import t5.models.mesh_transformer
import t5.data.sentencepiece_vocabulary
import mesh_tensorflow.optimize
import mesh_tensorflow.transformer.dataset
import mesh_tensorflow.transformer.learning_rate_schedules
import mesh_tensorflow.transformer.t2t_vocabulary
import mesh_tensorflow.transformer.transformer_layers
import mesh_tensorflow.transformer.utils

# Macros:
# ==============================================================================
d_ff = 2048
d_kv = 64
d_model = 512
dropout_rate = 0.1
inputs_length = 512
mean_noise_span_length = 3.0
MIXTURE_NAME = 'all_mix'
noise_density = 0.15
num_heads = 8
num_layers = 6
targets_length = 512
init_checkpoint = "gs://t5-data/pretrained_models/small/model.ckpt-1000000"
tokens_per_batch = 1048576

# Parameters for AdafactorOptimizer:
# ==============================================================================
AdafactorOptimizer.beta1 = 0.0
AdafactorOptimizer.clipping_threshold = 1.0
AdafactorOptimizer.decay_rate = None
AdafactorOptimizer.epsilon1 = 1e-30
AdafactorOptimizer.epsilon2 = 0.001
AdafactorOptimizer.factored = True
AdafactorOptimizer.min_dim_size_to_factor = 128
AdafactorOptimizer.multiply_by_parameter_scale = True

# Parameters for Bitransformer:
# ==============================================================================
Bitransformer.shared_embedding = True

# Parameters for denoise:
# ==============================================================================
denoise.inputs_fn = @preprocessors.noise_span_to_unique_sentinel
denoise.noise_density = %noise_density
denoise.noise_mask_fn = @preprocessors.random_spans_noise_mask
denoise.targets_fn = @preprocessors.nonnoise_span_to_unique_sentinel

# Parameters for decoder/DenseReluDense:
# ==============================================================================
decoder/DenseReluDense.dropout_rate = %dropout_rate
decoder/DenseReluDense.hidden_size = %d_ff

# Parameters for encoder/DenseReluDense:
# ==============================================================================
encoder/DenseReluDense.dropout_rate = %dropout_rate
encoder/DenseReluDense.hidden_size = %d_ff

# Parameters for decoder/EncDecAttention:
# ==============================================================================
# None.

# Parameters for get_sentencepiece_model_path:
# ==============================================================================
get_sentencepiece_model_path.mixture_or_task_name = %MIXTURE_NAME

# Parameters for get_variable_dtype:
# ==============================================================================
get_variable_dtype.activation_dtype = 'bfloat16'

# Parameters for decoder/LayerStack:
# ==============================================================================
decoder/LayerStack.dropout_rate = %dropout_rate
decoder/LayerStack.norm_epsilon = 1e-06

# Parameters for encoder/LayerStack:
# ==============================================================================
encoder/LayerStack.dropout_rate = %dropout_rate
encoder/LayerStack.norm_epsilon = 1e-06

# Parameters for learning_rate_schedule_noam:
# ==============================================================================
learning_rate_schedule_noam.linear_decay_fraction = 0.1
learning_rate_schedule_noam.multiplier = 1.0
learning_rate_schedule_noam.offset = 0
learning_rate_schedule_noam.warmup_steps = 10000

# Parameters for make_bitransformer:
# ==============================================================================
make_bitransformer.decoder_name = 'decoder'
make_bitransformer.encoder_name = 'encoder'

# Parameters for decoder/make_layer_stack:
# ==============================================================================
decoder/make_layer_stack.block_scope = True
decoder/make_layer_stack.layers = \
    [@mesh_tensorflow.transformer.transformer_layers.SelfAttention,
     @mesh_tensorflow.transformer.transformer_layers.EncDecAttention,
     @mesh_tensorflow.transformer.transformer_layers.DenseReluDense]
decoder/make_layer_stack.num_layers = %num_layers

# Parameters for encoder/make_layer_stack:
# ==============================================================================
encoder/make_layer_stack.block_scope = True
encoder/make_layer_stack.layers = \
    [@mesh_tensorflow.transformer.transformer_layers.SelfAttention,
     @mesh_tensorflow.transformer.transformer_layers.DenseReluDense]
encoder/make_layer_stack.num_layers = %num_layers

# Parameters for mesh_train_dataset_fn:
# ==============================================================================
mesh_train_dataset_fn.mixture_or_task_name = %MIXTURE_NAME


# Parameters for noise_span_to_unique_sentinel:
# ==============================================================================
# None.

# Parameters for nonnoise_span_to_unique_sentinel:
# ==============================================================================
# None.

# Parameters for pack_dataset:
# ==============================================================================


# Parameters for pack_or_pad:
# ==============================================================================
# None.

# Parameters for random_spans_helper:
# ==============================================================================
random_spans_helper.extra_tokens_per_span_inputs = 1
random_spans_helper.extra_tokens_per_span_targets = 1
random_spans_helper.inputs_length = %inputs_length
random_spans_helper.mean_noise_span_length = %mean_noise_span_length
random_spans_helper.noise_density = %noise_density

# Parameters for targets_length/random_spans_helper:
# ==============================================================================
targets_length/random_spans_helper.extra_tokens_per_span_inputs = 1
targets_length/random_spans_helper.extra_tokens_per_span_targets = 1
targets_length/random_spans_helper.inputs_length = %inputs_length
targets_length/random_spans_helper.mean_noise_span_length = %mean_noise_span_length
targets_length/random_spans_helper.noise_density = %noise_density

# Parameters for random_spans_noise_mask:
# ==============================================================================
random_spans_noise_mask.mean_noise_span_length = %mean_noise_span_length

# Parameters for targets_length/random_spans_targets_length:
# ==============================================================================
# None.

# Parameters for random_spans_tokens_length:
# ==============================================================================
# None.

# Parameters for rate_num_examples:
# ==============================================================================
rate_num_examples.maximum = 1000000.0
rate_num_examples.scale = 1.0
rate_num_examples.temperature = 1.0

# Parameters for rate_unsupervised:
# ==============================================================================
rate_unsupervised.value = 710000.0

# Parameters for reduce_concat_tokens:
# ==============================================================================
reduce_concat_tokens.batch_size = 128
reduce_concat_tokens.feature_key = 'targets'

# Parameters for run:
# ==============================================================================
run.autostack = True
run.batch_size = ('tokens_per_batch', %tokens_per_batch)
run.dataset_split = 'train'
run.ensemble_inputs = None
run.eval_checkpoint_step = None
run.eval_dataset_fn = None
run.eval_summary_dir = None
run.export_path = ''
run.iterations_per_loop = 100
run.keep_checkpoint_max = None
run.layout_rules = \
    'ensemble:ensemble,batch:batch,d_ff:model,heads:model,vocab:model,experts:batch'
run.learning_rate_schedule = @learning_rate_schedules.learning_rate_schedule_noam
run.mesh_shape = @mesh_tensorflow.transformer.utils.tpu_mesh_shape()
run.mode = 'train' 
run.init_checkpoint = %init_checkpoint
run.model_type = 'bitransformer'
run.optimizer = @optimize.AdafactorOptimizer
run.perplexity_eval_steps = 10
run.predict_fn = None
run.save_checkpoints_steps = 2400
run.sequence_length = {'inputs': %inputs_length, 'targets': %targets_length}
run.train_dataset_fn = \
    @t5.models.mesh_transformer.mesh_train_dataset_fn
run.train_steps = 1000000000
run.variable_filter = None
run.vocabulary = \
    @t5.data.sentencepiece_vocabulary.SentencePieceVocabulary()

# Parameters for select_random_chunk:
# ==============================================================================
select_random_chunk.feature_key = 'targets'
select_random_chunk.max_length = 65536

# Parameters for decoder/SelfAttention:
# ==============================================================================
decoder/SelfAttention.attention_kwargs = None
decoder/SelfAttention.dropout_rate = %dropout_rate
decoder/SelfAttention.key_value_size = %d_kv
decoder/SelfAttention.num_heads = %num_heads
decoder/SelfAttention.num_memory_heads = 0
decoder/SelfAttention.relative_attention_num_buckets = 32
decoder/SelfAttention.relative_attention_type = 'bias_shared'
decoder/SelfAttention.shared_kv = False

# Parameters for encoder/SelfAttention:
# ==============================================================================
encoder/SelfAttention.attention_kwargs = None
encoder/SelfAttention.dropout_rate = %dropout_rate
encoder/SelfAttention.key_value_size = %d_kv
encoder/SelfAttention.num_heads = %num_heads
encoder/SelfAttention.num_memory_heads = 0
encoder/SelfAttention.relative_attention_num_buckets = 32
encoder/SelfAttention.relative_attention_type = 'bias_shared'
encoder/SelfAttention.shared_kv = False

# Parameters for SentencePieceVocabulary:
# ==============================================================================
SentencePieceVocabulary.extra_ids = 100
SentencePieceVocabulary.sentencepiece_model_file = \
    @t5.models.mesh_transformer.get_sentencepiece_model_path()

# Parameters for serialize_num_microbatches:
# ==============================================================================
serialize_num_microbatches.tokens_per_microbatch_per_replica = 8192

# Parameters for split_tokens:
# ==============================================================================
split_tokens.feature_key = 'targets'
split_tokens.max_tokens_per_segment = @preprocessors.random_spans_tokens_length()
split_tokens.min_tokens_per_segment = None

# Parameters for tpu_estimator_model_fn:
# ==============================================================================
tpu_estimator_model_fn.init_checkpoint = %init_checkpoint
tpu_estimator_model_fn.outer_batch_size = 1
tpu_estimator_model_fn.tpu_summaries = False

# Parameters for tpu_mesh_shape:
# ==============================================================================
tpu_mesh_shape.ensemble_parallelism = None
tpu_mesh_shape.model_parallelism = 1
tpu_mesh_shape.tpu_topology = '8x8'

# Parameters for decoder/Unitransformer:
# ==============================================================================
decoder/Unitransformer.d_model = %d_model
decoder/Unitransformer.ensemble = None
decoder/Unitransformer.input_full_attention = False
decoder/Unitransformer.label_smoothing = 0.0
decoder/Unitransformer.loss_denominator = None
decoder/Unitransformer.loss_fn = None
decoder/Unitransformer.loss_on_targets_only = False
decoder/Unitransformer.max_length = 512
decoder/Unitransformer.positional_embedding = False
decoder/Unitransformer.shared_embedding_and_softmax_weights = True
decoder/Unitransformer.vocab_divisor = 128
decoder/Unitransformer.z_loss = 0.0001
decoder/Unitransformer.loss_denominator = 233472

# Parameters for encoder/Unitransformer:
# ==============================================================================
encoder/Unitransformer.d_model = %d_model
encoder/Unitransformer.ensemble = None
encoder/Unitransformer.input_full_attention = False
encoder/Unitransformer.label_smoothing = 0.0
encoder/Unitransformer.loss_denominator = None
encoder/Unitransformer.loss_fn = None
encoder/Unitransformer.loss_on_targets_only = False
encoder/Unitransformer.max_length = 512
encoder/Unitransformer.positional_embedding = False
encoder/Unitransformer.shared_embedding_and_softmax_weights = True
encoder/Unitransformer.vocab_divisor = 128
encoder/Unitransformer.z_loss = 0.0001

# Parameters for unsupervised:
# ==============================================================================
unsupervised.preprocessors = \
    [@preprocessors.select_random_chunk,
     @preprocessors.reduce_concat_tokens,
     @preprocessors.split_tokens,
     @preprocessors.denoise]