





"""simple docstring"""






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 				__lowercase			(			snake_case__							):







     '''simple docstring'''




     __lowerCAmelCase  =  '''microsoft/speecht5_tts'''
     __lowerCAmelCase  =  (
         '''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.'''
     )
     __lowerCAmelCase  =  '''text_reader'''
     __lowerCAmelCase  =  SpeechTaProcessor
     __lowerCAmelCase  =  SpeechTaForTextToSpeech
     __lowerCAmelCase  =  SpeechTaHifiGan

     __lowerCAmelCase  =  ['''text''']
     __lowerCAmelCase  =  ['''audio''']







     def  _lowerCamelCase		(						self     ):
         if self.post_processor is None:
             __a :						Dict			       =  "microsoft/speecht5_hifigan"
         super().setup()







     def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=None     ):
         __a :						Tuple			       =  self.pre_processor(text=lowercase_	,			return_tensors='''pt'''	,			truncation=lowercase_     )

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

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

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







     def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
         with torch.no_grad():
             return self.model.generate_speech(**lowercase_     )







     def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
         with torch.no_grad():
             return self.post_processor(lowercase_     ).cpu().detach()






"""simple docstring"""






from __future__ import annotations

import os
from collections.abc import Mapping

A 			=  tuple[int, int]




class 				__lowercase			:







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						set[int]			       =  vertices
									__a :						dict[EdgeT, int]			       =  {
									    (min(_UpperCAmelCase     ), max(_UpperCAmelCase     )): weight for edge, weight in edges.items()
									}







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									self.vertices.add(edge[0]     )
									self.vertices.add(edge[1]     )
									__a :						Any			       =  weight







					def  _lowerCamelCase		(						self     ):
									__a :						Graph			       =  Graph({min(self.vertices     )}	,			{}     )
									__a :						EdgeT
									__a :						int
									__a :						EdgeT
									__a :						int

									while len(subgraph.vertices     ) < len(self.vertices     ):
													__a :						List[Any]			       =  max(self.edges.values()     ) + 1
													for edge, weight in self.edges.items():
																	if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
																					if weight < min_weight:
																									__a :						str			       =  edge
																									__a :						int			       =  weight

													subgraph.add_edge(_UpperCAmelCase	,			_UpperCAmelCase     )

									return subgraph






def     __A      (			a_						:str = "p107_network.txt")		->		int:
				__a :						str			       =  os.path.abspath(os.path.dirname(a_))
				__a :						str			       =  os.path.join(a_   ,       a_)
				__a :						dict[EdgeT, int]			       =  {}
				__a :						list[str]
				__a :						int
				__a :						int

				with open(a_) as f:
								__a :						Any			       =  f.read().strip().split('''\n''')

				__a :						Union[str, Any]			       =  [line.split(''',''') for line in data]

				for edgea in range(1   ,       len(a_)):
								for edgea in range(a_):
												if adjaceny_matrix[edgea][edgea] != "-":
																__a :						int			       =  int(adjaceny_matrix[edgea][edgea])

				__a :						Graph			       =  Graph(set(range(len(a_)))   ,       a_)

				__a :						Graph			       =  graph.prims_algorithm()

				__a :						int			       =  sum(graph.edges.values())
				__a :						int			       =  sum(subgraph.edges.values())

				return initial_total - optimal_total


if __name__ == "__main__":
		print(F'{solution() = }')