

import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
 from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


__A      		=     logging.get_logger(__name__)  # pylint: disable=invalid-name

__A      		=     2_56



class   lowercase_   (  _a					):
      UpperCamelCase_      :		int										=    ["melgan"]



      def __init__(      self				:    Dict      , A__				:    SpectrogramNotesEncoder      , A__				:    SpectrogramContEncoder      , A__				:    TaFilmDecoder      , A__				:    DDPMScheduler      , A__				:    OnnxRuntimeModel if is_onnx_available() else Any      , )   ->					List[Any]:
        super().__init__()

        # From MELGAN
        _snake_case						=							math.log(1e-5		)  # Matches MelGAN training.
        _snake_case						=							4.0  # Largest value for most examples
        _snake_case						=							128

        self.register_modules(
            notes_encoder=snake_case_      , continuous_encoder=snake_case_      , decoder=snake_case_      , scheduler=snake_case_      , melgan=snake_case_      , )



      def 						UpperCamelCase_		(      self				:    Optional[Any]      , A__				:    Union[str, Any]      , A__				:    Tuple=(-1.0, 1.0)      , A__				:    Optional[Any]=False		)   ->					Optional[Any]:
        _snake_case						=							output_range
        if clip:
          _snake_case						=							torch.clip(snake_case_      , self.min_value      , self.max_value		)
        # Scale to [0, 1].
        _snake_case						=							(features - self.min_value) / (self.max_value - self.min_value)
        # Scale to [min_out, max_out].
        return zero_one * (max_out - min_out) + min_out



      def 						UpperCamelCase_		(      self				:    Union[str, Any]      , A__				:    Optional[int]      , A__				:    List[str]=(-1.0, 1.0)      , A__				:    Any=False		)   ->					Optional[int]:
        _snake_case						=							input_range
        _snake_case						=							torch.clip(snake_case_      , snake_case_      , snake_case_		) if clip else outputs
        # Scale to [0, 1].
        _snake_case						=							(outputs - min_out) / (max_out - min_out)
        # Scale to [self.min_value, self.max_value].
        return zero_one * (self.max_value - self.min_value) + self.min_value



      def 						UpperCamelCase_		(      self				:    Dict      , A__				:    Tuple      , A__				:    List[str]      , A__				:    Dict		)   ->					Tuple:
        _snake_case						=							input_tokens > 0
        _snake_case						=							self.notes_encoder(
            encoder_input_tokens=snake_case_      , encoder_inputs_mask=snake_case_		)

        _snake_case						=							self.continuous_encoder(
            encoder_inputs=snake_case_      , encoder_inputs_mask=snake_case_		)

        return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]



      def 						UpperCamelCase_		(      self				:    List[Any]      , A__				:    Optional[Any]      , A__				:    List[str]      , A__				:    Optional[int]		)   ->					List[str]:
        _snake_case						=							noise_time
        if not torch.is_tensor(snake_case_		):
          _snake_case						=							torch.tensor([timesteps]      , dtype=torch.long      , device=input_tokens.device		)
        elif torch.is_tensor(snake_case_		) and len(timesteps.shape		) == 0:
          _snake_case						=							timesteps[None].to(input_tokens.device		)

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        _snake_case						=							timesteps * torch.ones(input_tokens.shape[0]      , dtype=timesteps.dtype      , device=timesteps.device		)

        _snake_case						=							self.decoder(
            encodings_and_masks=snake_case_      , decoder_input_tokens=snake_case_      , decoder_noise_time=snake_case_		)
        return logits







      @torch.no_grad()
      def __call__(      self				:    Tuple      , A__				:    List[List[int]]      , A__				:    Optional[torch.Generator] = None      , A__				:    int = 100      , A__				:    bool = True      , A__				:    str = "numpy"      , A__				:    Optional[Callable[[int, int, torch.FloatTensor], None]] = None      , A__				:    int = 1      , )   ->					int:
        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(snake_case_      , snake_case_		) or callback_steps <= 0)
        ):
          raise ValueError(
              f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
              f""" {type(snake_case_		)}."""		)

        _snake_case						=							np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]      , dtype=np.floataa		)
        _snake_case						=							np.zeros([1, 0, self.n_dims]      , np.floataa		)
        _snake_case						=							torch.ones((1, TARGET_FEATURE_LENGTH)      , dtype=snake_case_      , device=self.device		)

        for i, encoder_input_tokens in enumerate(snake_case_		):
          if i == 0:
            _snake_case						=							torch.from_numpy(pred_mel[:1].copy()		).to(
                device=self.device      , dtype=self.decoder.dtype		)
            # The first chunk has no previous context.
            _snake_case						=							torch.zeros((1, TARGET_FEATURE_LENGTH)      , dtype=snake_case_      , device=self.device		)
          else:
            # The full song pipeline does not feed in a context feature, so the mask
            # will be all 0s after the feature converter. Because we know we're
            # feeding in a full context chunk from the previous prediction, set it
            # to all 1s.
            _snake_case						=							ones

          _snake_case						=							self.scale_features(
              snake_case_      , output_range=[-1.0, 1.0]      , clip=snake_case_		)

          _snake_case						=							self.encode(
              input_tokens=torch.IntTensor([encoder_input_tokens]		).to(device=self.device		)      , continuous_inputs=snake_case_      , continuous_mask=snake_case_      , )

          # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
          _snake_case						=							randn_tensor(
              shape=encoder_continuous_inputs.shape      , generator=snake_case_      , device=self.device      , dtype=self.decoder.dtype      , )

          # set step values
          self.scheduler.set_timesteps(snake_case_		)

          # Denoising diffusion loop
          for j, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
            _snake_case						=							self.decode(
                encodings_and_masks=snake_case_      , input_tokens=snake_case_      , noise_time=t / self.scheduler.config.num_train_timesteps      , )

            # Compute previous output: x_t -> x_t-1
            _snake_case						=							self.scheduler.step(snake_case_      , snake_case_      , snake_case_      , generator=snake_case_		).prev_sample

          _snake_case						=							self.scale_to_features(snake_case_      , input_range=[-1.0, 1.0]		)
          _snake_case						=							mel[:1]
          _snake_case						=							mel.cpu().float().numpy()

          _snake_case						=							np.concatenate([full_pred_mel, pred_mel[:1]]      , axis=1		)

          # call the callback, if provided
          if callback is not None and i % callback_steps == 0:
            callback(snake_case_      , snake_case_		)

          logger.info('''Generated segment'''      , snake_case_		)

        if output_type == "numpy" and not is_onnx_available():
          raise ValueError(
              '''Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.'''		)
        elif output_type == "numpy" and self.melgan is None:
          raise ValueError(
              '''Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.'''		)

        if output_type == "numpy":
          _snake_case						=							self.melgan(input_features=full_pred_mel.astype(np.floataa		)		)
        else:
          _snake_case						=							full_pred_mel

        if not return_dict:
          return (output,)

        return AudioPipelineOutput(audios=snake_case_		)







import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)



def 				snake_case_(_UpperCamelCase					)							->     List[Any]:



  """simple docstring"""






  _snake_case						=							torch.exp(_UpperCamelCase					)
  _snake_case						=							torch.sum(_UpperCamelCase			,	dim=1					)  # sum of exp(x_i)
  _snake_case						=							torch.sum(x * exp_x			,	dim=1					)  # sum of x_i * exp(x_i)
  return torch.log(_UpperCamelCase					) - B / A



class   lowercase_   (  nn.Module					):



      def __init__(      self				:    Tuple      , A__				:    int		)   ->					Tuple:
        super().__init__()
        _snake_case						=							config.output_attentions
        _snake_case						=							config.output_hidden_states
        _snake_case						=							nn.ModuleList([BertLayer(A__		) for _ in range(config.num_hidden_layers		)]		)
        _snake_case						=							nn.ModuleList([BertHighway(A__		) for _ in range(config.num_hidden_layers		)]		)

        _snake_case						=							[-1 for _ in range(config.num_hidden_layers		)]



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    Any		)   ->					Any:
        if (type(A__		) is float) or (type(A__		) is int):
          for i in range(len(self.early_exit_entropy		)		):
            _snake_case						=							x
        else:
          _snake_case						=							x



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    Tuple		)   ->					int:
        _snake_case						=							pooler.state_dict()
        for highway in self.highway:
          for name, param in highway.pooler.state_dict().items():
            param.copy_(loaded_model[name]		)







      def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    Optional[int]      , A__				:    Dict=None      , A__				:    List[str]=None      , A__				:    Union[str, Any]=None      , A__				:    Dict=None      , )   ->					Dict:
        _snake_case						=							()
        _snake_case						=							()
        _snake_case						=							()
        for i, layer_module in enumerate(self.layer		):
          if self.output_hidden_states:
            _snake_case						=							all_hidden_states + (hidden_states,)

          _snake_case						=							layer_module(
              A__      , A__      , head_mask[i]      , A__      , A__		)
          _snake_case						=							layer_outputs[0]

          if self.output_attentions:
            _snake_case						=							all_attentions + (layer_outputs[1],)

          _snake_case						=							(hidden_states,)
          if self.output_hidden_states:
            _snake_case						=							current_outputs + (all_hidden_states,)
          if self.output_attentions:
            _snake_case						=							current_outputs + (all_attentions,)

          _snake_case						=							self.highway[i](A__		)
          # logits, pooled_output

          if not self.training:
            _snake_case						=							highway_exit[0]
            _snake_case						=							entropy(A__		)
            _snake_case						=							highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
            _snake_case						=							all_highway_exits + (highway_exit,)

            if highway_entropy < self.early_exit_entropy[i]:
              _snake_case						=							(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
              raise HighwayException(A__      , i + 1		)
          else:
            _snake_case						=							all_highway_exits + (highway_exit,)

        # Add last layer
        if self.output_hidden_states:
          _snake_case						=							all_hidden_states + (hidden_states,)

        _snake_case						=							(hidden_states,)
        if self.output_hidden_states:
          _snake_case						=							outputs + (all_hidden_states,)
        if self.output_attentions:
          _snake_case						=							outputs + (all_attentions,)

        _snake_case						=							outputs + (all_highway_exits,)
        return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits



@add_start_docstrings(
    "The Bert Model transformer with early exiting (DeeBERT). "       ,							__lowercase       ,							)
class   lowercase_   (  __lowercase					):



      def __init__(      self				:    Optional[Any]      , A__				:    Any		)   ->					str:
        super().__init__(A__		)
        _snake_case						=							config

        _snake_case						=							BertEmbeddings(A__		)
        _snake_case						=							DeeBertEncoder(A__		)
        _snake_case						=							BertPooler(A__		)

        self.init_weights()



      def 						UpperCamelCase_		(      self				:    Tuple		)   ->					Optional[Any]:
        self.encoder.init_highway_pooler(self.pooler		)



      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Tuple:
        return self.embeddings.word_embeddings



      def 						UpperCamelCase_		(      self				:    Optional[Any]      , A__				:    str		)   ->					str:
        _snake_case						=							value



      def 						UpperCamelCase_		(      self				:    Union[str, Any]      , A__				:    List[Any]		)   ->					Any:
        for layer, heads in heads_to_prune.items():
          self.encoder.layer[layer].attention.prune_heads(A__		)







      @add_start_docstrings_to_model_forward(A__		)
      def 						UpperCamelCase_		(      self				:    int      , A__				:    Tuple=None      , A__				:    Union[str, Any]=None      , A__				:    Union[str, Any]=None      , A__				:    Optional[Any]=None      , A__				:    Dict=None      , A__				:    Any=None      , A__				:    str=None      , A__				:    Optional[int]=None      , )   ->					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:
          _snake_case						=							input_ids.size()
        elif inputs_embeds is not None:
          _snake_case						=							inputs_embeds.size()[:-1]
        else:
          raise ValueError('''You have to specify either input_ids or inputs_embeds'''		)

        _snake_case						=							input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
          _snake_case						=							torch.ones(A__      , device=A__		)
        if encoder_attention_mask is None:
          _snake_case						=							torch.ones(A__      , device=A__		)
        if token_type_ids is None:
          _snake_case						=							torch.zeros(A__      , dtype=torch.long      , device=A__		)

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        _snake_case						=							self.get_extended_attention_mask(A__      , A__      , A__		)

        # If a 2D ou 3D attention mask is provided for the cross-attention
        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
        if encoder_attention_mask.dim() == 3:
          _snake_case						=							encoder_attention_mask[:, None, :, :]
        if encoder_attention_mask.dim() == 2:
          _snake_case						=							encoder_attention_mask[:, None, None, :]

        _snake_case						=							encoder_extended_attention_mask.to(
            dtype=next(self.parameters()		).dtype		)  # fp16 compatibility
        _snake_case						=							(1.0 - encoder_extended_attention_mask) * -1_0000.0

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        _snake_case						=							self.get_head_mask(A__      , self.config.num_hidden_layers		)

        _snake_case						=							self.embeddings(
            input_ids=A__      , position_ids=A__      , token_type_ids=A__      , inputs_embeds=A__		)
        _snake_case						=							self.encoder(
            A__      , attention_mask=A__      , head_mask=A__      , encoder_hidden_states=A__      , encoder_attention_mask=A__      , )
        _snake_case						=							encoder_outputs[0]
        _snake_case						=							self.pooler(A__		)

        _snake_case						=							(
            sequence_output,
            pooled_output,
        ) + encoder_outputs[
            1:
        ]  # add hidden_states and attentions if they are here
        return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits



class   lowercase_   (  __lowercase					):



      def __init__(      self				:    Union[str, Any]      , A__				:    Dict      , A__				:    Optional[Any]		)   ->					List[str]:
        _snake_case						=							message
        _snake_case						=							exit_layer  # start from 1!



class   lowercase_   (  nn.Module					):



      def __init__(      self				:    Any      , A__				:    int		)   ->					Optional[Any]:
        super().__init__()
        _snake_case						=							BertPooler(A__		)
        _snake_case						=							nn.Dropout(config.hidden_dropout_prob		)
        _snake_case						=							nn.Linear(config.hidden_size      , config.num_labels		)







      def 						UpperCamelCase_		(      self				:    Optional[Any]      , A__				:    str		)   ->					Optional[int]:
        # Pooler
        _snake_case						=							encoder_outputs[0]
        _snake_case						=							self.pooler(A__		)
        # "return" pooler_output

        # BertModel
        _snake_case						=							(pooler_input, pooler_output) + encoder_outputs[1:]
        # "return" bmodel_output

        # Dropout and classification
        _snake_case						=							bmodel_output[1]

        _snake_case						=							self.dropout(A__		)
        _snake_case						=							self.classifier(A__		)

        return logits, pooled_output



@add_start_docstrings(
    "Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. "       ,							__lowercase       ,							)
class   lowercase_   (  __lowercase					):



      def __init__(      self				:    List[str]      , A__				:    Optional[int]		)   ->					int:
        super().__init__(A__		)
        _snake_case						=							config.num_labels
        _snake_case						=							config.num_hidden_layers

        _snake_case						=							DeeBertModel(A__		)
        _snake_case						=							nn.Dropout(config.hidden_dropout_prob		)
        _snake_case						=							nn.Linear(config.hidden_size      , self.config.num_labels		)

        self.init_weights()







      @add_start_docstrings_to_model_forward(A__		)
      def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    Optional[Any]=None      , A__				:    List[Any]=None      , A__				:    Optional[int]=None      , A__				:    List[Any]=None      , A__				:    List[Any]=None      , A__				:    Union[str, Any]=None      , A__				:    Union[str, Any]=None      , A__				:    List[Any]=-1      , A__				:    str=False      , )   ->					Dict:
        _snake_case						=							self.num_layers
        try:
          _snake_case						=							self.bert(
              A__      , attention_mask=A__      , token_type_ids=A__      , position_ids=A__      , head_mask=A__      , inputs_embeds=A__      , )
          # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

          _snake_case						=							outputs[1]

          _snake_case						=							self.dropout(A__		)
          _snake_case						=							self.classifier(A__		)
          _snake_case						=							(logits,) + outputs[2:]  # add hidden states and attention if they are here
        except HighwayException as e:
          _snake_case						=							e.message
          _snake_case						=							e.exit_layer
          _snake_case						=							outputs[0]

        if not self.training:
          _snake_case						=							entropy(A__		)
          _snake_case						=							[]
          _snake_case						=							[]
        if labels is not None:
          if self.num_labels == 1:
            #  We are doing regression
            _snake_case						=							MSELoss()
            _snake_case						=							loss_fct(logits.view(-1		)      , labels.view(-1		)		)
          else:
            _snake_case						=							CrossEntropyLoss()
            _snake_case						=							loss_fct(logits.view(-1      , self.num_labels		)      , labels.view(-1		)		)

          # work with highway exits
          _snake_case						=							[]
          for highway_exit in outputs[-1]:
            _snake_case						=							highway_exit[0]
            if not self.training:
              highway_logits_all.append(A__		)
              highway_entropy.append(highway_exit[2]		)
            if self.num_labels == 1:
              #  We are doing regression
              _snake_case						=							MSELoss()
              _snake_case						=							loss_fct(highway_logits.view(-1		)      , labels.view(-1		)		)
            else:
              _snake_case						=							CrossEntropyLoss()
              _snake_case						=							loss_fct(highway_logits.view(-1      , self.num_labels		)      , labels.view(-1		)		)
            highway_losses.append(A__		)

          if train_highway:
            _snake_case						=							(sum(highway_losses[:-1]		),) + outputs
            # exclude the final highway, of course
          else:
            _snake_case						=							(loss,) + outputs
        if not self.training:
          _snake_case						=							outputs + ((original_entropy, highway_entropy), exit_layer)
          if output_layer >= 0:
            _snake_case						=							(
                (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
            )  # use the highway of the last layer

        return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)

