

import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
      from datasets import load_dataset






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:Any      	=					"""microsoft/speecht5_tts"""
 __SCREAMING_SNAKE_CASE					:List[str]      	=					(
     """This is a tool that reads an English text out loud. It takes an input named `text` which should contain the """
     """text to read (in English) and returns a waveform object containing the sound."""
 )
 __SCREAMING_SNAKE_CASE					:int      	=					"""text_reader"""
 __SCREAMING_SNAKE_CASE					:Tuple      	=					SpeechTaProcessor
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					SpeechTaForTextToSpeech
 __SCREAMING_SNAKE_CASE					:Optional[int]      	=					SpeechTaHifiGan

 __SCREAMING_SNAKE_CASE					:List[Any]      	=					["""text"""]
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					["""audio"""]



 def 			snake_case__  (   self		:   List[Any]      ):
      if self.post_processor is None:
           __magic_name__						       =    '''microsoft/speecht5_hifigan'''
      super().setup()



 def 			snake_case__  (   self		:   int			,				a__		:   Dict			,				a__		:   Optional[Any]=None      ):
      __magic_name__						       =    self.pre_processor(text=a__			,				return_tensors='''pt'''			,				truncation=a__      )

      if speaker_embeddings is None:
           if not is_datasets_available():
                raise ImportError('''Datasets needs to be installed if not passing speaker embeddings.'''      )

           __magic_name__						       =    load_dataset('''Matthijs/cmu-arctic-xvectors'''			,				split='''validation'''      )
           __magic_name__						       =    torch.tensor(embeddings_dataset[7305]['''xvector''']      ).unsqueeze(0      )

      return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   Any      ):
      with torch.no_grad():
           return self.model.generate_speech(**a__      )



 def 			snake_case__  (   self		:   Any			,				a__		:   str      ):
      with torch.no_grad():
           return self.post_processor(a__      ).cpu().detach()









'''simple docstring'''




import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
_lowerCAmelCase         =						object()

# For specifying empty leaf dict `{}`
_lowerCAmelCase         =						object()




def 						UpperCamelCase	(			a		,						a			)				->     Union[str, Any]:






     '''simple docstring'''
     __magic_name__						       =    tuple((re.compile(x + '''$'''			) for x in qs)			)
     for i in range(len(a			) - len(a			) + 1			):
          __magic_name__						       =    [x.match(a			) for x, y in zip(a		,						ks[i:]			)]
          if matches and all(a			):
               return True
     return False




def 						UpperCamelCase	(			a			)				->     Tuple:






     '''simple docstring'''
     def replace(a		,						a			):
          for rule, replacement in rules:
               if _match(a		,						a			):
                    return replacement
          return val

     return replace




def 						UpperCamelCase	(			)				->     Union[str, Any]:






     '''simple docstring'''
     return [
         # embeddings
         (("transformer", "wpe", "embedding"), P('''mp'''		,						a			)),
         (("transformer", "wte", "embedding"), P('''mp'''		,						a			)),
         # atention
         (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(a		,						'''mp'''			)),
         (("attention", "out_proj", "kernel"), P('''mp'''		,						a			)),
         (("attention", "out_proj", "bias"), None),
         # mlp
         (("mlp", "c_fc", "kernel"), P(a		,						'''mp'''			)),
         (("mlp", "c_fc", "bias"), P('''mp'''			)),
         (("mlp", "c_proj", "kernel"), P('''mp'''		,						a			)),
         (("mlp", "c_proj", "bias"), None),
         # layer norms
         ((r"ln_\d+", "bias"), None),
         ((r"\d+", r"ln_\d+", "scale"), None),
         (("ln_f", "bias"), None),
         (("ln_f", "scale"), None),
     ]




def 						UpperCamelCase	(			a			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    _get_partition_rules()
     __magic_name__						       =    _replacement_rules(a			)
     __magic_name__						       =    {k: _unmatched for k in flatten_dict(a			)}
     __magic_name__						       =    {k: replace(a		,						a			) for k, v in initd.items()}
     assert _unmatched not in result.values(), "Incomplete partition spec."
     return freeze(unflatten_dict(a			)			)






