


'''simple docstring'''







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__       (						__A      ):






      @slow
      @require_torch
      def __UpperCAmelCase       ( self			:Optional[Any]	)       ->					Optional[Any]:




             '''simple docstring'''
             _a    : List[Any]     					=EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""     ,						"""prajjwal1/bert-tiny"""	)
             _a    : str     					=BertTokenizer.from_pretrained("""bert-base-uncased"""	)

             _a    : Tuple     					=bertabert.config.encoder.vocab_size
             _a    : Tuple     					=tokenizer.sep_token_id
             _a    : Dict     					=tokenizer.cls_token_id
             _a    : Optional[int]     					=1_2_8

             _a    : List[str]     					=datasets.load_dataset("""cnn_dailymail"""     ,						"""3.0.0"""     ,						split="""train[:1%]"""	)
             _a    : Any     					=datasets.load_dataset("""cnn_dailymail"""     ,						"""3.0.0"""     ,						split="""validation[:1%]"""	)

             _a    : List[Any]     					=train_dataset.select(range(3_2	)	)
             _a    : str     					=val_dataset.select(range(1_6	)	)

             _a    : Union[str, Any]     					=4

             def _map_to_encoder_decoder_inputs(SCREAMING_SNAKE_CASE			:List[str]	):
                    # Tokenizer will automatically set [BOS] <text> [EOS]
                    _a    : Union[str, Any]     					=tokenizer(batch["""article"""]     ,						padding="""max_length"""     ,						truncation=lowercase_     ,						max_length=5_1_2	)
                    _a    : str     					=tokenizer(batch["""highlights"""]     ,						padding="""max_length"""     ,						truncation=lowercase_     ,						max_length=1_2_8	)
                    _a    : Tuple     					=inputs.input_ids
                    _a    : int     					=inputs.attention_mask

                    _a    : Optional[int]     					=outputs.input_ids
                    _a    : Union[str, Any]     					=outputs.input_ids.copy()
                    _a    : str     					=[
                        [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
                    ]
                    _a    : Union[str, Any]     					=outputs.attention_mask

                    assert all(len(lowercase_	) == 5_1_2 for x in inputs.input_ids	)
                    assert all(len(lowercase_	) == 1_2_8 for x in outputs.input_ids	)

                    return batch

             def _compute_metrics(SCREAMING_SNAKE_CASE			:List[str]	):
                    _a    : List[str]     					=pred.label_ids
                    _a    : int     					=pred.predictions

                    # all unnecessary tokens are removed
                    _a    : Tuple     					=tokenizer.batch_decode(lowercase_     ,						skip_special_tokens=lowercase_	)
                    _a    : List[str]     					=tokenizer.batch_decode(lowercase_     ,						skip_special_tokens=lowercase_	)

                    _a    : Optional[int]     					=sum([int(pred_str[i] == label_str[i]	) for i in range(len(lowercase_	)	)]	) / len(lowercase_	)

                    return {"accuracy": accuracy}

             # map train dataset
             _a    : str     					=train_dataset.map(
                 _map_to_encoder_decoder_inputs     ,						batched=lowercase_     ,						batch_size=lowercase_     ,						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
             _a    : Dict     					=val_dataset.map(
                 _map_to_encoder_decoder_inputs     ,						batched=lowercase_     ,						batch_size=lowercase_     ,						remove_columns=["""article""", """highlights"""]     ,						)
             val_dataset.set_format(
                 type="""torch"""     ,						columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]     ,						)

             _a    : str     					=self.get_auto_remove_tmp_dir()

             _a    : Any     					=SeqaSeqTrainingArguments(
                 output_dir=lowercase_     ,						per_device_train_batch_size=lowercase_     ,						per_device_eval_batch_size=lowercase_     ,						predict_with_generate=lowercase_     ,						evaluation_strategy="""steps"""     ,						do_train=lowercase_     ,						do_eval=lowercase_     ,						warmup_steps=0     ,						eval_steps=2     ,						logging_steps=2     ,						)

             # instantiate trainer
             _a    : Union[str, Any]     					=SeqaSeqTrainer(
                 model=lowercase_     ,						args=lowercase_     ,						compute_metrics=_compute_metrics     ,						train_dataset=lowercase_     ,						eval_dataset=lowercase_     ,						tokenizer=lowercase_     ,						)

             # start training
             trainer.train()



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


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

UpperCamelCase  							=						256



class   snake_case_       (		__A       ):
   __A	:		str         =				["melgan"]






   def __init__(      self				:     str	,						lowercase_				:     SpectrogramNotesEncoder	,						lowercase_				:     SpectrogramContEncoder	,						lowercase_				:     TaFilmDecoder	,						lowercase_				:     DDPMScheduler	,						lowercase_				:     OnnxRuntimeModel if is_onnx_available() else Any	,						)					->						None:
       super().__init__()

       # From MELGAN
       lowercase__ :   List[Any]    					=				math.log(1E-5  )  # Matches MelGAN training.
       lowercase__ :   str    					=				4.0  # Largest value for most examples
       lowercase__ :   Any    					=				1_28

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






   def       __UpperCamelCase     (      self				:     Union[str, Any]	,						lowercase_				:     Union[str, Any]	,						lowercase_				:     List[str]=(-1.0, 1.0)	,						lowercase_				:     Dict=False  )					->						Optional[Any]:
       lowercase__					,   lowercase__ :   int    					=				output_range
       if clip:
           lowercase__ :   Optional[Any]    					=				torch.clip(lowercase_	,						self.min_value	,						self.max_value  )
       # Scale to [0, 1].
       lowercase__ :   List[str]    					=				(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				:     Optional[int]	,						lowercase_				:     List[str]	,						lowercase_				:     List[str]=(-1.0, 1.0)	,						lowercase_				:     List[Any]=False  )					->						Union[str, Any]:
       lowercase__					,   lowercase__ :   Tuple    					=				input_range
       lowercase__ :   Optional[Any]    					=				torch.clip(lowercase_	,						lowercase_	,						lowercase_  ) if clip else outputs
       # Scale to [0, 1].
       lowercase__ :   Union[str, Any]    					=				(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				:     List[str]	,						lowercase_				:     Any	,						lowercase_				:     Optional[Any]	,						lowercase_				:     Tuple  )					->						List[str]:
       lowercase__ :   Optional[Any]    					=				input_tokens > 0
       lowercase__					,   lowercase__ :   int    					=				self.notes_encoder(
           encoder_input_tokens=lowercase_	,						encoder_inputs_mask=lowercase_  )

       lowercase__					,   lowercase__ :   List[Any]    					=				self.continuous_encoder(
           encoder_inputs=lowercase_	,						encoder_inputs_mask=lowercase_  )

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






   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     Optional[int]	,						lowercase_				:     Optional[int]	,						lowercase_				:     str  )					->						Tuple:
       lowercase__ :   Union[str, Any]    					=				noise_time
       if not torch.is_tensor(lowercase_  ):
           lowercase__ :   Optional[Any]    					=				torch.tensor([timesteps]	,						dtype=torch.long	,						device=input_tokens.device  )
       elif torch.is_tensor(lowercase_  ) and len(timesteps.shape  ) == 0:
           lowercase__ :   Optional[Any]    					=				timesteps[None].to(input_tokens.device  )

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

       lowercase__ :   str    					=				self.decoder(
           encodings_and_masks=lowercase_	,						decoder_input_tokens=lowercase_	,						decoder_noise_time=lowercase_  )
       return logits







   @torch.no_grad()
   def __call__(      self				:     List[str]	,						lowercase_				:     List[List[int]]	,						lowercase_				:     Optional[torch.Generator] = None	,						lowercase_				:     int = 1_00	,						lowercase_				:     bool = True	,						lowercase_				:     str = "numpy"	,						lowercase_				:     Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,						lowercase_				:     int = 1	,						)					->						Union[AudioPipelineOutput, Tuple]:
       if (callback_steps is None) or (
           callback_steps is not None and (not isinstance(lowercase_	,						lowercase_  ) or callback_steps <= 0)
       ):
           raise ValueError(
               F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
               F''' {type(lowercase_  )}.'''  )

       lowercase__ :   str    					=				np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]	,						dtype=np.floataa  )
       lowercase__ :   Optional[int]    					=				np.zeros([1, 0, self.n_dims]	,						np.floataa  )
       lowercase__ :   str    					=				torch.ones((1, TARGET_FEATURE_LENGTH)	,						dtype=lowercase_	,						device=self.device  )

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

           lowercase__ :   str    					=				self.scale_features(
               lowercase_	,						output_range=[-1.0, 1.0]	,						clip=lowercase_  )

           lowercase__ :   str    					=				self.encode(
               input_tokens=torch.IntTensor([encoder_input_tokens]  ).to(device=self.device  )	,						continuous_inputs=lowercase_	,						continuous_mask=lowercase_	,						)

           # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
           lowercase__ :   List[str]    					=				randn_tensor(
               shape=encoder_continuous_inputs.shape	,						generator=lowercase_	,						device=self.device	,						dtype=self.decoder.dtype	,						)

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

           # Denoising diffusion loop
           for j, t in enumerate(self.progress_bar(self.scheduler.timesteps  )  ):
               lowercase__ :   Optional[int]    					=				self.decode(
                   encodings_and_masks=lowercase_	,						input_tokens=lowercase_	,						noise_time=t / self.scheduler.config.num_train_timesteps	,						)

               # Compute previous output: x_t -> x_t-1
               lowercase__ :   Optional[Any]    					=				self.scheduler.step(lowercase_	,						lowercase_	,						lowercase_	,						generator=lowercase_  ).prev_sample

           lowercase__ :   Tuple    					=				self.scale_to_features(lowercase_	,						input_range=[-1.0, 1.0]  )
           lowercase__ :   List[str]    					=				mel[:1]
           lowercase__ :   Optional[int]    					=				mel.cpu().float().numpy()

           lowercase__ :   str    					=				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(lowercase_	,						lowercase_  )

           logger.info("Generated segment"	,						lowercase_  )

       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":
           lowercase__ :   Union[str, Any]    					=				self.melgan(input_features=full_pred_mel.astype(np.floataa  )  )
       else:
           lowercase__ :   Dict    					=				full_pred_mel

       if not return_dict:
           return (output,)

       return AudioPipelineOutput(audios=lowercase_  )



