Uplaod files

.ipynb_checkpoints/README-checkpoint.md

@@ -1,13 +0,0 @@
----
-title: Mustango
-emoji: 🐢
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 4.3.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

.ipynb_checkpoints/app-checkpoint.py

@@ -1,263 +0,0 @@
-import gradio as gr
-import json
-import torch
-import wavio
-from tqdm import tqdm
-from huggingface_hub import snapshot_download
-
-from audioldm.audio.stft import TacotronSTFT
-from audioldm.variational_autoencoder import AutoencoderKL
-
-from transformers import AutoTokenizer, T5ForConditionalGeneration
-from modelling_deberta_v2 import DebertaV2ForTokenClassificationRegression
-
-import sys
-sys.path.insert(0, "diffusers/src")
-
-from diffusers import DDPMScheduler
-from models import MusicAudioDiffusion
-
-from gradio import Markdown
-
-class MusicFeaturePredictor:
-    def __init__(self, path, device="cuda:0", cache_dir=None, local_files_only=False):
-        self.beats_tokenizer = AutoTokenizer.from_pretrained(
-            "microsoft/deberta-v3-large",
-            cache_dir=cache_dir,
-            local_files_only=local_files_only,
-        )
-        self.beats_model = DebertaV2ForTokenClassificationRegression.from_pretrained(
-            "microsoft/deberta-v3-large",
-            cache_dir=cache_dir,
-            local_files_only=local_files_only,
-        )
-        self.beats_model.eval()
-        self.beats_model.to(device)
-
-        beats_ckpt = f"{path}/beats/microsoft-deberta-v3-large.pt"
-        beats_weight = torch.load(beats_ckpt, map_location="cpu")
-        self.beats_model.load_state_dict(beats_weight)
-
-        self.chords_tokenizer = AutoTokenizer.from_pretrained(
-            "google/flan-t5-large",
-            cache_dir=cache_dir,
-            local_files_only=local_files_only,
-        )
-        self.chords_model = T5ForConditionalGeneration.from_pretrained(
-            "google/flan-t5-large",
-            cache_dir=cache_dir,
-            local_files_only=local_files_only,
-        )
-        self.chords_model.eval()
-        self.chords_model.to(device)
-
-        chords_ckpt = f"{path}/chords/flan-t5-large.bin"
-        chords_weight = torch.load(chords_ckpt, map_location="cpu")
-        self.chords_model.load_state_dict(chords_weight)
-
-    def generate_beats(self, prompt):
-        tokenized = self.beats_tokenizer(
-            prompt, max_length=512, padding=True, truncation=True, return_tensors="pt"
-        )
-        tokenized = {k: v.to(self.beats_model.device) for k, v in tokenized.items()}
-
-        with torch.no_grad():
-            out = self.beats_model(**tokenized)
-
-        max_beat = (
-            1 + torch.argmax(out["logits"][:, 0, :], -1).detach().cpu().numpy()
-        ).tolist()[0]
-        intervals = (
-            out["values"][:, :, 0]
-            .detach()
-            .cpu()
-            .numpy()
-            .astype("float32")
-            .round(4)
-            .tolist()
-        )
-
-        intervals = np.cumsum(intervals)
-        predicted_beats_times = []
-        for t in intervals:
-            if t < 10:
-                predicted_beats_times.append(round(t, 2))
-            else:
-                break
-        predicted_beats_times = list(np.array(predicted_beats_times)[:50])
-
-        if len(predicted_beats_times) == 0:
-            predicted_beats = [[], []]
-        else:
-            beat_counts = []
-            for i in range(len(predicted_beats_times)):
-                beat_counts.append(float(1.0 + np.mod(i, max_beat)))
-            predicted_beats = [[predicted_beats_times, beat_counts]]
-
-        return max_beat, predicted_beats_times, predicted_beats
-
-    def generate(self, prompt):
-        max_beat, predicted_beats_times, predicted_beats = self.generate_beats(prompt)
-
-        chords_prompt = "Caption: {} \\n Timestamps: {} \\n Max Beat: {}".format(
-            prompt,
-            " , ".join([str(round(t, 2)) for t in predicted_beats_times]),
-            max_beat,
-        )
-
-        tokenized = self.chords_tokenizer(
-            chords_prompt,
-            max_length=512,
-            padding=True,
-            truncation=True,
-            return_tensors="pt",
-        )
-        tokenized = {k: v.to(self.chords_model.device) for k, v in tokenized.items()}
-
-        generated_chords = self.chords_model.generate(
-            input_ids=tokenized["input_ids"],
-            attention_mask=tokenized["attention_mask"],
-            min_length=8,
-            max_length=128,
-            num_beams=5,
-            early_stopping=True,
-            num_return_sequences=1,
-        )
-
-        generated_chords = self.chords_tokenizer.decode(
-            generated_chords[0],
-            skip_special_tokens=True,
-            clean_up_tokenization_spaces=True,
-        ).split(" n ")
-
-        predicted_chords, predicted_chords_times = [], []
-        for item in generated_chords:
-            c, ct = item.split(" at ")
-            predicted_chords.append(c)
-            predicted_chords_times.append(float(ct))
-
-        return predicted_beats, predicted_chords, predicted_chords_times
-
-
-class Mustango:
-    def __init__(
-        self,
-        name="declare-lab/mustango",
-        device="cuda:0",
-        cache_dir=None,
-        local_files_only=False,
-    ):
-        path = snapshot_download(repo_id=name, cache_dir=cache_dir)
-
-        self.music_model = MusicFeaturePredictor(
-            path, device, cache_dir=cache_dir, local_files_only=local_files_only
-        )
-
-        vae_config = json.load(open(f"{path}/configs/vae_config.json"))
-        stft_config = json.load(open(f"{path}/configs/stft_config.json"))
-        main_config = json.load(open(f"{path}/configs/main_config.json"))
-
-        self.vae = AutoencoderKL(**vae_config).to(device)
-        self.stft = TacotronSTFT(**stft_config).to(device)
-        self.model = MusicAudioDiffusion(
-            main_config["text_encoder_name"],
-            main_config["scheduler_name"],
-            unet_model_config_path=f"{path}/configs/music_diffusion_model_config.json",
-        ).to(device)
-
-        vae_weights = torch.load(
-            f"{path}/vae/pytorch_model_vae.bin", map_location=device
-        )
-        stft_weights = torch.load(
-            f"{path}/stft/pytorch_model_stft.bin", map_location=device
-        )
-        main_weights = torch.load(
-            f"{path}/ldm/pytorch_model_ldm.bin", map_location=device
-        )
-
-        self.vae.load_state_dict(vae_weights)
-        self.stft.load_state_dict(stft_weights)
-        self.model.load_state_dict(main_weights)
-
-        print("Successfully loaded checkpoint from:", name)
-
-        self.vae.eval()
-        self.stft.eval()
-        self.model.eval()
-
-        self.scheduler = DDPMScheduler.from_pretrained(
-            main_config["scheduler_name"], subfolder="scheduler"
-        )
-
-    def generate(self, prompt, steps=100, guidance=3, samples=1, disable_progress=True):
-        """Genrate music for a single prompt string."""
-
-        with torch.no_grad():
-            beats, chords, chords_times = self.music_model.generate(prompt)
-            latents = self.model.inference(
-                [prompt],
-                beats,
-                [chords],
-                [chords_times],
-                self.scheduler,
-                steps,
-                guidance,
-                samples,
-                disable_progress,
-            )
-            mel = self.vae.decode_first_stage(latents)
-            wave = self.vae.decode_to_waveform(mel)
-
-        return wave[0]
-
-
-# Initialize Mustango
-if torch.cuda.is_available():
-    mustango = Mustango()
-else:
-    mustango = Mustango(device="cpu")
-
-def gradio_generate(prompt, steps, guidance):
-    output_wave = mustango.generate(prompt, steps, guidance)
-    # output_filename = f"{prompt.replace(' ', '_')}_{steps}_{guidance}"[:250] + ".wav"
-    output_filename = "temp.wav"
-    wavio.write(output_filename, output_wave, rate=16000, sampwidth=2)
-    
-    return output_filename
-
-# description_text = """
-# <p><a href="https://huggingface.co/spaces/declare-lab/mustango/blob/main/app.py?duplicate=true"> <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> For faster inference without waiting in queue, you may duplicate the space and upgrade to a GPU in the settings. <br/><br/>
-# Generate music using Mustango by providing a text prompt.
-# <br/><br/> Meet Mustango, an exciting addition to the vibrant landscape of Multimodal Large Language Models \
-# designed for controlled music generation. Mustango leverages Latent Diffusion Model (LDM), Flan-T5, and \
-# musical features to do the magic! \
-# <p/>
-# """
-description_text = ""
-# Gradio input and output components
-input_text = gr.inputs.Textbox(lines=2, label="Prompt")
-output_audio = gr.outputs.Audio(label="Generated Music", type="filepath")
-denoising_steps = gr.Slider(minimum=100, maximum=200, value=100, step=1, label="Steps", interactive=True)
-guidance_scale = gr.Slider(minimum=1, maximum=10, value=3, step=0.1, label="Guidance Scale", interactive=True)
-
-# Gradio interface
-gr_interface = gr.Interface(
-    fn=gradio_generate,
-    inputs=[input_text, denoising_steps, guidance_scale],
-    outputs=[output_audio],
-    title="Mustango: Toward Controllable Text-to-Music Generation",
-    description=description_text,
-    allow_flagging=False,
-    examples=[
-        ["This techno song features a synth lead playing the main melody. This is accompanied by programmed percussion playing a simple kick focused beat. The hi-hat is accented in an open position on the 3-and count of every bar. The synth plays the bass part with a voicing that sounds like a cello. This techno song can be played in a club. The chord sequence is Gm, A7, Eb, Bb, C, F, Gm. The beat counts to 2. The tempo of this song is 128.0 beats per minute. The key of this song is G minor."],
-        ["This is a new age piece. There is a flute playing the main melody with a lot of staccato notes. The rhythmic background consists of a medium tempo electronic drum beat with percussive elements all over the spectrum. There is a playful atmosphere to the piece. This piece can be used in the soundtrack of a children's TV show or an advertisement jingle."],
-        ["The song is an instrumental. The song is in medium tempo with a classical guitar playing a lilting melody in accompaniment style. The song is emotional and romantic. The song is a romantic instrumental song. The chord sequence is Gm, F6, Ebm. The time signature is 4/4. This song is in Adagio. The key of this song is G minor."],
-        ["This folk song features a female voice singing the main melody. This is accompanied by a tabla playing the percussion. A guitar strums chords. For most parts of the song, only one chord is played. At the last bar, a different chord is played. This song has minimal instruments. This song has a story-telling mood. This song can be played in a village scene in an Indian movie. The chord sequence is Bbm, Ab. The beat is 3. The tempo of this song is Allegro. The key of this song is Bb minor."],
-        ["This is a live performance of a classical music piece. There is an orchestra performing the piece with a violin lead playing the main melody. The atmosphere is sentimental and heart-touching. This piece could be playing in the background at a classy restaurant. The chord progression in this song is Am7, Gm, Dm, A7, Dm. The beat is 3. This song is in Largo. The key of this song is D minor."],
-        ["This is a techno piece with drums and beats and a leading melody. A synth plays chords. The music kicks off with a powerful and relentless drumbeat. Over the pounding beats, a leading melody emerges. In the middle of the song, a flock of seagulls flies over the venue and make loud bird sounds. It has strong danceability and can be played in a club. The tempo is 120 bpm. The chords played by the synth are Am, Cm, Dm, Gm."],
-    ],
-    cache_examples=False,
-)
-
-# Launch Gradio app
-gr_interface.launch()

.ipynb_checkpoints/modelling_deberta_v2-checkpoint.py

@@ -1,1750 +0,0 @@
-# coding=utf-8
-# Copyright 2020 Microsoft and the Hugging Face Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" PyTorch DeBERTa-v2 model."""
-
-from collections.abc import Sequence
-from typing import Optional, Tuple, Union
-
-import torch
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
-
-from transformers.activations import ACT2FN
-from transformers.modeling_outputs import (
-    ModelOutput,
-    BaseModelOutput,
-    MaskedLMOutput,
-    MultipleChoiceModelOutput,
-    QuestionAnsweringModelOutput,
-    SequenceClassifierOutput,
-    TokenClassifierOutput,
-)
-from transformers.modeling_utils import PreTrainedModel
-from transformers.pytorch_utils import softmax_backward_data
-from transformers.utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
-from transformers.models.deberta_v2.configuration_deberta_v2 import DebertaV2Config
-
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "DebertaV2Config"
-_CHECKPOINT_FOR_DOC = "microsoft/deberta-v2-xlarge"
-_QA_TARGET_START_INDEX = 2
-_QA_TARGET_END_INDEX = 9
-
-DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "microsoft/deberta-v2-xlarge",
-    "microsoft/deberta-v2-xxlarge",
-    "microsoft/deberta-v2-xlarge-mnli",
-    "microsoft/deberta-v2-xxlarge-mnli",
-]
-
-
-# Copied from transformers.models.deberta.modeling_deberta.ContextPooler
-class ContextPooler(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.pooler_hidden_size, config.pooler_hidden_size)
-        self.dropout = StableDropout(config.pooler_dropout)
-        self.config = config
-
-    def forward(self, hidden_states):
-        # We "pool" the model by simply taking the hidden state corresponding
-        # to the first token.
-
-        context_token = hidden_states[:, 0]
-        context_token = self.dropout(context_token)
-        pooled_output = self.dense(context_token)
-        pooled_output = ACT2FN[self.config.pooler_hidden_act](pooled_output)
-        return pooled_output
-
-    @property
-    def output_dim(self):
-        return self.config.hidden_size
-
-
-# Copied from transformers.models.deberta.modeling_deberta.XSoftmax with deberta->deberta_v2
-class XSoftmax(torch.autograd.Function):
-    """
-    Masked Softmax which is optimized for saving memory
-
-    Args:
-        input (`torch.tensor`): The input tensor that will apply softmax.
-        mask (`torch.IntTensor`):
-            The mask matrix where 0 indicate that element will be ignored in the softmax calculation.
-        dim (int): The dimension that will apply softmax
-
-    Example:
-
-    ```python
-    >>> import torch
-    >>> from transformers.models.deberta_v2.modeling_deberta_v2 import XSoftmax
-
-    >>> # Make a tensor
-    >>> x = torch.randn([4, 20, 100])
-
-    >>> # Create a mask
-    >>> mask = (x > 0).int()
-
-    >>> # Specify the dimension to apply softmax
-    >>> dim = -1
-
-    >>> y = XSoftmax.apply(x, mask, dim)
-    ```"""
-
-    @staticmethod
-    def forward(self, input, mask, dim):
-        self.dim = dim
-        rmask = ~(mask.to(torch.bool))
-
-        output = input.masked_fill(rmask, torch.tensor(torch.finfo(input.dtype).min))
-        output = torch.softmax(output, self.dim)
-        output.masked_fill_(rmask, 0)
-        self.save_for_backward(output)
-        return output
-
-    @staticmethod
-    def backward(self, grad_output):
-        (output,) = self.saved_tensors
-        inputGrad = softmax_backward_data(self, grad_output, output, self.dim, output)
-        return inputGrad, None, None
-
-    @staticmethod
-    def symbolic(g, self, mask, dim):
-        import torch.onnx.symbolic_helper as sym_help
-        from torch.onnx.symbolic_opset9 import masked_fill, softmax
-
-        mask_cast_value = g.op("Cast", mask, to_i=sym_help.cast_pytorch_to_onnx["Long"])
-        r_mask = g.op(
-            "Cast",
-            g.op("Sub", g.op("Constant", value_t=torch.tensor(1, dtype=torch.int64)), mask_cast_value),
-            to_i=sym_help.cast_pytorch_to_onnx["Bool"],
-        )
-        output = masked_fill(
-            g, self, r_mask, g.op("Constant", value_t=torch.tensor(torch.finfo(self.type().dtype()).min))
-        )
-        output = softmax(g, output, dim)
-        return masked_fill(g, output, r_mask, g.op("Constant", value_t=torch.tensor(0, dtype=torch.bool)))
-
-
-# Copied from transformers.models.deberta.modeling_deberta.DropoutContext
-class DropoutContext(object):
-    def __init__(self):
-        self.dropout = 0
-        self.mask = None
-        self.scale = 1
-        self.reuse_mask = True
-
-
-# Copied from transformers.models.deberta.modeling_deberta.get_mask
-def get_mask(input, local_context):
-    if not isinstance(local_context, DropoutContext):
-        dropout = local_context
-        mask = None
-    else:
-        dropout = local_context.dropout
-        dropout *= local_context.scale
-        mask = local_context.mask if local_context.reuse_mask else None
-
-    if dropout > 0 and mask is None:
-        mask = (1 - torch.empty_like(input).bernoulli_(1 - dropout)).to(torch.bool)
-
-    if isinstance(local_context, DropoutContext):
-        if local_context.mask is None:
-            local_context.mask = mask
-
-    return mask, dropout
-
-
-# Copied from transformers.models.deberta.modeling_deberta.XDropout
-class XDropout(torch.autograd.Function):
-    """Optimized dropout function to save computation and memory by using mask operation instead of multiplication."""
-
-    @staticmethod
-    def forward(ctx, input, local_ctx):
-        mask, dropout = get_mask(input, local_ctx)
-        ctx.scale = 1.0 / (1 - dropout)
-        if dropout > 0:
-            ctx.save_for_backward(mask)
-            return input.masked_fill(mask, 0) * ctx.scale
-        else:
-            return input
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        if ctx.scale > 1:
-            (mask,) = ctx.saved_tensors
-            return grad_output.masked_fill(mask, 0) * ctx.scale, None
-        else:
-            return grad_output, None
-
-    @staticmethod
-    def symbolic(g: torch._C.Graph, input: torch._C.Value, local_ctx: Union[float, DropoutContext]) -> torch._C.Value:
-        from torch.onnx import symbolic_opset12
-
-        dropout_p = local_ctx
-        if isinstance(local_ctx, DropoutContext):
-            dropout_p = local_ctx.dropout
-        # StableDropout only calls this function when training.
-        train = True
-        # TODO: We should check if the opset_version being used to export
-        # is > 12 here, but there's no good way to do that. As-is, if the
-        # opset_version < 12, export will fail with a CheckerError.
-        # Once https://github.com/pytorch/pytorch/issues/78391 is fixed, do something like:
-        # if opset_version < 12:
-        #   return torch.onnx.symbolic_opset9.dropout(g, input, dropout_p, train)
-        return symbolic_opset12.dropout(g, input, dropout_p, train)
-
-
-# Copied from transformers.models.deberta.modeling_deberta.StableDropout
-class StableDropout(nn.Module):
-    """
-    Optimized dropout module for stabilizing the training
-
-    Args:
-        drop_prob (float): the dropout probabilities
-    """
-
-    def __init__(self, drop_prob):
-        super().__init__()
-        self.drop_prob = drop_prob
-        self.count = 0
-        self.context_stack = None
-
-    def forward(self, x):
-        """
-        Call the module
-
-        Args:
-            x (`torch.tensor`): The input tensor to apply dropout
-        """
-        if self.training and self.drop_prob > 0:
-            return XDropout.apply(x, self.get_context())
-        return x
-
-    def clear_context(self):
-        self.count = 0
-        self.context_stack = None
-
-    def init_context(self, reuse_mask=True, scale=1):
-        if self.context_stack is None:
-            self.context_stack = []
-        self.count = 0
-        for c in self.context_stack:
-            c.reuse_mask = reuse_mask
-            c.scale = scale
-
-    def get_context(self):
-        if self.context_stack is not None:
-            if self.count >= len(self.context_stack):
-                self.context_stack.append(DropoutContext())
-            ctx = self.context_stack[self.count]
-            ctx.dropout = self.drop_prob
-            self.count += 1
-            return ctx
-        else:
-            return self.drop_prob
-
-
-# Copied from transformers.models.deberta.modeling_deberta.DebertaSelfOutput with DebertaLayerNorm->LayerNorm
-class DebertaV2SelfOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
-        self.dropout = StableDropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-# Copied from transformers.models.deberta.modeling_deberta.DebertaAttention with Deberta->DebertaV2
-class DebertaV2Attention(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.self = DisentangledSelfAttention(config)
-        self.output = DebertaV2SelfOutput(config)
-        self.config = config
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask,
-        output_attentions=False,
-        query_states=None,
-        relative_pos=None,
-        rel_embeddings=None,
-    ):
-        self_output = self.self(
-            hidden_states,
-            attention_mask,
-            output_attentions,
-            query_states=query_states,
-            relative_pos=relative_pos,
-            rel_embeddings=rel_embeddings,
-        )
-        if output_attentions:
-            self_output, att_matrix = self_output
-        if query_states is None:
-            query_states = hidden_states
-        attention_output = self.output(self_output, query_states)
-
-        if output_attentions:
-            return (attention_output, att_matrix)
-        else:
-            return attention_output
-
-
-# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->DebertaV2
-class DebertaV2Intermediate(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
-        if isinstance(config.hidden_act, str):
-            self.intermediate_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.intermediate_act_fn = config.hidden_act
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.intermediate_act_fn(hidden_states)
-        return hidden_states
-
-
-# Copied from transformers.models.deberta.modeling_deberta.DebertaOutput with DebertaLayerNorm->LayerNorm
-class DebertaV2Output(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
-        self.dropout = StableDropout(config.hidden_dropout_prob)
-        self.config = config
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-# Copied from transformers.models.deberta.modeling_deberta.DebertaLayer with Deberta->DebertaV2
-class DebertaV2Layer(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.attention = DebertaV2Attention(config)
-        self.intermediate = DebertaV2Intermediate(config)
-        self.output = DebertaV2Output(config)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask,
-        query_states=None,
-        relative_pos=None,
-        rel_embeddings=None,
-        output_attentions=False,
-    ):
-        attention_output = self.attention(
-            hidden_states,
-            attention_mask,
-            output_attentions=output_attentions,
-            query_states=query_states,
-            relative_pos=relative_pos,
-            rel_embeddings=rel_embeddings,
-        )
-        if output_attentions:
-            attention_output, att_matrix = attention_output
-        intermediate_output = self.intermediate(attention_output)
-        layer_output = self.output(intermediate_output, attention_output)
-        if output_attentions:
-            return (layer_output, att_matrix)
-        else:
-            return layer_output
-
-
-class ConvLayer(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        kernel_size = getattr(config, "conv_kernel_size", 3)
-        groups = getattr(config, "conv_groups", 1)
-        self.conv_act = getattr(config, "conv_act", "tanh")
-        self.conv = nn.Conv1d(
-            config.hidden_size, config.hidden_size, kernel_size, padding=(kernel_size - 1) // 2, groups=groups
-        )
-        self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
-        self.dropout = StableDropout(config.hidden_dropout_prob)
-        self.config = config
-
-    def forward(self, hidden_states, residual_states, input_mask):
-        out = self.conv(hidden_states.permute(0, 2, 1).contiguous()).permute(0, 2, 1).contiguous()
-        rmask = (1 - input_mask).bool()
-        out.masked_fill_(rmask.unsqueeze(-1).expand(out.size()), 0)
-        out = ACT2FN[self.conv_act](self.dropout(out))
-
-        layer_norm_input = residual_states + out
-        output = self.LayerNorm(layer_norm_input).to(layer_norm_input)
-
-        if input_mask is None:
-            output_states = output
-        else:
-            if input_mask.dim() != layer_norm_input.dim():
-                if input_mask.dim() == 4:
-                    input_mask = input_mask.squeeze(1).squeeze(1)
-                input_mask = input_mask.unsqueeze(2)
-
-            input_mask = input_mask.to(output.dtype)
-            output_states = output * input_mask
-
-        return output_states
-
-
-class DebertaV2Encoder(nn.Module):
-    """Modified BertEncoder with relative position bias support"""
-
-    def __init__(self, config):
-        super().__init__()
-
-        self.layer = nn.ModuleList([DebertaV2Layer(config) for _ in range(config.num_hidden_layers)])
-        self.relative_attention = getattr(config, "relative_attention", False)
-
-        if self.relative_attention:
-            self.max_relative_positions = getattr(config, "max_relative_positions", -1)
-            if self.max_relative_positions < 1:
-                self.max_relative_positions = config.max_position_embeddings
-
-            self.position_buckets = getattr(config, "position_buckets", -1)
-            pos_ebd_size = self.max_relative_positions * 2
-
-            if self.position_buckets > 0:
-                pos_ebd_size = self.position_buckets * 2
-
-            self.rel_embeddings = nn.Embedding(pos_ebd_size, config.hidden_size)
-
-        self.norm_rel_ebd = [x.strip() for x in getattr(config, "norm_rel_ebd", "none").lower().split("|")]
-
-        if "layer_norm" in self.norm_rel_ebd:
-            self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=True)
-
-        self.conv = ConvLayer(config) if getattr(config, "conv_kernel_size", 0) > 0 else None
-        self.gradient_checkpointing = False
-
-    def get_rel_embedding(self):
-        rel_embeddings = self.rel_embeddings.weight if self.relative_attention else None
-        if rel_embeddings is not None and ("layer_norm" in self.norm_rel_ebd):
-            rel_embeddings = self.LayerNorm(rel_embeddings)
-        return rel_embeddings
-
-    def get_attention_mask(self, attention_mask):
-        if attention_mask.dim() <= 2:
-            extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
-            attention_mask = extended_attention_mask * extended_attention_mask.squeeze(-2).unsqueeze(-1)
-        elif attention_mask.dim() == 3:
-            attention_mask = attention_mask.unsqueeze(1)
-
-        return attention_mask
-
-    def get_rel_pos(self, hidden_states, query_states=None, relative_pos=None):
-        if self.relative_attention and relative_pos is None:
-            q = query_states.size(-2) if query_states is not None else hidden_states.size(-2)
-            relative_pos = build_relative_position(
-                q,
-                hidden_states.size(-2),
-                bucket_size=self.position_buckets,
-                max_position=self.max_relative_positions,
-                device=hidden_states.device,
-            )
-        return relative_pos
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask,
-        output_hidden_states=True,
-        output_attentions=False,
-        query_states=None,
-        relative_pos=None,
-        return_dict=True,
-    ):
-        if attention_mask.dim() <= 2:
-            input_mask = attention_mask
-        else:
-            input_mask = attention_mask.sum(-2) > 0
-        attention_mask = self.get_attention_mask(attention_mask)
-        relative_pos = self.get_rel_pos(hidden_states, query_states, relative_pos)
-
-        all_hidden_states = () if output_hidden_states else None
-        all_attentions = () if output_attentions else None
-
-        if isinstance(hidden_states, Sequence):
-            next_kv = hidden_states[0]
-        else:
-            next_kv = hidden_states
-        rel_embeddings = self.get_rel_embedding()
-        output_states = next_kv
-        for i, layer_module in enumerate(self.layer):
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (output_states,)
-
-            if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs, output_attentions)
-
-                    return custom_forward
-
-                output_states = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(layer_module),
-                    next_kv,
-                    attention_mask,
-                    query_states,
-                    relative_pos,
-                    rel_embeddings,
-                )
-            else:
-                output_states = layer_module(
-                    next_kv,
-                    attention_mask,
-                    query_states=query_states,
-                    relative_pos=relative_pos,
-                    rel_embeddings=rel_embeddings,
-                    output_attentions=output_attentions,
-                )
-
-            if output_attentions:
-                output_states, att_m = output_states
-
-            if i == 0 and self.conv is not None:
-                output_states = self.conv(hidden_states, output_states, input_mask)
-
-            if query_states is not None:
-                query_states = output_states
-                if isinstance(hidden_states, Sequence):
-                    next_kv = hidden_states[i + 1] if i + 1 < len(self.layer) else None
-            else:
-                next_kv = output_states
-
-            if output_attentions:
-                all_attentions = all_attentions + (att_m,)
-
-        if output_hidden_states:
-            all_hidden_states = all_hidden_states + (output_states,)
-
-        if not return_dict:
-            return tuple(v for v in [output_states, all_hidden_states, all_attentions] if v is not None)
-        return BaseModelOutput(
-            last_hidden_state=output_states, hidden_states=all_hidden_states, attentions=all_attentions
-        )
-
-
-def make_log_bucket_position(relative_pos, bucket_size, max_position):
-    sign = torch.sign(relative_pos)
-    mid = bucket_size // 2
-    abs_pos = torch.where(
-        (relative_pos < mid) & (relative_pos > -mid),
-        torch.tensor(mid - 1).type_as(relative_pos),
-        torch.abs(relative_pos),
-    )
-    log_pos = (
-        torch.ceil(torch.log(abs_pos / mid) / torch.log(torch.tensor((max_position - 1) / mid)) * (mid - 1)) + mid
-    )
-    bucket_pos = torch.where(abs_pos <= mid, relative_pos.type_as(log_pos), log_pos * sign)
-    return bucket_pos
-
-
-def build_relative_position(query_size, key_size, bucket_size=-1, max_position=-1, device=None):
-    """
-    Build relative position according to the query and key
-
-    We assume the absolute position of query \\(P_q\\) is range from (0, query_size) and the absolute position of key
-    \\(P_k\\) is range from (0, key_size), The relative positions from query to key is \\(R_{q \\rightarrow k} = P_q -
-    P_k\\)
-
-    Args:
-        query_size (int): the length of query
-        key_size (int): the length of key
-        bucket_size (int): the size of position bucket
-        max_position (int): the maximum allowed absolute position
-        device (`torch.device`): the device on which tensors will be created.
-
-    Return:
-        `torch.LongTensor`: A tensor with shape [1, query_size, key_size]
-    """
-
-    q_ids = torch.arange(0, query_size, device=device)
-    k_ids = torch.arange(0, key_size, device=device)
-    rel_pos_ids = q_ids[:, None] - k_ids[None, :]
-    if bucket_size > 0 and max_position > 0:
-        rel_pos_ids = make_log_bucket_position(rel_pos_ids, bucket_size, max_position)
-    rel_pos_ids = rel_pos_ids.to(torch.long)
-    rel_pos_ids = rel_pos_ids[:query_size, :]
-    rel_pos_ids = rel_pos_ids.unsqueeze(0)
-    return rel_pos_ids
-
-
-@torch.jit.script
-# Copied from transformers.models.deberta.modeling_deberta.c2p_dynamic_expand
-def c2p_dynamic_expand(c2p_pos, query_layer, relative_pos):
-    return c2p_pos.expand([query_layer.size(0), query_layer.size(1), query_layer.size(2), relative_pos.size(-1)])
-
-
-@torch.jit.script
-# Copied from transformers.models.deberta.modeling_deberta.p2c_dynamic_expand
-def p2c_dynamic_expand(c2p_pos, query_layer, key_layer):
-    return c2p_pos.expand([query_layer.size(0), query_layer.size(1), key_layer.size(-2), key_layer.size(-2)])
-
-
-@torch.jit.script
-# Copied from transformers.models.deberta.modeling_deberta.pos_dynamic_expand
-def pos_dynamic_expand(pos_index, p2c_att, key_layer):
-    return pos_index.expand(p2c_att.size()[:2] + (pos_index.size(-2), key_layer.size(-2)))
-
-
-class DisentangledSelfAttention(nn.Module):
-    """
-    Disentangled self-attention module
-
-    Parameters:
-        config (`DebertaV2Config`):
-            A model config class instance with the configuration to build a new model. The schema is similar to
-            *BertConfig*, for more details, please refer [`DebertaV2Config`]
-
-    """
-
-    def __init__(self, config):
-        super().__init__()
-        if config.hidden_size % config.num_attention_heads != 0:
-            raise ValueError(
-                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
-                f"heads ({config.num_attention_heads})"
-            )
-        self.num_attention_heads = config.num_attention_heads
-        _attention_head_size = config.hidden_size // config.num_attention_heads
-        self.attention_head_size = getattr(config, "attention_head_size", _attention_head_size)
-        self.all_head_size = self.num_attention_heads * self.attention_head_size
-        self.query_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)
-        self.key_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)
-        self.value_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)
-
-        self.share_att_key = getattr(config, "share_att_key", False)
-        self.pos_att_type = config.pos_att_type if config.pos_att_type is not None else []
-        self.relative_attention = getattr(config, "relative_attention", False)
-
-        if self.relative_attention:
-            self.position_buckets = getattr(config, "position_buckets", -1)
-            self.max_relative_positions = getattr(config, "max_relative_positions", -1)
-            if self.max_relative_positions < 1:
-                self.max_relative_positions = config.max_position_embeddings
-            self.pos_ebd_size = self.max_relative_positions
-            if self.position_buckets > 0:
-                self.pos_ebd_size = self.position_buckets
-
-            self.pos_dropout = StableDropout(config.hidden_dropout_prob)
-
-            if not self.share_att_key:
-                if "c2p" in self.pos_att_type:
-                    self.pos_key_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)
-                if "p2c" in self.pos_att_type:
-                    self.pos_query_proj = nn.Linear(config.hidden_size, self.all_head_size)
-
-        self.dropout = StableDropout(config.attention_probs_dropout_prob)
-
-    def transpose_for_scores(self, x, attention_heads):
-        new_x_shape = x.size()[:-1] + (attention_heads, -1)
-        x = x.view(new_x_shape)
-        return x.permute(0, 2, 1, 3).contiguous().view(-1, x.size(1), x.size(-1))
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask,
-        output_attentions=False,
-        query_states=None,
-        relative_pos=None,
-        rel_embeddings=None,
-    ):
-        """
-        Call the module
-
-        Args:
-            hidden_states (`torch.FloatTensor`):
-                Input states to the module usually the output from previous layer, it will be the Q,K and V in
-                *Attention(Q,K,V)*
-
-            attention_mask (`torch.BoolTensor`):
-                An attention mask matrix of shape [*B*, *N*, *N*] where *B* is the batch size, *N* is the maximum
-                sequence length in which element [i,j] = *1* means the *i* th token in the input can attend to the *j*
-                th token.
-
-            output_attentions (`bool`, optional):
-                Whether return the attention matrix.
-
-            query_states (`torch.FloatTensor`, optional):
-                The *Q* state in *Attention(Q,K,V)*.
-
-            relative_pos (`torch.LongTensor`):
-                The relative position encoding between the tokens in the sequence. It's of shape [*B*, *N*, *N*] with
-                values ranging in [*-max_relative_positions*, *max_relative_positions*].
-
-            rel_embeddings (`torch.FloatTensor`):
-                The embedding of relative distances. It's a tensor of shape [\\(2 \\times
-                \\text{max_relative_positions}\\), *hidden_size*].
-
-
-        """
-        if query_states is None:
-            query_states = hidden_states
-        query_layer = self.transpose_for_scores(self.query_proj(query_states), self.num_attention_heads)
-        key_layer = self.transpose_for_scores(self.key_proj(hidden_states), self.num_attention_heads)
-        value_layer = self.transpose_for_scores(self.value_proj(hidden_states), self.num_attention_heads)
-
-        rel_att = None
-        # Take the dot product between "query" and "key" to get the raw attention scores.
-        scale_factor = 1
-        if "c2p" in self.pos_att_type:
-            scale_factor += 1
-        if "p2c" in self.pos_att_type:
-            scale_factor += 1
-        scale = torch.sqrt(torch.tensor(query_layer.size(-1), dtype=torch.float) * scale_factor)
-        attention_scores = torch.bmm(query_layer, key_layer.transpose(-1, -2)) / scale.to(dtype=query_layer.dtype)
-        if self.relative_attention:
-            rel_embeddings = self.pos_dropout(rel_embeddings)
-            rel_att = self.disentangled_attention_bias(
-                query_layer, key_layer, relative_pos, rel_embeddings, scale_factor
-            )
-
-        if rel_att is not None:
-            attention_scores = attention_scores + rel_att
-        attention_scores = attention_scores
-        attention_scores = attention_scores.view(
-            -1, self.num_attention_heads, attention_scores.size(-2), attention_scores.size(-1)
-        )
-
-        # bsz x height x length x dimension
-        attention_probs = XSoftmax.apply(attention_scores, attention_mask, -1)
-        attention_probs = self.dropout(attention_probs)
-        context_layer = torch.bmm(
-            attention_probs.view(-1, attention_probs.size(-2), attention_probs.size(-1)), value_layer
-        )
-        context_layer = (
-            context_layer.view(-1, self.num_attention_heads, context_layer.size(-2), context_layer.size(-1))
-            .permute(0, 2, 1, 3)
-            .contiguous()
-        )
-        new_context_layer_shape = context_layer.size()[:-2] + (-1,)
-        context_layer = context_layer.view(new_context_layer_shape)
-        if output_attentions:
-            return (context_layer, attention_probs)
-        else:
-            return context_layer
-
-    def disentangled_attention_bias(self, query_layer, key_layer, relative_pos, rel_embeddings, scale_factor):
-        if relative_pos is None:
-            q = query_layer.size(-2)
-            relative_pos = build_relative_position(
-                q,
-                key_layer.size(-2),
-                bucket_size=self.position_buckets,
-                max_position=self.max_relative_positions,
-                device=query_layer.device,
-            )
-        if relative_pos.dim() == 2:
-            relative_pos = relative_pos.unsqueeze(0).unsqueeze(0)
-        elif relative_pos.dim() == 3:
-            relative_pos = relative_pos.unsqueeze(1)
-        # bsz x height x query x key
-        elif relative_pos.dim() != 4:
-            raise ValueError(f"Relative position ids must be of dim 2 or 3 or 4. {relative_pos.dim()}")
-
-        att_span = self.pos_ebd_size
-        relative_pos = relative_pos.long().to(query_layer.device)
-
-        rel_embeddings = rel_embeddings[0 : att_span * 2, :].unsqueeze(0)
-        if self.share_att_key:
-            pos_query_layer = self.transpose_for_scores(
-                self.query_proj(rel_embeddings), self.num_attention_heads
-            ).repeat(query_layer.size(0) // self.num_attention_heads, 1, 1)
-            pos_key_layer = self.transpose_for_scores(self.key_proj(rel_embeddings), self.num_attention_heads).repeat(
-                query_layer.size(0) // self.num_attention_heads, 1, 1
-            )
-        else:
-            if "c2p" in self.pos_att_type:
-                pos_key_layer = self.transpose_for_scores(
-                    self.pos_key_proj(rel_embeddings), self.num_attention_heads
-                ).repeat(
-                    query_layer.size(0) // self.num_attention_heads, 1, 1
-                )  # .split(self.all_head_size, dim=-1)
-            if "p2c" in self.pos_att_type:
-                pos_query_layer = self.transpose_for_scores(
-                    self.pos_query_proj(rel_embeddings), self.num_attention_heads
-                ).repeat(
-                    query_layer.size(0) // self.num_attention_heads, 1, 1
-                )  # .split(self.all_head_size, dim=-1)
-
-        score = 0
-        # content->position
-        if "c2p" in self.pos_att_type:
-            scale = torch.sqrt(torch.tensor(pos_key_layer.size(-1), dtype=torch.float) * scale_factor)
-            c2p_att = torch.bmm(query_layer, pos_key_layer.transpose(-1, -2))
-            c2p_pos = torch.clamp(relative_pos + att_span, 0, att_span * 2 - 1)
-            c2p_att = torch.gather(
-                c2p_att,
-                dim=-1,
-                index=c2p_pos.squeeze(0).expand([query_layer.size(0), query_layer.size(1), relative_pos.size(-1)]),
-            )
-            score += c2p_att / scale.to(dtype=c2p_att.dtype)
-
-        # position->content
-        if "p2c" in self.pos_att_type:
-            scale = torch.sqrt(torch.tensor(pos_query_layer.size(-1), dtype=torch.float) * scale_factor)
-            if key_layer.size(-2) != query_layer.size(-2):
-                r_pos = build_relative_position(
-                    key_layer.size(-2),
-                    key_layer.size(-2),
-                    bucket_size=self.position_buckets,
-                    max_position=self.max_relative_positions,
-                    device=query_layer.device,
-                )
-                r_pos = r_pos.unsqueeze(0)
-            else:
-                r_pos = relative_pos
-
-            p2c_pos = torch.clamp(-r_pos + att_span, 0, att_span * 2 - 1)
-            p2c_att = torch.bmm(key_layer, pos_query_layer.transpose(-1, -2))
-            p2c_att = torch.gather(
-                p2c_att,
-                dim=-1,
-                index=p2c_pos.squeeze(0).expand([query_layer.size(0), key_layer.size(-2), key_layer.size(-2)]),
-            ).transpose(-1, -2)
-            score += p2c_att / scale.to(dtype=p2c_att.dtype)
-
-        return score
-
-
-# Copied from transformers.models.deberta.modeling_deberta.DebertaEmbeddings with DebertaLayerNorm->LayerNorm
-class DebertaV2Embeddings(nn.Module):
-    """Construct the embeddings from word, position and token_type embeddings."""
-
-    def __init__(self, config):
-        super().__init__()
-        pad_token_id = getattr(config, "pad_token_id", 0)
-        self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
-        self.word_embeddings = nn.Embedding(config.vocab_size, self.embedding_size, padding_idx=pad_token_id)
-
-        self.position_biased_input = getattr(config, "position_biased_input", True)
-        if not self.position_biased_input:
-            self.position_embeddings = None
-        else:
-            self.position_embeddings = nn.Embedding(config.max_position_embeddings, self.embedding_size)
-
-        if config.type_vocab_size > 0:
-            self.token_type_embeddings = nn.Embedding(config.type_vocab_size, self.embedding_size)
-
-        if self.embedding_size != config.hidden_size:
-            self.embed_proj = nn.Linear(self.embedding_size, config.hidden_size, bias=False)
-        self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
-        self.dropout = StableDropout(config.hidden_dropout_prob)
-        self.config = config
-
-        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
-        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
-
-    def forward(self, input_ids=None, token_type_ids=None, position_ids=None, mask=None, inputs_embeds=None):
-        if input_ids is not None:
-            input_shape = input_ids.size()
-        else:
-            input_shape = inputs_embeds.size()[:-1]
-
-        seq_length = input_shape[1]
-
-        if position_ids is None:
-            position_ids = self.position_ids[:, :seq_length]
-
-        if token_type_ids is None:
-            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
-
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-
-        if self.position_embeddings is not None:
-            position_embeddings = self.position_embeddings(position_ids.long())
-        else:
-            position_embeddings = torch.zeros_like(inputs_embeds)
-
-        embeddings = inputs_embeds
-        if self.position_biased_input:
-            embeddings += position_embeddings
-        if self.config.type_vocab_size > 0:
-            token_type_embeddings = self.token_type_embeddings(token_type_ids)
-            embeddings += token_type_embeddings
-
-        if self.embedding_size != self.config.hidden_size:
-            embeddings = self.embed_proj(embeddings)
-
-        embeddings = self.LayerNorm(embeddings)
-
-        if mask is not None:
-            if mask.dim() != embeddings.dim():
-                if mask.dim() == 4:
-                    mask = mask.squeeze(1).squeeze(1)
-                mask = mask.unsqueeze(2)
-            mask = mask.to(embeddings.dtype)
-
-            embeddings = embeddings * mask
-
-        embeddings = self.dropout(embeddings)
-        return embeddings
-
-
-# Copied from transformers.models.deberta.modeling_deberta.DebertaPreTrainedModel with Deberta->DebertaV2
-class DebertaV2PreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = DebertaV2Config
-    base_model_prefix = "deberta"
-    _keys_to_ignore_on_load_missing = ["position_ids"]
-    _keys_to_ignore_on_load_unexpected = ["position_embeddings"]
-    supports_gradient_checkpointing = True
-
-    def _init_weights(self, module):
-        """Initialize the weights."""
-        if isinstance(module, nn.Linear):
-            # Slightly different from the TF version which uses truncated_normal for initialization
-            # cf https://github.com/pytorch/pytorch/pull/5617
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, DebertaV2Encoder):
-            module.gradient_checkpointing = value
-
-
-DEBERTA_START_DOCSTRING = r"""
-    The DeBERTa model was proposed in [DeBERTa: Decoding-enhanced BERT with Disentangled
-    Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It's build
-    on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two
-    improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.
-
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-
-
-    Parameters:
-        config ([`DebertaV2Config`]): Model configuration class with all the parameters of the model.
-            Initializing with a config file does not load the weights associated with the model, only the
-            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-DEBERTA_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `({0})`):
-            Indices of input sequence tokens in the vocabulary.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-        token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
-            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
-            1]`:
-
-            - 0 corresponds to a *sentence A* token,
-            - 1 corresponds to a *sentence B* token.
-
-            [What are token type IDs?](../glossary#token-type-ids)
-        position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.max_position_embeddings - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
-            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
-            is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
-            model's internal embedding lookup matrix.
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-@add_start_docstrings(
-    "The bare DeBERTa Model transformer outputting raw hidden-states without any specific head on top.",
-    DEBERTA_START_DOCSTRING,
-)
-# Copied from transformers.models.deberta.modeling_deberta.DebertaModel with Deberta->DebertaV2
-class DebertaV2Model(DebertaV2PreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.embeddings = DebertaV2Embeddings(config)
-        self.encoder = DebertaV2Encoder(config)
-        self.z_steps = 0
-        self.config = config
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.embeddings.word_embeddings
-
-    def set_input_embeddings(self, new_embeddings):
-        self.embeddings.word_embeddings = new_embeddings
-
-    def _prune_heads(self, heads_to_prune):
-        """
-        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
-        class PreTrainedModel
-        """
-        raise NotImplementedError("The prune function is not implemented in DeBERTa model.")
-
-    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=BaseModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutput]:
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            input_shape = input_ids.size()
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        device = input_ids.device if input_ids is not None else inputs_embeds.device
-
-        if attention_mask is None:
-            attention_mask = torch.ones(input_shape, device=device)
-        if token_type_ids is None:
-            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
-
-        embedding_output = self.embeddings(
-            input_ids=input_ids,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            mask=attention_mask,
-            inputs_embeds=inputs_embeds,
-        )
-
-        encoder_outputs = self.encoder(
-            embedding_output,
-            attention_mask,
-            output_hidden_states=True,
-            output_attentions=output_attentions,
-            return_dict=return_dict,
-        )
-        encoded_layers = encoder_outputs[1]
-
-        if self.z_steps > 1:
-            hidden_states = encoded_layers[-2]
-            layers = [self.encoder.layer[-1] for _ in range(self.z_steps)]
-            query_states = encoded_layers[-1]
-            rel_embeddings = self.encoder.get_rel_embedding()
-            attention_mask = self.encoder.get_attention_mask(attention_mask)
-            rel_pos = self.encoder.get_rel_pos(embedding_output)
-            for layer in layers[1:]:
-                query_states = layer(
-                    hidden_states,
-                    attention_mask,
-                    output_attentions=False,
-                    query_states=query_states,
-                    relative_pos=rel_pos,
-                    rel_embeddings=rel_embeddings,
-                )
-                encoded_layers.append(query_states)
-
-        sequence_output = encoded_layers[-1]
-
-        if not return_dict:
-            return (sequence_output,) + encoder_outputs[(1 if output_hidden_states else 2) :]
-
-        return BaseModelOutput(
-            last_hidden_state=sequence_output,
-            hidden_states=encoder_outputs.hidden_states if output_hidden_states else None,
-            attentions=encoder_outputs.attentions,
-        )
-
-
-@add_start_docstrings("""DeBERTa Model with a `language modeling` head on top.""", DEBERTA_START_DOCSTRING)
-class DebertaV2ForMaskedLM(DebertaV2PreTrainedModel):
-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias", "cls.predictions.decoder.weight"]
-
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.deberta = DebertaV2Model(config)
-        self.cls = DebertaV2OnlyMLMHead(config)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_output_embeddings(self):
-        return self.cls.predictions.decoder
-
-    def set_output_embeddings(self, new_embeddings):
-        self.cls.predictions.decoder = new_embeddings
-
-    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=MaskedLMOutput,
-        config_class=_CONFIG_FOR_DOC,
-        mask="[MASK]",
-    )
-    # Copied from transformers.models.deberta.modeling_deberta.DebertaForMaskedLM.forward with Deberta->DebertaV2
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        labels: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, MaskedLMOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
-            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
-            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
-        """
-
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.deberta(
-            input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output = outputs[0]
-        prediction_scores = self.cls(sequence_output)
-
-        masked_lm_loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()  # -100 index = padding token
-            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
-
-        if not return_dict:
-            output = (prediction_scores,) + outputs[1:]
-            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
-
-        return MaskedLMOutput(
-            loss=masked_lm_loss,
-            logits=prediction_scores,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-# copied from transformers.models.bert.BertPredictionHeadTransform with bert -> deberta
-class DebertaV2PredictionHeadTransform(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        if isinstance(config.hidden_act, str):
-            self.transform_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.transform_act_fn = config.hidden_act
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.transform_act_fn(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states)
-        return hidden_states
-
-
-# copied from transformers.models.bert.BertLMPredictionHead with bert -> deberta
-class DebertaV2LMPredictionHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.transform = DebertaV2PredictionHeadTransform(config)
-
-        # The output weights are the same as the input embeddings, but there is
-        # an output-only bias for each token.
-        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
-
-        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
-        self.decoder.bias = self.bias
-
-    def forward(self, hidden_states):
-        hidden_states = self.transform(hidden_states)
-        hidden_states = self.decoder(hidden_states)
-        return hidden_states
-
-
-# copied from transformers.models.bert.BertOnlyMLMHead with bert -> deberta
-class DebertaV2OnlyMLMHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.predictions = DebertaV2LMPredictionHead(config)
-
-    def forward(self, sequence_output):
-        prediction_scores = self.predictions(sequence_output)
-        return prediction_scores
-
-
-@add_start_docstrings(
-    """
-    DeBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the
-    pooled output) e.g. for GLUE tasks.
-    """,
-    DEBERTA_START_DOCSTRING,
-)
-class DebertaV2ForSequenceClassification(DebertaV2PreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        num_labels = getattr(config, "num_labels", 2)
-        self.num_labels = num_labels
-
-        self.deberta = DebertaV2Model(config)
-        self.pooler = ContextPooler(config)
-        output_dim = self.pooler.output_dim
-
-        self.classifier = nn.Linear(output_dim, num_labels)
-        drop_out = getattr(config, "cls_dropout", None)
-        drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out
-        self.dropout = StableDropout(drop_out)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.deberta.get_input_embeddings()
-
-    def set_input_embeddings(self, new_embeddings):
-        self.deberta.set_input_embeddings(new_embeddings)
-
-    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=SequenceClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    # Copied from transformers.models.deberta.modeling_deberta.DebertaForSequenceClassification.forward with Deberta->DebertaV2
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        labels: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, SequenceClassifierOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.deberta(
-            input_ids,
-            token_type_ids=token_type_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        encoder_layer = outputs[0]
-        pooled_output = self.pooler(encoder_layer)
-        pooled_output = self.dropout(pooled_output)
-        logits = self.classifier(pooled_output)
-
-        loss = None
-        if labels is not None:
-            if self.config.problem_type is None:
-                if self.num_labels == 1:
-                    # regression task
-                    loss_fn = nn.MSELoss()
-                    logits = logits.view(-1).to(labels.dtype)
-                    loss = loss_fn(logits, labels.view(-1))
-                elif labels.dim() == 1 or labels.size(-1) == 1:
-                    label_index = (labels >= 0).nonzero()
-                    labels = labels.long()
-                    if label_index.size(0) > 0:
-                        labeled_logits = torch.gather(
-                            logits, 0, label_index.expand(label_index.size(0), logits.size(1))
-                        )
-                        labels = torch.gather(labels, 0, label_index.view(-1))
-                        loss_fct = CrossEntropyLoss()
-                        loss = loss_fct(labeled_logits.view(-1, self.num_labels).float(), labels.view(-1))
-                    else:
-                        loss = torch.tensor(0).to(logits)
-                else:
-                    log_softmax = nn.LogSoftmax(-1)
-                    loss = -((log_softmax(logits) * labels).sum(-1)).mean()
-            elif self.config.problem_type == "regression":
-                loss_fct = MSELoss()
-                if self.num_labels == 1:
-                    loss = loss_fct(logits.squeeze(), labels.squeeze())
-                else:
-                    loss = loss_fct(logits, labels)
-            elif self.config.problem_type == "single_label_classification":
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-            elif self.config.problem_type == "multi_label_classification":
-                loss_fct = BCEWithLogitsLoss()
-                loss = loss_fct(logits, labels)
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return SequenceClassifierOutput(
-            loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
-        )
-
-
-@add_start_docstrings(
-    """
-    DeBERTa Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
-    Named-Entity-Recognition (NER) tasks.
-    """,
-    DEBERTA_START_DOCSTRING,
-)
-# Copied from transformers.models.deberta.modeling_deberta.DebertaForTokenClassification with Deberta->DebertaV2
-class DebertaV2ForTokenClassification(DebertaV2PreTrainedModel):
-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-
-        self.deberta = DebertaV2Model(config)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=TokenClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        labels: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, TokenClassifierOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.deberta(
-            input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output = outputs[0]
-
-        sequence_output = self.dropout(sequence_output)
-        logits = self.classifier(sequence_output)
-
-        loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return TokenClassifierOutput(
-            loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
-        )
-    
-class TokenClassifierRegressionOutput(ModelOutput):
-    """
-    Base class for outputs of token classification models.
-
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) :
-            Classification loss.
-        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`):
-            Classification scores (before SoftMax).
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
-            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
-
-            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`.
-
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    logits: torch.FloatTensor = None
-    values: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-    
-class DebertaV2ForTokenClassificationRegression(DebertaV2PreTrainedModel):
-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = 4
-
-        self.deberta = DebertaV2Model(config)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        
-        self.hidden1 = nn.Linear(config.hidden_size, config.hidden_size)
-        self.classifier = nn.Linear(config.hidden_size, self.num_labels)
-        
-        self.hidden2 = nn.Linear(config.hidden_size, config.hidden_size)
-        self.regressor = nn.Linear(config.hidden_size, 1)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=TokenClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        labels: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, TokenClassifierOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.deberta(
-            input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output = outputs[0]
-
-        sequence_output = self.dropout(sequence_output)
-        
-        logits = self.classifier(self.hidden1(sequence_output))
-        values = self.regressor(self.hidden2(sequence_output))
-
-        loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return TokenClassifierRegressionOutput(
-            loss=loss, logits=logits, values=values, hidden_states=outputs.hidden_states, attentions=outputs.attentions
-        )
-
-
-@add_start_docstrings(
-    """
-    DeBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
-    layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
-    """,
-    DEBERTA_START_DOCSTRING,
-)
-class DebertaV2ForQuestionAnswering(DebertaV2PreTrainedModel):
-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-
-        self.deberta = DebertaV2Model(config)
-        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=QuestionAnsweringModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-        qa_target_start_index=_QA_TARGET_START_INDEX,
-        qa_target_end_index=_QA_TARGET_END_INDEX,
-    )
-    # Copied from transformers.models.deberta.modeling_deberta.DebertaForQuestionAnswering.forward with Deberta->DebertaV2
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        start_positions: Optional[torch.Tensor] = None,
-        end_positions: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, QuestionAnsweringModelOutput]:
-        r"""
-        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for position (index) of the start of the labelled span for computing the token classification loss.
-            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
-            are not taken into account for computing the loss.
-        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for position (index) of the end of the labelled span for computing the token classification loss.
-            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
-            are not taken into account for computing the loss.
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.deberta(
-            input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output = outputs[0]
-
-        logits = self.qa_outputs(sequence_output)
-        start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1).contiguous()
-
-        total_loss = None
-        if start_positions is not None and end_positions is not None:
-            # If we are on multi-GPU, split add a dimension
-            if len(start_positions.size()) > 1:
-                start_positions = start_positions.squeeze(-1)
-            if len(end_positions.size()) > 1:
-                end_positions = end_positions.squeeze(-1)
-            # sometimes the start/end positions are outside our model inputs, we ignore these terms
-            ignored_index = start_logits.size(1)
-            start_positions = start_positions.clamp(0, ignored_index)
-            end_positions = end_positions.clamp(0, ignored_index)
-
-            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
-            start_loss = loss_fct(start_logits, start_positions)
-            end_loss = loss_fct(end_logits, end_positions)
-            total_loss = (start_loss + end_loss) / 2
-
-        if not return_dict:
-            output = (start_logits, end_logits) + outputs[1:]
-            return ((total_loss,) + output) if total_loss is not None else output
-
-        return QuestionAnsweringModelOutput(
-            loss=total_loss,
-            start_logits=start_logits,
-            end_logits=end_logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    DeBERTa Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
-    softmax) e.g. for RocStories/SWAG tasks.
-    """,
-    DEBERTA_START_DOCSTRING,
-)
-class DebertaV2ForMultipleChoice(DebertaV2PreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        num_labels = getattr(config, "num_labels", 2)
-        self.num_labels = num_labels
-
-        self.deberta = DebertaV2Model(config)
-        self.pooler = ContextPooler(config)
-        output_dim = self.pooler.output_dim
-
-        self.classifier = nn.Linear(output_dim, 1)
-        drop_out = getattr(config, "cls_dropout", None)
-        drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out
-        self.dropout = StableDropout(drop_out)
-
-        self.init_weights()
-
-    def get_input_embeddings(self):
-        return self.deberta.get_input_embeddings()
-
-    def set_input_embeddings(self, new_embeddings):
-        self.deberta.set_input_embeddings(new_embeddings)
-
-    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=MultipleChoiceModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        labels: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, MultipleChoiceModelOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
-            num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
-            `input_ids` above)
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
-
-        flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
-        flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
-        flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
-        flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
-        flat_inputs_embeds = (
-            inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
-            if inputs_embeds is not None
-            else None
-        )
-
-        outputs = self.deberta(
-            flat_input_ids,
-            position_ids=flat_position_ids,
-            token_type_ids=flat_token_type_ids,
-            attention_mask=flat_attention_mask,
-            inputs_embeds=flat_inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        encoder_layer = outputs[0]
-        pooled_output = self.pooler(encoder_layer)
-        pooled_output = self.dropout(pooled_output)
-        logits = self.classifier(pooled_output)
-        reshaped_logits = logits.view(-1, num_choices)
-
-        loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(reshaped_logits, labels)
-
-        if not return_dict:
-            output = (reshaped_logits,) + outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return MultipleChoiceModelOutput(
-            loss=loss,
-            logits=reshaped_logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )

.ipynb_checkpoints/models-checkpoint.py

@@ -1,738 +0,0 @@
-import yaml
-import random
-import inspect
-import numpy as np
-from tqdm import tqdm
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from einops import repeat
-from tools.torch_tools import wav_to_fbank
-
-from audioldm.audio.stft import TacotronSTFT
-from audioldm.variational_autoencoder import AutoencoderKL
-from audioldm.utils import default_audioldm_config, get_metadata
-
-from transformers import CLIPTokenizer, AutoTokenizer, AutoProcessor
-from transformers import CLIPTextModel, T5EncoderModel, AutoModel, ClapAudioModel, ClapTextModel
-
-import sys
-sys.path.insert(0, "diffusers/src")
-
-import diffusers
-from diffusers.utils import randn_tensor
-from diffusers import DDPMScheduler, UNet2DConditionModel, UNet2DConditionModelMusic
-from diffusers import AutoencoderKL as DiffuserAutoencoderKL
-from layers.layers import chord_tokenizer, beat_tokenizer, Chord_Embedding, Beat_Embedding, Music_PositionalEncoding, Fundamental_Music_Embedding
-
-def build_pretrained_models(name):
-	checkpoint = torch.load(get_metadata()[name]["path"], map_location="cpu")
-	scale_factor = checkpoint["state_dict"]["scale_factor"].item()
-
-	vae_state_dict = {k[18:]: v for k, v in checkpoint["state_dict"].items() if "first_stage_model." in k}
-
-	config = default_audioldm_config(name)
-	vae_config = config["model"]["params"]["first_stage_config"]["params"]
-	vae_config["scale_factor"] = scale_factor
-
-	vae = AutoencoderKL(**vae_config)
-	vae.load_state_dict(vae_state_dict)
-
-	fn_STFT = TacotronSTFT(
-		config["preprocessing"]["stft"]["filter_length"],
-		config["preprocessing"]["stft"]["hop_length"],
-		config["preprocessing"]["stft"]["win_length"],
-		config["preprocessing"]["mel"]["n_mel_channels"],
-		config["preprocessing"]["audio"]["sampling_rate"],
-		config["preprocessing"]["mel"]["mel_fmin"],
-		config["preprocessing"]["mel"]["mel_fmax"],
-	)
-
-	vae.eval()
-	fn_STFT.eval()
-	return vae, fn_STFT
-
-
-class AudioDiffusion(nn.Module):
-	def __init__(
-		self,
-		text_encoder_name,
-		scheduler_name,
-		unet_model_name=None,
-		unet_model_config_path=None,
-		snr_gamma=None,
-		freeze_text_encoder=True,
-		uncondition=False,
-
-	):
-		super().__init__()
-
-		assert unet_model_name is not None or unet_model_config_path is not None, "Either UNet pretrain model name or a config file path is required"
-
-		self.text_encoder_name = text_encoder_name
-		self.scheduler_name = scheduler_name
-		self.unet_model_name = unet_model_name
-		self.unet_model_config_path = unet_model_config_path
-		self.snr_gamma = snr_gamma
-		self.freeze_text_encoder = freeze_text_encoder
-		self.uncondition = uncondition
-
-		# https://huggingface.co/docs/diffusers/v0.14.0/en/api/schedulers/overview
-		self.noise_scheduler = DDPMScheduler.from_pretrained(self.scheduler_name, subfolder="scheduler")
-		self.inference_scheduler = DDPMScheduler.from_pretrained(self.scheduler_name, subfolder="scheduler")
-
-		if unet_model_config_path:
-			unet_config = UNet2DConditionModel.load_config(unet_model_config_path)
-			self.unet = UNet2DConditionModel.from_config(unet_config, subfolder="unet")
-			self.set_from = "random"
-			print("UNet initialized randomly.")
-		else:
-			self.unet = UNet2DConditionModel.from_pretrained(unet_model_name, subfolder="unet")
-			self.set_from = "pre-trained"
-			self.group_in = nn.Sequential(nn.Linear(8, 512), nn.Linear(512, 4))
-			self.group_out = nn.Sequential(nn.Linear(4, 512), nn.Linear(512, 8))
-			print("UNet initialized from stable diffusion checkpoint.")
-
-		if "stable-diffusion" in self.text_encoder_name:
-			self.tokenizer = CLIPTokenizer.from_pretrained(self.text_encoder_name, subfolder="tokenizer")
-			self.text_encoder = CLIPTextModel.from_pretrained(self.text_encoder_name, subfolder="text_encoder")
-		elif "t5" in self.text_encoder_name:
-			self.tokenizer = AutoTokenizer.from_pretrained(self.text_encoder_name)
-			self.text_encoder = T5EncoderModel.from_pretrained(self.text_encoder_name)
-		else:
-			self.tokenizer = AutoTokenizer.from_pretrained(self.text_encoder_name)
-			self.text_encoder = AutoModel.from_pretrained(self.text_encoder_name)
-
-	def compute_snr(self, timesteps):
-		"""
-		Computes SNR as per https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849
-		"""
-		alphas_cumprod = self.noise_scheduler.alphas_cumprod
-		sqrt_alphas_cumprod = alphas_cumprod**0.5
-		sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5
-
-		# Expand the tensors.
-		# Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026
-		sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
-		while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape):
-			sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None]
-		alpha = sqrt_alphas_cumprod.expand(timesteps.shape)
-
-		sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
-		while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape):
-			sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None]
-		sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape)
-
-		# Compute SNR.
-		snr = (alpha / sigma) ** 2
-		return snr
-
-	def encode_text(self, prompt):
-		device = self.text_encoder.device
-		batch = self.tokenizer(
-			prompt, max_length=self.tokenizer.model_max_length, padding=True, truncation=True, return_tensors="pt"
-		)
-		input_ids, attention_mask = batch.input_ids.to(device), batch.attention_mask.to(device)
-
-		if self.freeze_text_encoder:
-			with torch.no_grad():
-				encoder_hidden_states = self.text_encoder(
-					input_ids=input_ids, attention_mask=attention_mask
-				)[0]
-		else:
-			encoder_hidden_states = self.text_encoder(
-				input_ids=input_ids, attention_mask=attention_mask
-			)[0]
-
-		boolean_encoder_mask = (attention_mask == 1).to(device)
-		return encoder_hidden_states, boolean_encoder_mask
-
-	def forward(self, latents, prompt, validation_mode=False):
-		device = self.text_encoder.device
-		num_train_timesteps = self.noise_scheduler.num_train_timesteps
-		self.noise_scheduler.set_timesteps(num_train_timesteps, device=device)
-
-		encoder_hidden_states, boolean_encoder_mask = self.encode_text(prompt)
-
-		if self.uncondition:
-			mask_indices = [k for k in range(len(prompt)) if random.random() < 0.1]
-			if len(mask_indices) > 0:
-				encoder_hidden_states[mask_indices] = 0
-
-		bsz = latents.shape[0]
-
-		if validation_mode:
-			timesteps = (self.noise_scheduler.num_train_timesteps//2) * torch.ones((bsz,), dtype=torch.int64, device=device)
-		else:
-			# Sample a random timestep for each instance
-			timesteps = torch.randint(0, self.noise_scheduler.num_train_timesteps, (bsz,), device=device)
-			# print('in if ', timesteps)
-		timesteps = timesteps.long()
-		# print('outside if ' , timesteps)
-		noise = torch.randn_like(latents)
-		noisy_latents = self.noise_scheduler.add_noise(latents, noise, timesteps)
-
-		# Get the target for loss depending on the prediction type
-		if self.noise_scheduler.config.prediction_type == "epsilon":
-			target = noise
-		elif self.noise_scheduler.config.prediction_type == "v_prediction":
-			target = self.noise_scheduler.get_velocity(latents, noise, timesteps)
-		else:
-			raise ValueError(f"Unknown prediction type {self.noise_scheduler.config.prediction_type}")
-
-		if self.set_from == "random":
-			model_pred = self.unet(
-				noisy_latents, timesteps, encoder_hidden_states, 
-				encoder_attention_mask=boolean_encoder_mask
-			).sample
-
-		elif self.set_from == "pre-trained":
-			compressed_latents = self.group_in(noisy_latents.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous()
-			model_pred = self.unet(
-				compressed_latents, timesteps, encoder_hidden_states, 
-				encoder_attention_mask=boolean_encoder_mask
-			).sample
-			model_pred = self.group_out(model_pred.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous()
-
-		if self.snr_gamma is None:
-			loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-		else:
-			# Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
-			# Adaptef from huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py
-			snr = self.compute_snr(timesteps)
-			mse_loss_weights = (
-				torch.stack([snr, self.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
-			)
-			loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
-			loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
-			loss = loss.mean()
-
-		return loss
-
-	@torch.no_grad()
-	def inference(self, prompt, inference_scheduler, num_steps=20, guidance_scale=3, num_samples_per_prompt=1, 
-					disable_progress=True):
-		device = self.text_encoder.device
-		classifier_free_guidance = guidance_scale > 1.0
-		batch_size = len(prompt) * num_samples_per_prompt
-
-		if classifier_free_guidance:
-			prompt_embeds, boolean_prompt_mask = self.encode_text_classifier_free(prompt, num_samples_per_prompt)
-		else:
-			prompt_embeds, boolean_prompt_mask = self.encode_text(prompt)
-			prompt_embeds = prompt_embeds.repeat_interleave(num_samples_per_prompt, 0)
-			boolean_prompt_mask = boolean_prompt_mask.repeat_interleave(num_samples_per_prompt, 0)
-
-		inference_scheduler.set_timesteps(num_steps, device=device)
-		timesteps = inference_scheduler.timesteps
-
-		num_channels_latents = self.unet.in_channels
-		latents = self.prepare_latents(batch_size, inference_scheduler, num_channels_latents, prompt_embeds.dtype, device)
-
-		num_warmup_steps = len(timesteps) - num_steps * inference_scheduler.order
-		progress_bar = tqdm(range(num_steps), disable=disable_progress)
-
-		for i, t in enumerate(timesteps):
-			# expand the latents if we are doing classifier free guidance
-			latent_model_input = torch.cat([latents] * 2) if classifier_free_guidance else latents
-			latent_model_input = inference_scheduler.scale_model_input(latent_model_input, t)
-
-			noise_pred = self.unet(
-				latent_model_input, t, encoder_hidden_states=prompt_embeds,
-				encoder_attention_mask=boolean_prompt_mask
-			).sample
-
-			# perform guidance
-			if classifier_free_guidance:
-				noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-				noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-			# compute the previous noisy sample x_t -> x_t-1
-			latents = inference_scheduler.step(noise_pred, t, latents).prev_sample
-
-			# call the callback, if provided
-			if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % inference_scheduler.order == 0):
-				progress_bar.update(1)
-
-		if self.set_from == "pre-trained":
-			latents = self.group_out(latents.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous()
-		return latents
-
-	def prepare_latents(self, batch_size, inference_scheduler, num_channels_latents, dtype, device):
-		shape = (batch_size, num_channels_latents, 256, 16)
-		latents = randn_tensor(shape, generator=None, device=device, dtype=dtype)
-		# scale the initial noise by the standard deviation required by the scheduler
-		latents = latents * inference_scheduler.init_noise_sigma
-		return latents
-
-	def encode_text_classifier_free(self, prompt, num_samples_per_prompt):
-		device = self.text_encoder.device
-		batch = self.tokenizer(
-			prompt, max_length=self.tokenizer.model_max_length, padding=True, truncation=True, return_tensors="pt"
-		)
-		input_ids, attention_mask = batch.input_ids.to(device), batch.attention_mask.to(device)
-
-		with torch.no_grad():
-			prompt_embeds = self.text_encoder(
-				input_ids=input_ids, attention_mask=attention_mask
-			)[0]
-				
-		prompt_embeds = prompt_embeds.repeat_interleave(num_samples_per_prompt, 0)
-		attention_mask = attention_mask.repeat_interleave(num_samples_per_prompt, 0)
-
-		# get unconditional embeddings for classifier free guidance
-		uncond_tokens = [""] * len(prompt)
-
-		max_length = prompt_embeds.shape[1]
-		uncond_batch = self.tokenizer(
-			uncond_tokens, max_length=max_length, padding="max_length", truncation=True, return_tensors="pt",
-		)
-		uncond_input_ids = uncond_batch.input_ids.to(device)
-		uncond_attention_mask = uncond_batch.attention_mask.to(device)
-
-		with torch.no_grad():
-			negative_prompt_embeds = self.text_encoder(
-				input_ids=uncond_input_ids, attention_mask=uncond_attention_mask
-			)[0]
-				
-		negative_prompt_embeds = negative_prompt_embeds.repeat_interleave(num_samples_per_prompt, 0)
-		uncond_attention_mask = uncond_attention_mask.repeat_interleave(num_samples_per_prompt, 0)
-
-		# For classifier free guidance, we need to do two forward passes.
-		# We concatenate the unconditional and text embeddings into a single batch to avoid doing two forward passes
-		prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-		prompt_mask = torch.cat([uncond_attention_mask, attention_mask])
-		boolean_prompt_mask = (prompt_mask == 1).to(device)
-
-		return prompt_embeds, boolean_prompt_mask
-	
-
-class MusicAudioDiffusion(nn.Module):
-	def __init__(
-		self,
-		text_encoder_name,
-		scheduler_name,
-		unet_model_name=None,
-		unet_model_config_path=None,
-		snr_gamma=None,
-		freeze_text_encoder=True,
-		uncondition=False,
-
-		d_fme = 1024,  #FME
-		fme_type = "se", 
-		base = 1, 
-		if_trainable = True, 
-		translation_bias_type = "nd",
-		emb_nn = True,
-		d_pe = 1024, #PE
-		if_index = True, 
-		if_global_timing = True,
-		if_modulo_timing = False,
-		d_beat = 1024, #Beat
-		d_oh_beat_type = 7, 
-		beat_len = 50,
-		d_chord = 1024, #Chord
-		d_oh_chord_type = 12,
-		d_oh_inv_type = 4,
-		chord_len = 20,
-
-	):
-		super().__init__()
-
-		assert unet_model_name is not None or unet_model_config_path is not None, "Either UNet pretrain model name or a config file path is required"
-
-		self.text_encoder_name = text_encoder_name
-		self.scheduler_name = scheduler_name
-		self.unet_model_name = unet_model_name
-		self.unet_model_config_path = unet_model_config_path
-		self.snr_gamma = snr_gamma
-		self.freeze_text_encoder = freeze_text_encoder
-		self.uncondition = uncondition
-
-		# https://huggingface.co/docs/diffusers/v0.14.0/en/api/schedulers/overview
-		self.noise_scheduler = DDPMScheduler.from_pretrained(self.scheduler_name, subfolder="scheduler")
-		self.inference_scheduler = DDPMScheduler.from_pretrained(self.scheduler_name, subfolder="scheduler")
-
-		if unet_model_config_path:
-			unet_config = UNet2DConditionModelMusic.load_config(unet_model_config_path)
-			self.unet = UNet2DConditionModelMusic.from_config(unet_config, subfolder="unet")
-			self.set_from = "random"
-			print("UNet initialized randomly.")
-		else:
-			self.unet = UNet2DConditionModel.from_pretrained(unet_model_name, subfolder="unet")
-			self.set_from = "pre-trained"
-			self.group_in = nn.Sequential(nn.Linear(8, 512), nn.Linear(512, 4))
-			self.group_out = nn.Sequential(nn.Linear(4, 512), nn.Linear(512, 8))
-			print("UNet initialized from stable diffusion checkpoint.")
-
-		if "stable-diffusion" in self.text_encoder_name:
-			self.tokenizer = CLIPTokenizer.from_pretrained(self.text_encoder_name, subfolder="tokenizer")
-			self.text_encoder = CLIPTextModel.from_pretrained(self.text_encoder_name, subfolder="text_encoder")
-		elif "t5" in self.text_encoder_name:
-			self.tokenizer = AutoTokenizer.from_pretrained(self.text_encoder_name)
-			self.text_encoder = T5EncoderModel.from_pretrained(self.text_encoder_name)
-		else:
-			self.tokenizer = AutoTokenizer.from_pretrained(self.text_encoder_name)
-			self.text_encoder = AutoModel.from_pretrained(self.text_encoder_name)
-
-		self.device = self.text_encoder.device
-		#Music Feature Encoder
-		self.FME = Fundamental_Music_Embedding(d_model = d_fme, base= base, if_trainable = False, type = fme_type,emb_nn=emb_nn,translation_bias_type = translation_bias_type)
-		self.PE = Music_PositionalEncoding(d_model = d_pe, if_index = if_index, if_global_timing = if_global_timing, if_modulo_timing = if_modulo_timing, device = self.device)
-		# self.PE2 = Music_PositionalEncoding(d_model = d_pe, if_index = if_index, if_global_timing = if_global_timing, if_modulo_timing = if_modulo_timing, device = self.device)
-		self.beat_tokenizer = beat_tokenizer(seq_len_beat=beat_len, if_pad = True)
-		self.beat_embedding_layer = Beat_Embedding(self.PE, d_model = d_beat, d_oh_beat_type = d_oh_beat_type)
-		self.chord_embedding_layer = Chord_Embedding(self.FME, self.PE, d_model = d_chord, d_oh_type = d_oh_chord_type, d_oh_inv = d_oh_inv_type)
-		self.chord_tokenizer = chord_tokenizer(seq_len_chord=chord_len, if_pad = True)
-
-
-	def compute_snr(self, timesteps):
-		"""
-		Computes SNR as per https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849
-		"""
-		alphas_cumprod = self.noise_scheduler.alphas_cumprod
-		sqrt_alphas_cumprod = alphas_cumprod**0.5
-		sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5
-
-		# Expand the tensors.
-		# Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026
-		sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
-		while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape):
-			sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None]
-		alpha = sqrt_alphas_cumprod.expand(timesteps.shape)
-
-		sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
-		while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape):
-			sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None]
-		sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape)
-
-		# Compute SNR.
-		snr = (alpha / sigma) ** 2
-		return snr
-
-	def encode_text(self, prompt):
-		device = self.text_encoder.device
-		batch = self.tokenizer(
-			prompt, max_length=self.tokenizer.model_max_length, padding=True, truncation=True, return_tensors="pt"
-		)
-		input_ids, attention_mask = batch.input_ids.to(device), batch.attention_mask.to(device) #cuda
-		if self.freeze_text_encoder:
-			with torch.no_grad():
-				encoder_hidden_states = self.text_encoder(
-					input_ids=input_ids, attention_mask=attention_mask
-				)[0] #batch, len_text, dim
-		else:
-			encoder_hidden_states = self.text_encoder(
-				input_ids=input_ids, attention_mask=attention_mask
-			)[0]
-		boolean_encoder_mask = (attention_mask == 1).to(device) ##batch, len_text
-		return encoder_hidden_states, boolean_encoder_mask
-
-	def encode_beats(self, beats): 
-		# device = self.beat_embedding_layer.device
-		out_beat = []
-		out_beat_timing = []
-		out_mask = []
-		for beat in beats:
-			tokenized_beats,tokenized_beats_timing, tokenized_beat_mask = self.beat_tokenizer(beat)
-			out_beat.append(tokenized_beats)
-			out_beat_timing.append(tokenized_beats_timing)
-			out_mask.append(tokenized_beat_mask)
-		out_beat, out_beat_timing, out_mask = torch.tensor(out_beat).cuda(), torch.tensor(out_beat_timing).cuda(), torch.tensor(out_mask).cuda() #batch, len_beat
-		embedded_beat = self.beat_embedding_layer(out_beat, out_beat_timing)
-
-		return embedded_beat, out_mask
-
-	def encode_chords(self, chords,chords_time):
-		out_chord_root = []
-		out_chord_type = []
-		out_chord_inv = []
-		out_chord_timing = []
-		out_mask = []
-		for chord, chord_time in zip(chords,chords_time): #batch loop
-			tokenized_chord_root, tokenized_chord_type, tokenized_chord_inv, tokenized_chord_time, tokenized_chord_mask = self.chord_tokenizer(chord, chord_time)
-			out_chord_root.append(tokenized_chord_root)
-			out_chord_type.append(tokenized_chord_type)
-			out_chord_inv.append(tokenized_chord_inv)
-			out_chord_timing.append(tokenized_chord_time)
-			out_mask.append(tokenized_chord_mask)
-		#chords: (B, LEN, 4)
-		out_chord_root, out_chord_type, out_chord_inv, out_chord_timing, out_mask = torch.tensor(out_chord_root).cuda(), torch.tensor(out_chord_type).cuda(), torch.tensor(out_chord_inv).cuda(), torch.tensor(out_chord_timing).cuda(), torch.tensor(out_mask).cuda()
-		embedded_chord = self.chord_embedding_layer(out_chord_root, out_chord_type, out_chord_inv, out_chord_timing)
-		return embedded_chord, out_mask
-		# return out_chord_root, out_mask
-
-
-	def forward(self, latents, prompt, beats, chords,chords_time, validation_mode=False):
-		device = self.text_encoder.device
-		num_train_timesteps = self.noise_scheduler.num_train_timesteps
-		self.noise_scheduler.set_timesteps(num_train_timesteps, device=device)
-
-		encoder_hidden_states, boolean_encoder_mask = self.encode_text(prompt)
-		
-		# with torch.no_grad():
-		encoded_beats, beat_mask = self.encode_beats(beats) #batch, len_beats, dim; batch, len_beats
-		encoded_chords, chord_mask = self.encode_chords(chords,chords_time)
-
-
-		if self.uncondition:
-			mask_indices = [k for k in range(len(prompt)) if random.random() < 0.1]
-			if len(mask_indices) > 0:
-				encoder_hidden_states[mask_indices] = 0
-				encoded_chords[mask_indices] = 0
-				encoded_beats[mask_indices] = 0
-
-		bsz = latents.shape[0]
-
-		if validation_mode:
-			timesteps = (self.noise_scheduler.num_train_timesteps//2) * torch.ones((bsz,), dtype=torch.int64, device=device)
-		else:
-			timesteps = torch.randint(0, self.noise_scheduler.num_train_timesteps, (bsz,), device=device)
-		
-		
-		timesteps = timesteps.long()
-
-		noise = torch.randn_like(latents)
-		noisy_latents = self.noise_scheduler.add_noise(latents, noise, timesteps)
-
-		# Get the target for loss depending on the prediction type
-		if self.noise_scheduler.config.prediction_type == "epsilon":
-			target = noise
-		elif self.noise_scheduler.config.prediction_type == "v_prediction":
-			target = self.noise_scheduler.get_velocity(latents, noise, timesteps)
-		else:
-			raise ValueError(f"Unknown prediction type {self.noise_scheduler.config.prediction_type}")
-
-		if self.set_from == "random":
-			# model_pred = torch.zeros((bsz,8,256,16)).to(device)
-			model_pred = self.unet(
-				noisy_latents, timesteps, encoder_hidden_states, encoded_beats, encoded_chords,
-				encoder_attention_mask=boolean_encoder_mask, beat_attention_mask = beat_mask, chord_attention_mask = chord_mask
-			).sample
-
-		elif self.set_from == "pre-trained":
-			compressed_latents = self.group_in(noisy_latents.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous()
-			model_pred = self.unet(
-				compressed_latents, timesteps, encoder_hidden_states, 
-				encoder_attention_mask=boolean_encoder_mask
-			).sample
-			model_pred = self.group_out(model_pred.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous()
-
-		if self.snr_gamma is None:
-			loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-		else:
-			# Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
-			# Adaptef from huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py
-			snr = self.compute_snr(timesteps)
-			mse_loss_weights = (
-				torch.stack([snr, self.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
-			)
-			loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
-			loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
-			loss = loss.mean()
-
-		return loss
-
-	@torch.no_grad()
-	def inference(self, prompt, beats, chords,chords_time, inference_scheduler, num_steps=20, guidance_scale=3, num_samples_per_prompt=1, 
-				  disable_progress=True):
-		device = self.text_encoder.device
-		classifier_free_guidance = guidance_scale > 1.0
-		batch_size = len(prompt) * num_samples_per_prompt
-
-		if classifier_free_guidance:
-			prompt_embeds, boolean_prompt_mask = self.encode_text_classifier_free(prompt, num_samples_per_prompt)
-			encoded_beats, beat_mask = self.encode_beats_classifier_free(beats, num_samples_per_prompt) #batch, len_beats, dim; batch, len_beats
-			encoded_chords, chord_mask = self.encode_chords_classifier_free(chords, chords_time, num_samples_per_prompt)
-		else:
-			prompt_embeds, boolean_prompt_mask = self.encode_text(prompt)
-			prompt_embeds = prompt_embeds.repeat_interleave(num_samples_per_prompt, 0)
-			boolean_prompt_mask = boolean_prompt_mask.repeat_interleave(num_samples_per_prompt, 0)
-
-			encoded_beats, beat_mask = self.encode_beats(beats) #batch, len_beats, dim; batch, len_beats
-			encoded_beats = encoded_beats.repeat_interleave(num_samples_per_prompt, 0)
-			beat_mask = beat_mask.repeat_interleave(num_samples_per_prompt, 0)
-
-			encoded_chords, chord_mask = self.encode_chords(chords,chords_time)
-			encoded_chords = encoded_chords.repeat_interleave(num_samples_per_prompt, 0)
-			chord_mask = chord_mask.repeat_interleave(num_samples_per_prompt, 0)
-
-		# print(f"encoded_chords:{encoded_chords.shape}, chord_mask:{chord_mask.shape}, prompt_embeds:{prompt_embeds.shape},boolean_prompt_mask:{boolean_prompt_mask.shape} ")
-		inference_scheduler.set_timesteps(num_steps, device=device)
-		timesteps = inference_scheduler.timesteps
-
-		num_channels_latents = self.unet.in_channels
-		latents = self.prepare_latents(batch_size, inference_scheduler, num_channels_latents, prompt_embeds.dtype, device)
-
-		num_warmup_steps = len(timesteps) - num_steps * inference_scheduler.order
-		progress_bar = tqdm(range(num_steps), disable=disable_progress)
-
-		for i, t in enumerate(timesteps):
-			# expand the latents if we are doing classifier free guidance
-			latent_model_input = torch.cat([latents] * 2) if classifier_free_guidance else latents
-			latent_model_input = inference_scheduler.scale_model_input(latent_model_input, t)
-
-			noise_pred = self.unet(
-				latent_model_input, t, encoder_hidden_states=prompt_embeds,
-				encoder_attention_mask=boolean_prompt_mask, 
-				beat_features = encoded_beats, beat_attention_mask = beat_mask, chord_features = encoded_chords,chord_attention_mask = chord_mask
-			).sample
-
-			# perform guidance
-			if classifier_free_guidance: #should work for beats and chords too
-				noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-				noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-			# compute the previous noisy sample x_t -> x_t-1
-			latents = inference_scheduler.step(noise_pred, t, latents).prev_sample
-
-			# call the callback, if provided
-			if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % inference_scheduler.order == 0):
-				progress_bar.update(1)
-
-		if self.set_from == "pre-trained":
-			latents = self.group_out(latents.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous()
-		return latents
-
-	def prepare_latents(self, batch_size, inference_scheduler, num_channels_latents, dtype, device):
-		shape = (batch_size, num_channels_latents, 256, 16)
-		latents = randn_tensor(shape, generator=None, device=device, dtype=dtype)
-		# scale the initial noise by the standard deviation required by the scheduler
-		latents = latents * inference_scheduler.init_noise_sigma
-		return latents
-
-	def encode_text_classifier_free(self, prompt, num_samples_per_prompt):
-		device = self.text_encoder.device
-		batch = self.tokenizer(
-			prompt, max_length=self.tokenizer.model_max_length, padding=True, truncation=True, return_tensors="pt"
-		)
-		input_ids, attention_mask = batch.input_ids.to(device), batch.attention_mask.to(device)
-
-		with torch.no_grad():
-			prompt_embeds = self.text_encoder(
-				input_ids=input_ids, attention_mask=attention_mask
-			)[0]
-				
-		prompt_embeds = prompt_embeds.repeat_interleave(num_samples_per_prompt, 0)
-		attention_mask = attention_mask.repeat_interleave(num_samples_per_prompt, 0)
-
-		# get unconditional embeddings for classifier free guidance
-		# print(len(prompt), 'this is prompt len')
-		uncond_tokens = [""] * len(prompt)
-
-		max_length = prompt_embeds.shape[1]
-		uncond_batch = self.tokenizer(
-			uncond_tokens, max_length=max_length, padding="max_length", truncation=True, return_tensors="pt",
-		)
-		uncond_input_ids = uncond_batch.input_ids.to(device)
-		uncond_attention_mask = uncond_batch.attention_mask.to(device)
-
-		with torch.no_grad():
-			negative_prompt_embeds = self.text_encoder(
-				input_ids=uncond_input_ids, attention_mask=uncond_attention_mask
-			)[0]
-				
-		negative_prompt_embeds = negative_prompt_embeds.repeat_interleave(num_samples_per_prompt, 0)
-		uncond_attention_mask = uncond_attention_mask.repeat_interleave(num_samples_per_prompt, 0)
-
-		# For classifier free guidance, we need to do two forward passes.
-		# We concatenate the unconditional and text embeddings into a single batch to avoid doing two forward passes
-		prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-		prompt_mask = torch.cat([uncond_attention_mask, attention_mask])
-		boolean_prompt_mask = (prompt_mask == 1).to(device)
-
-		return prompt_embeds, boolean_prompt_mask
-	
-
-	def encode_beats_classifier_free(self, beats, num_samples_per_prompt):
-		with torch.no_grad():
-			out_beat = []
-			out_beat_timing = []
-			out_mask = []
-			for beat in beats:
-				tokenized_beats,tokenized_beats_timing, tokenized_beat_mask = self.beat_tokenizer(beat)
-				out_beat.append(tokenized_beats)
-				out_beat_timing.append(tokenized_beats_timing)
-				out_mask.append(tokenized_beat_mask)
-			out_beat, out_beat_timing, out_mask = torch.tensor(out_beat).cuda(), torch.tensor(out_beat_timing).cuda(), torch.tensor(out_mask).cuda() #batch, len_beat
-			embedded_beat = self.beat_embedding_layer(out_beat, out_beat_timing)	
-			
-		embedded_beat = embedded_beat.repeat_interleave(num_samples_per_prompt, 0)
-		out_mask = out_mask.repeat_interleave(num_samples_per_prompt, 0)
-
-		uncond_beats = [[[],[]]] * len(beats)
-
-		max_length = embedded_beat.shape[1]
-		with torch.no_grad():
-			out_beat_unc = []
-			out_beat_timing_unc = []
-			out_mask_unc = []
-			for beat in uncond_beats:
-				tokenized_beats, tokenized_beats_timing, tokenized_beat_mask = self.beat_tokenizer(beat)
-				out_beat_unc.append(tokenized_beats)
-				out_beat_timing_unc.append(tokenized_beats_timing)
-				out_mask_unc.append(tokenized_beat_mask)
-			out_beat_unc, out_beat_timing_unc, out_mask_unc = torch.tensor(out_beat_unc).cuda(), torch.tensor(out_beat_timing_unc).cuda(), torch.tensor(out_mask_unc).cuda() #batch, len_beat
-			embedded_beat_unc = self.beat_embedding_layer(out_beat_unc, out_beat_timing_unc)
-
-		embedded_beat_unc = embedded_beat_unc.repeat_interleave(num_samples_per_prompt, 0)
-		out_mask_unc = out_mask_unc.repeat_interleave(num_samples_per_prompt, 0)
-
-		embedded_beat = torch.cat([embedded_beat_unc, embedded_beat])
-		out_mask = torch.cat([out_mask_unc, out_mask])
-
-		return embedded_beat, out_mask
-
-
-	def encode_chords_classifier_free(self, chords, chords_time, num_samples_per_prompt):
-
-		with torch.no_grad():
-			out_chord_root = []
-			out_chord_type = []
-			out_chord_inv = []
-			out_chord_timing = []
-			out_mask = []
-			for chord, chord_time in zip(chords,chords_time): #batch loop
-				tokenized_chord_root, tokenized_chord_type, tokenized_chord_inv, tokenized_chord_time, tokenized_chord_mask = self.chord_tokenizer(chord, chord_time)
-				out_chord_root.append(tokenized_chord_root)
-				out_chord_type.append(tokenized_chord_type)
-				out_chord_inv.append(tokenized_chord_inv)
-				out_chord_timing.append(tokenized_chord_time)
-				out_mask.append(tokenized_chord_mask)	
-			out_chord_root, out_chord_type, out_chord_inv, out_chord_timing, out_mask = torch.tensor(out_chord_root).cuda(), torch.tensor(out_chord_type).cuda(), torch.tensor(out_chord_inv).cuda(), torch.tensor(out_chord_timing).cuda(), torch.tensor(out_mask).cuda()
-			embedded_chord = self.chord_embedding_layer(out_chord_root, out_chord_type, out_chord_inv, out_chord_timing)
-		
-		embedded_chord = embedded_chord.repeat_interleave(num_samples_per_prompt, 0)
-		out_mask = out_mask.repeat_interleave(num_samples_per_prompt, 0)
-
-		chords_unc=[[]] * len(chords)
-		chords_time_unc=[[]] * len(chords_time)
-
-		max_length = embedded_chord.shape[1]
-
-		with torch.no_grad():
-			out_chord_root_unc = []
-			out_chord_type_unc = []
-			out_chord_inv_unc = []
-			out_chord_timing_unc = []
-			out_mask_unc = []
-			for chord, chord_time in zip(chords_unc,chords_time_unc): #batch loop
-				tokenized_chord_root, tokenized_chord_type, tokenized_chord_inv, tokenized_chord_time, tokenized_chord_mask = self.chord_tokenizer(chord, chord_time)
-				out_chord_root_unc.append(tokenized_chord_root)
-				out_chord_type_unc.append(tokenized_chord_type)
-				out_chord_inv_unc.append(tokenized_chord_inv)
-				out_chord_timing_unc.append(tokenized_chord_time)
-				out_mask_unc.append(tokenized_chord_mask)	
-			out_chord_root_unc, out_chord_type_unc, out_chord_inv_unc, out_chord_timing_unc, out_mask_unc = torch.tensor(out_chord_root_unc).cuda(), torch.tensor(out_chord_type_unc).cuda(), torch.tensor(out_chord_inv_unc).cuda(), torch.tensor(out_chord_timing_unc).cuda(), torch.tensor(out_mask_unc).cuda()
-			embedded_chord_unc = self.chord_embedding_layer(out_chord_root_unc, out_chord_type_unc, out_chord_inv_unc, out_chord_timing_unc)
-		
-
-		embedded_chord_unc = embedded_chord_unc.repeat_interleave(num_samples_per_prompt, 0)
-		out_mask_unc = out_mask_unc.repeat_interleave(num_samples_per_prompt, 0)
-
-		embedded_chord = torch.cat([embedded_chord_unc, embedded_chord])
-		out_mask = torch.cat([out_mask_unc, out_mask])
-
-		return embedded_chord, out_mask