


'''simple docstring'''







import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

A__:    Tuple					    =       logging.get_logger(__name__)


A__:    Optional[Any]					    =       {name: getattr(transformers, name + '''Fast''') for name in SLOW_TO_FAST_CONVERTERS}


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Tuple    ,_UpperCAmelCase	:	Any    ,_UpperCAmelCase	:	Union[str, Any]    ,_UpperCAmelCase	:	int  )  ->			List[Any]:
       if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
              raise ValueError(F"Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()  )}."  )

       if tokenizer_name is None:
              _a    : Optional[Any]     					=TOKENIZER_CLASSES
       else:
              _a    : int     					={tokenizer_name: getattr(_lowerCamelCase    ,tokenizer_name + """Fast"""  )}

       logger.info(F"Loading tokenizer classes: {tokenizer_names}"  )

       for tokenizer_name in tokenizer_names:
              _a    : Union[str, Any]     					=TOKENIZER_CLASSES[tokenizer_name]

              _a    : Optional[Any]     					=True
              if checkpoint_name is None:
                     _a    : str     					=list(tokenizer_class.max_model_input_sizes.keys()  )
              else:
                     _a    : Optional[Any]     					=[checkpoint_name]

              logger.info(F"For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}"  )

              for checkpoint in checkpoint_names:
                     logger.info(F"Loading {tokenizer_class.__class__.__name__} {checkpoint}"  )

                     # Load tokenizer
                     _a    : Optional[Any]     					=tokenizer_class.from_pretrained(_lowerCamelCase    ,force_download=_lowerCamelCase  )

                     # Save fast tokenizer
                     logger.info(F"Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}"  )

                     # For organization names we create sub-directories
                     if "/" in checkpoint:
                            _a    : Union[str, Any]     					=checkpoint.split("""/"""  )
                            _a    : Any     					=os.path.join(_lowerCamelCase    ,_lowerCamelCase  )
                     elif add_prefix:
                            _a    : List[str]     					=checkpoint
                            _a    : List[str]     					=dump_path
                     else:
                            _a    : Any     					=None
                            _a    : str     					=dump_path

                     logger.info(F"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"  )

                     if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()  )[0]:
                            _a    : int     					=list(tokenizer.pretrained_vocab_files_map.values()  )[0][checkpoint]
                            _a    : int     					=file_path.split(_lowerCamelCase  )[-1][0]
                            if next_char == "/":
                                   _a    : Union[str, Any]     					=os.path.join(_lowerCamelCase    ,_lowerCamelCase  )
                                   _a    : Dict     					=None

                            logger.info(F"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"  )

                     _a    : List[Any]     					=tokenizer.save_pretrained(
                         _lowerCamelCase    ,legacy_format=_lowerCamelCase    ,filename_prefix=_lowerCamelCase  )
                     logger.info(F"=> File names {file_names}"  )

                     for file_name in file_names:
                            if not file_name.endswith("""tokenizer.json"""  ):
                                   os.remove(_lowerCamelCase  )
                                   logger.info(F"=> removing {file_name}"  )


if __name__ == "__main__":
   A__:    int					    =       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--dump_path''', default=None, type=str, required=True, help='''Path to output generated fast tokenizer files.'''
   )
   parser.add_argument(
       '''--tokenizer_name''',
       default=None,
       type=str,
       help=(
           F"Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will "
           '''download and convert all the checkpoints from AWS.'''
       ),
   )
   parser.add_argument(
       '''--checkpoint_name''',
       default=None,
       type=str,
       help='''Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.''',
   )
   parser.add_argument(
       '''--force_download''',
       action='''store_true''',
       help='''Re-download checkpoints.''',
   )
   A__:    Any					    =       parser.parse_args()

   convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)



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_  )



