






'''simple docstring'''

from __future__ import annotations

from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE__      =   TypeVar('T')






class 				a_						(			Generic[T]				):

  def __init__(      self			,		_SCREAMING_SNAKE_CASE      )  ->      Dict:



        """simple docstring"""






        UpperCamelCase				  =					data
        UpperCamelCase				  =					self
        UpperCamelCase				  =					0






class 				a_						(			Generic[T]				):

  def __init__(      self      )  ->      Any:



        """simple docstring"""






        UpperCamelCase				  =					{}

  def   A__						(      self			,		_SCREAMING_SNAKE_CASE      )  ->      List[str]:



        """simple docstring"""






        UpperCamelCase				  =					DisjointSetTreeNode(__lowerCamelCase      )

  def   A__						(      self			,		_SCREAMING_SNAKE_CASE      )  ->      Optional[Any]:



        """simple docstring"""






        UpperCamelCase				  =					self.map[data]
        if elem_ref != elem_ref.parent:
              UpperCamelCase				  =					self.find_set(elem_ref.parent.data      )
        return elem_ref.parent

  def   A__						(      self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE      )  ->      Dict:



        """simple docstring"""






        if nodea.rank > nodea.rank:
              UpperCamelCase				  =					nodea
        else:
              UpperCamelCase				  =					nodea
              if nodea.rank == nodea.rank:
                    nodea.rank += 1


  def   A__						(      self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE      )  ->      Dict:



        """simple docstring"""






        self.link(self.find_set(__lowerCamelCase      )			,		self.find_set(__lowerCamelCase      )      )






class 				a_						(			Generic[T]				):

  def __init__(      self      )  ->      Optional[int]:



        """simple docstring"""






        UpperCamelCase				  =					{}

  def   A__						(      self			,		_SCREAMING_SNAKE_CASE      )  ->      str:



        """simple docstring"""






        if node not in self.connections:
              UpperCamelCase				  =					{}

  def   A__						(      self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE      )  ->      List[Any]:



        """simple docstring"""






        self.add_node(__lowerCamelCase      )
        self.add_node(__lowerCamelCase      )
        UpperCamelCase				  =					weight
        UpperCamelCase				  =					weight


  def   A__						(      self      )  ->      Optional[int]:



        """simple docstring"""






        UpperCamelCase				  =					[]
        UpperCamelCase				  =					set()
        for start in self.connections:
              for end in self.connections[start]:
                    if (start, end) not in seen:
                          seen.add((end, start)      )
                          edges.append((start, end, self.connections[start][end])      )
        edges.sort(key=lambda _SCREAMING_SNAKE_CASE      : x[2]      )

        # creating the disjoint set
        UpperCamelCase				  =					DisjointSetTree[T]()
        for node in self.connections:
              disjoint_set.make_set(__lowerCamelCase      )

        # MST generation
        UpperCamelCase				  =					0
        UpperCamelCase				  =					0
        UpperCamelCase				  =					GraphUndirectedWeighted[T]()
        while num_edges < len(self.connections      ) - 1:
              UpperCamelCase				  =					edges[index]
              index += 1
              UpperCamelCase				  =					disjoint_set.find_set(__lowerCamelCase      )
              UpperCamelCase				  =					disjoint_set.find_set(__lowerCamelCase      )
              if parent_u != parent_v:
                    num_edges += 1
                    graph.add_edge(__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase      )
                    disjoint_set.union(__lowerCamelCase			,		__lowerCamelCase      )
        return graph







"""simple docstring"""



# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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 		__snake_case					(		SCREAMING_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 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     if self.post_processor is None:
         __A    :						List[str]						 =     '''microsoft/speecht5_hifigan'''
     super().setup()

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase=None  ):



     '''simple docstring'''




     __A    :						int						 =     self.pre_processor(text=__lowerCamelCase					,						return_tensors='''pt'''					,						truncation=__lowerCamelCase  )

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

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

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

 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     with torch.no_grad():
         return self.model.generate_speech(**__lowerCamelCase  )







 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     with torch.no_grad():
         return self.post_processor(__lowerCamelCase  ).cpu().detach()




