






'''simple docstring'''



from __future__ import annotations

import numpy as np



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	np.ndarray    )  ->			tuple[np.ndarray, np.ndarray]:

   """simple docstring"""
   __a     ,			__a				     =    np.shape(SCREAMING_SNAKE_CASE__    )
   if rows != columns:
      __a				     =    (
          '\'table\' has to be of square shaped array but got a '
          f"""{rows}x{columns} array:\n{table}"""
      )
      raise ValueError(SCREAMING_SNAKE_CASE__    )

   __a				     =    np.zeros((rows, columns)    )
   __a				     =    np.zeros((rows, columns)    )
   for i in range(SCREAMING_SNAKE_CASE__    ):
      for j in range(SCREAMING_SNAKE_CASE__    ):
         __a				     =    sum(lower[i][k] * upper[k][j] for k in range(SCREAMING_SNAKE_CASE__    )    )
         if upper[j][j] == 0:
            raise ArithmeticError('No LU decomposition exists'    )
         __a				     =    (table[i][j] - total) / upper[j][j]
      __a				     =    1
      for j in range(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    ):
         __a				     =    sum(lower[i][k] * upper[k][j] for k in range(SCREAMING_SNAKE_CASE__    )    )
         __a				     =    table[i][j] - total
   return lower, upper


if __name__ == "__main__":
 import doctest

 doctest.testmod()







'''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 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="microsoft/speecht5_tts"
      __a  					=(
          "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."
      )
      __a  					="text_reader"
      __a  					=SpeechTaProcessor
      __a  					=SpeechTaForTextToSpeech
      __a  					=SpeechTaHifiGan

      __a  					=["text"]
      __a  					=["audio"]




      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         if self.post_processor is None:
            __a				     =    'microsoft/speecht5_hifigan'
         super().setup()




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase=None      )							->str:



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

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




      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         with torch.no_grad():
            return self.model.generate_speech(**lowerCamelCase      )






      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         with torch.no_grad():
            return self.post_processor(lowerCamelCase      ).cpu().detach()