






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


__magic_name__   :  List[str]        =							logging.get_logger(__name__)  # pylint: disable=invalid-name

__magic_name__   :  Tuple        =							256





class 					A__     (     __snake_case ):

     '''simple docstring'''






     snake_case__					=					["""melgan"""]







     def __init__( self      :    str   ,						_SCREAMING_SNAKE_CASE      :    SpectrogramNotesEncoder   ,						_SCREAMING_SNAKE_CASE      :    SpectrogramContEncoder   ,						_SCREAMING_SNAKE_CASE      :    TaFilmDecoder   ,						_SCREAMING_SNAKE_CASE      :    DDPMScheduler   ,						_SCREAMING_SNAKE_CASE      :    OnnxRuntimeModel if is_onnx_available() else Any   ,						):






           """simple docstring"""




           super().__init__()

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

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







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    str   ,						_SCREAMING_SNAKE_CASE      :    Any=(-1.0, 1.0)   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any]=False ):






           """simple docstring"""




           UpperCamelCase       ,  UpperCamelCase					       =  output_range
           if clip:
                 UpperCamelCase					       =  torch.clip(_SCREAMING_SNAKE_CASE   ,						self.min_value   ,						self.max_value )
           # Scale to [0, 1].
           UpperCamelCase					       =  (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   _SCREAMING_SNAKE_CASE     ( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    List[Any]=(-1.0, 1.0)   ,						_SCREAMING_SNAKE_CASE      :    str=False ):






           """simple docstring"""




           UpperCamelCase       ,  UpperCamelCase					       =  input_range
           UpperCamelCase					       =  torch.clip(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ) if clip else outputs
           # Scale to [0, 1].
           UpperCamelCase					       =  (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   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    Dict ):






           """simple docstring"""




           UpperCamelCase					       =  input_tokens > 0
           UpperCamelCase       ,  UpperCamelCase					       =  self.notes_encoder(
               encoder_input_tokens=_SCREAMING_SNAKE_CASE   ,						encoder_inputs_mask=_SCREAMING_SNAKE_CASE )

           UpperCamelCase       ,  UpperCamelCase					       =  self.continuous_encoder(
               encoder_inputs=_SCREAMING_SNAKE_CASE   ,						encoder_inputs_mask=_SCREAMING_SNAKE_CASE )

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







     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    Any   ,						_SCREAMING_SNAKE_CASE      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any] ):






           """simple docstring"""




           UpperCamelCase					       =  noise_time
           if not torch.is_tensor(_SCREAMING_SNAKE_CASE ):
                 UpperCamelCase					       =  torch.tensor([timesteps]   ,						dtype=torch.long   ,						device=input_tokens.device )
           elif torch.is_tensor(_SCREAMING_SNAKE_CASE ) and len(timesteps.shape ) == 0:
                 UpperCamelCase					       =  timesteps[None].to(input_tokens.device )

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

           UpperCamelCase					       =  self.decoder(
               encodings_and_masks=_SCREAMING_SNAKE_CASE   ,						decoder_input_tokens=_SCREAMING_SNAKE_CASE   ,						decoder_noise_time=_SCREAMING_SNAKE_CASE )
           return logits







     @torch.no_grad()
     def __call__( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    List[List[int]]   ,						_SCREAMING_SNAKE_CASE      :    Optional[torch.Generator] = None   ,						_SCREAMING_SNAKE_CASE      :    int = 100   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						_SCREAMING_SNAKE_CASE      :    str = "numpy"   ,						_SCREAMING_SNAKE_CASE      :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None   ,						_SCREAMING_SNAKE_CASE      :    int = 1   ,						):






           """simple docstring"""




           if (callback_steps is None) or (
               callback_steps is not None and (not isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_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(_SCREAMING_SNAKE_CASE )}.' )

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

           for i, encoder_input_tokens in enumerate(_SCREAMING_SNAKE_CASE ):
                 if i == 0:
                       UpperCamelCase					       =  torch.from_numpy(pred_mel[:1].copy() ).to(
                           device=self.device   ,						dtype=self.decoder.dtype )
                       # The first chunk has no previous context.
                       UpperCamelCase					       =  torch.zeros((1, TARGET_FEATURE_LENGTH)   ,						dtype=_SCREAMING_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.
                       UpperCamelCase					       =  ones

                 UpperCamelCase					       =  self.scale_features(
                     _SCREAMING_SNAKE_CASE   ,						output_range=[-1.0, 1.0]   ,						clip=_SCREAMING_SNAKE_CASE )

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

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

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

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

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

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

                 UpperCamelCase					       =  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(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

                 logger.info('Generated segment'   ,						_SCREAMING_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":
                 UpperCamelCase					       =  self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
           else:
                 UpperCamelCase					       =  full_pred_mel

           if not return_dict:
                 return (output,)

           return AudioPipelineOutput(audios=_SCREAMING_SNAKE_CASE )











from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
       import datasets





class 					A__     (     __snake_case ):

     '''simple docstring'''






     @slow
     @require_torch
     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           UpperCamelCase					       =  EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'   ,						'prajjwal1/bert-tiny' )
           UpperCamelCase					       =  BertTokenizer.from_pretrained('bert-base-uncased' )

           UpperCamelCase					       =  bertabert.config.encoder.vocab_size
           UpperCamelCase					       =  tokenizer.sep_token_id
           UpperCamelCase					       =  tokenizer.cls_token_id
           UpperCamelCase					       =  128

           UpperCamelCase					       =  datasets.load_dataset('cnn_dailymail'   ,						'3.0.0'   ,						split='train[:1%]' )
           UpperCamelCase					       =  datasets.load_dataset('cnn_dailymail'   ,						'3.0.0'   ,						split='validation[:1%]' )

           UpperCamelCase					       =  train_dataset.select(range(32 ) )
           UpperCamelCase					       =  val_dataset.select(range(16 ) )

           UpperCamelCase					       =  4

           def _map_to_encoder_decoder_inputs(_SCREAMING_SNAKE_CASE      :    Tuple ):
                 # Tokenizer will automatically set [BOS] <text> [EOS]
                 UpperCamelCase					       =  tokenizer(batch['article']   ,						padding='max_length'   ,						truncation=_SCREAMING_SNAKE_CASE   ,						max_length=512 )
                 UpperCamelCase					       =  tokenizer(batch['highlights']   ,						padding='max_length'   ,						truncation=_SCREAMING_SNAKE_CASE   ,						max_length=128 )
                 UpperCamelCase					       =  inputs.input_ids
                 UpperCamelCase					       =  inputs.attention_mask

                 UpperCamelCase					       =  outputs.input_ids
                 UpperCamelCase					       =  outputs.input_ids.copy()
                 UpperCamelCase					       =  [
                     [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
                 ]
                 UpperCamelCase					       =  outputs.attention_mask

                 assert all(len(_SCREAMING_SNAKE_CASE ) == 512 for x in inputs.input_ids )
                 assert all(len(_SCREAMING_SNAKE_CASE ) == 128 for x in outputs.input_ids )

                 return batch

           def _compute_metrics(_SCREAMING_SNAKE_CASE      :    str ):
                 UpperCamelCase					       =  pred.label_ids
                 UpperCamelCase					       =  pred.predictions

                 # all unnecessary tokens are removed
                 UpperCamelCase					       =  tokenizer.batch_decode(_SCREAMING_SNAKE_CASE   ,						skip_special_tokens=_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  tokenizer.batch_decode(_SCREAMING_SNAKE_CASE   ,						skip_special_tokens=_SCREAMING_SNAKE_CASE )

                 UpperCamelCase					       =  sum([int(pred_str[i] == label_str[i] ) for i in range(len(_SCREAMING_SNAKE_CASE ) )] ) / len(_SCREAMING_SNAKE_CASE )

                 return {"accuracy": accuracy}

           # map train dataset
           UpperCamelCase					       =  train_dataset.map(
               _map_to_encoder_decoder_inputs   ,						batched=_SCREAMING_SNAKE_CASE   ,						batch_size=_SCREAMING_SNAKE_CASE   ,						remove_columns=['article', 'highlights']   ,						)
           train_dataset.set_format(
               type='torch'   ,						columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']   ,						)

           # same for validation dataset
           UpperCamelCase					       =  val_dataset.map(
               _map_to_encoder_decoder_inputs   ,						batched=_SCREAMING_SNAKE_CASE   ,						batch_size=_SCREAMING_SNAKE_CASE   ,						remove_columns=['article', 'highlights']   ,						)
           val_dataset.set_format(
               type='torch'   ,						columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']   ,						)

           UpperCamelCase					       =  self.get_auto_remove_tmp_dir()

           UpperCamelCase					       =  SeqaSeqTrainingArguments(
               output_dir=_SCREAMING_SNAKE_CASE   ,						per_device_train_batch_size=_SCREAMING_SNAKE_CASE   ,						per_device_eval_batch_size=_SCREAMING_SNAKE_CASE   ,						predict_with_generate=_SCREAMING_SNAKE_CASE   ,						evaluation_strategy='steps'   ,						do_train=_SCREAMING_SNAKE_CASE   ,						do_eval=_SCREAMING_SNAKE_CASE   ,						warmup_steps=0   ,						eval_steps=2   ,						logging_steps=2   ,						)

           # instantiate trainer
           UpperCamelCase					       =  SeqaSeqTrainer(
               model=_SCREAMING_SNAKE_CASE   ,						args=_SCREAMING_SNAKE_CASE   ,						compute_metrics=_compute_metrics   ,						train_dataset=_SCREAMING_SNAKE_CASE   ,						eval_dataset=_SCREAMING_SNAKE_CASE   ,						tokenizer=_SCREAMING_SNAKE_CASE   ,						)

           # start training
           trainer.train()



