 47/85 [17:31<08:17, 13.10s/it]
+
+
Evaluating ...:  56% 48/85 [17:35<06:20, 10.28s/it]
+
+
Evaluating ...:  58% 49/85 [17:39<05:01,  8.37s/it]
+
+
Evaluating ...:  59% 50/85 [17:43<04:03,  6.96s/it]
+
+
Evaluating ...:  60% 51/85 [17:47<03:24,  6.02s/it]
+
+
Evaluating ...:  61% 52/85 [17:50<02:50,  5.16s/it]
+
+
Evaluating ...:  62% 53/85 [17:53<02:26,  4.57s/it]
+
+
Evaluating ...:  64% 54/85 [17:56<02:12,  4.26s/it]
+
+
Evaluating ...:  65% 55/85 [18:00<02:02,  4.07s/it]
+
+
Evaluating ...:  66% 56/85 [18:04<01:59,  4.13s/it]
+
+
Evaluating ...:  67% 57/85 [18:09<02:00,  4.30s/it]
+
+
Evaluating ...:  68% 58/85 [18:13<01:54,  4.26s/it]
+
+
Evaluating ...:  69% 59/85 [18:17<01:47,  4.12s/it]
+
+
Evaluating ...:  71% 60/85 [18:21<01:42,  4.09s/it]
+
+
Evaluating ...:  72% 61/85 [18:25<01:34,  3.96s/it]
+
+
Evaluating ...:  73% 62/85 [18:30<01:43,  4.49s/it]
+
+
Evaluating ...:  74% 63/85 [18:38<01:57,  5.32s/it]/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+run_flax_speech_recognition_seq2seq.py:336: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, t)
+run_flax_speech_recognition_seq2seq.py:1308: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  loss = jax.tree_map(lambda l: l / total_samples, loss)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1127: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  cache = jax.tree_map(
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1213: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  model_outputs["past_key_values"] = jax.tree_map(lambda x: flatten_beam_dim(x), next_cache)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:321: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return out if static_return else jax.tree_map(unpad, out)
+
+
+
Evaluating ...:  75% 64/85 [20:18<11:48, 33.73s/it]/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:291: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  batch_sizes |= {t.shape[0] for t in jax.tree_leaves(a)}
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:312: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(pad, tree)
+
+
+
Evaluating ...:  76% 65/85 [20:24<08:28, 25.42s/it]
+
+
Evaluating ...:  78% 66/85 [20:29<06:06, 19.31s/it]
+
+
Evaluating ...:  79% 67/85 [20:33<04:27, 14.86s/it]
+
+
Evaluating ...:  80% 68/85 [20:37<03:17, 11.62s/it]
+
+
Evaluating ...:  81% 69/85 [20:41<02:29,  9.36s/it]
+
+
Evaluating ...:  82% 70/85 [20:45<01:55,  7.69s/it]
+
+
Evaluating ...:  84% 71/85 [20:49<01:30,  6.47s/it]
+
+
Evaluating ...:  85% 72/85 [20:52<01:12,  5.58s/it]
+
+
Evaluating ...:  86% 73/85 [20:56<01:00,  5.02s/it]
+
+
Evaluating ...:  87% 74/85 [20:59<00:49,  4.54s/it]
+
+
Evaluating ...:  88% 75/85 [21:03<00:42,  4.25s/it]
+
+
Evaluating ...:  89% 76/85 [21:06<00:35,  3.90s/it]
+
+
Evaluating ...:  91% 77/85 [21:10<00:30,  3.86s/it]
+
+
Evaluating ...:  92% 78/85 [21:14<00:26,  3.80s/it]
+
+
Evaluating ...:  93% 79/85 [21:17<00:21,  3.61s/it]
+
+
Evaluating ...:  94% 80/85 [21:21<00:19,  3.85s/it]
+
+
Evaluating ...:  95% 81/85 [21:26<00:16,  4.03s/it]
+
+
Evaluating ...:  96% 82/85 [21:31<00:13,  4.48s/it]
+
+
Evaluating ...:  98% 83/85 [21:36<00:09,  4.54s/it]
+
+
Evaluating ...:  99% 84/85 [21:41<00:04,  4.62s/it]/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+run_flax_speech_recognition_seq2seq.py:336: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, t)
+run_flax_speech_recognition_seq2seq.py:1308: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  loss = jax.tree_map(lambda l: l / total_samples, loss)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:112: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  scopes, treedef = jax.tree_flatten(scope_tree)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/lift.py:729: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  lengths = set(jax.tree_leaves(lengths))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:134: FutureWarning: jax.tree_flatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_flatten instead.
+  in_avals, in_tree = jax.tree_flatten(input_avals)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/linen/transforms.py:249: FutureWarning: jax.tree_leaves is deprecated, and will be removed in a future release. Use jax.tree_util.tree_leaves instead.
+  jax.tree_leaves(tree)))
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/core/axes_scan.py:146: FutureWarning: jax.tree_unflatten is deprecated, and will be removed in a future release. Use jax.tree_util.tree_unflatten instead.
+  broadcast_in, constants_out = jax.tree_unflatten(out_tree(), out_flat)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1127: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  cache = jax.tree_map(
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1038: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(gather_fn, nested)
+/home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/models/modeling_flax_speech_encoder_decoder.py:1213: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  model_outputs["past_key_values"] = jax.tree_map(lambda x: flatten_beam_dim(x), next_cache)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/jax_utils.py:321: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return out if static_return else jax.tree_map(unpad, out)
+
+
+
Evaluating ...: 100% 85/85 [23:11<00:00, 30.31s/it]
Evaluating ...: 100% 85/85 [23:11<00:00, 16.37s/it]
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/training/common_utils.py:51: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  device_metrics = jax.tree_map(lambda x: x[0], device_metrics)
+/home/sanchitgandhi/hf/lib/python3.8/site-packages/flax/training/common_utils.py:45: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(stack_args, *forest)
+run_flax_speech_recognition_seq2seq.py:1392: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  eval_metrics = jax.tree_map(jnp.mean, eval_metrics)
+run_flax_speech_recognition_seq2seq.py:336: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  return jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, t)
+
+
                                                        

                                                             

+
Training...:  28% 1213/4393 [1:57:39<5:03:02,  5.72s/it]
Epoch ... (1/12):  17% 2/12 [13:27:12<57:16:35, 20619.59s/it]run_flax_speech_recognition_seq2seq.py:1425: FutureWarning: jax.tree_map is deprecated, and will be removed in a future release. Use jax.tree_util.tree_map instead.
+  params = jax.device_get(jax.tree_map(lambda x: x[0], state.params))
+Configuration saved in /home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/config.json
+tcmalloc: large alloc 1226489856 bytes == 0x35593c000 @  0x7f7cba873680 0x7f7cba893bdd 0x7f7b690721ff 0x7f7b6908142c 0x7f7b6908241d 0x7f7b6908241d 0x7f7b6908241d 0x7f7b6908241d 0x7f7b6908241d 0x7f7b6908241d 0x7f7b6908241d 0x7f7b6907c164 0x7f7b6907c91e 0x505166 0x56bbfa 0x569dba 0x5f6eb3 0x56cc1f 0x569dba 0x5f6eb3 0x56cc1f 0x5f6cd6 0x56bacd 0x569dba 0x50bca0 0x56cc1f 0x569dba 0x5f6eb3 0x56bacd 0x569dba 0x5f6eb3
+tcmalloc: large alloc 2586787840 bytes == 0x3b7a32000 @  0x7f7cba873680 0x7f7cba893bdd 0x7f7b690721ff 0x7f7b6908142c 0x7f7b6908241d 0x7f7b6908241d 0x7f7b6908241d 0x7f7b6908241d 0x7f7b6908241d 0x7f7b6908241d 0x7f7b6908241d 0x7f7b6907c164 0x7f7b6907c91e 0x505166 0x56bbfa 0x569dba 0x5f6eb3 0x56cc1f 0x569dba 0x5f6eb3 0x56cc1f 0x5f6cd6 0x56bacd 0x569dba 0x50bca0 0x56cc1f 0x569dba 0x5f6eb3 0x56bacd 0x569dba 0x5f6eb3
+tcmalloc: large alloc 2353618944 bytes == 0x452526000 @  0x7f7cba873680 0x7f7cba894824 0x5fb391 0x7f7b6907c209 0x7f7b6907c91e 0x505166 0x56bbfa 0x569dba 0x5f6eb3 0x56cc1f 0x569dba 0x5f6eb3 0x56cc1f 0x5f6cd6 0x56bacd 0x569dba 0x50bca0 0x56cc1f 0x569dba 0x5f6eb3 0x56bacd 0x569dba 0x5f6eb3 0x56bacd 0x569dba 0x6902a7 0x67f951 0x67f9cf 0x67fa71 0x681b97 0x6b9d32
+Model weights saved in /home/sanchitgandhi/flax-wav2vec2-2-bart-large-ls-960h-black-box/flax_model.msgpack
+tokenizer config file saved in ./tokenizer_config.json
+Special tokens file saved in ./special_tokens_map.json
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)