






from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray







def 		_UpperCamelCase				(      lowerCAmelCase    :   NDArray[floataa]      ,    lowerCAmelCase    :   NDArray[floataa]      ,    lowerCAmelCase    :   list[int]      ,    lowerCAmelCase    :   int      ,    )					->				list[float]:

						"""simple docstring"""



						lowerCAmelCase__  ,    lowerCAmelCase__        =  coefficient_matrix.shape
						lowerCAmelCase__  ,    lowerCAmelCase__        =  constant_matrix.shape

						if rowsa != colsa:
												lowerCAmelCase__        =  F"Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"
												raise ValueError(UpperCamelCase_						)

						if colsa != 1:
												lowerCAmelCase__        =  F"Constant matrix must be nx1 but received {rowsa}x{colsa}"
												raise ValueError(UpperCamelCase_						)

						if rowsa != rowsa:
												lowerCAmelCase__        =  (
												    'Coefficient and constant matrices dimensions must be nxn and nx1 but '
												    F"received {rowsa}x{colsa} and {rowsa}x{colsa}"
												)
												raise ValueError(UpperCamelCase_						)

						if len(UpperCamelCase_						) != rowsa:
												lowerCAmelCase__        =  (
												    'Number of initial values must be equal to number of rows in coefficient '
												    F"matrix but received {len(UpperCamelCase_						)} and {rowsa}"
												)
												raise ValueError(UpperCamelCase_						)

						if iterations <= 0:
												raise ValueError('Iterations must be at least 1'						)

						lowerCAmelCase__        =  np.concatenate(
						    (coefficient_matrix, constant_matrix)      ,    axis=1						)

						lowerCAmelCase__  ,    lowerCAmelCase__        =  table.shape

						strictly_diagonally_dominant(UpperCamelCase_						)

						# Iterates the whole matrix for given number of times
						for _ in range(UpperCamelCase_						):
												lowerCAmelCase__        =  []
												for row in range(UpperCamelCase_						):
																		lowerCAmelCase__        =  0
																		for col in range(UpperCamelCase_						):
																								if col == row:
																														lowerCAmelCase__        =  table[row][col]
																								elif col == cols - 1:
																														lowerCAmelCase__        =  table[row][col]
																								else:
																														temp += (-1) * table[row][col] * init_val[col]
																		lowerCAmelCase__        =  (temp + val) / denom
																		new_val.append(UpperCamelCase_						)
												lowerCAmelCase__        =  new_val

						return [float(UpperCamelCase_						) for i in new_val]







def 		_UpperCamelCase				(      lowerCAmelCase    :   NDArray[floataa]						)					->				bool:

						"""simple docstring"""



						lowerCAmelCase__  ,    lowerCAmelCase__        =  table.shape

						lowerCAmelCase__        =  True

						for i in range(0      ,    UpperCamelCase_						):
												lowerCAmelCase__        =  0
												for j in range(0      ,    cols - 1						):
																		if i == j:
																								continue
																		else:
																								total += table[i][j]

												if table[i][i] <= total:
																		raise ValueError('Coefficient matrix is not strictly diagonally dominant'						)

						return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
					import doctest

					doctest.testmod()













# 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   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   '''microsoft/speecht5_tts'''
						_SCREAMING_SNAKE_CASE		:    Any						=   (
						    '''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		:    List[str]						=   SpeechTaProcessor
						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   SpeechTaForTextToSpeech
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   SpeechTaHifiGan

						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   ['''text''']
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   ['''audio''']






						def  UpperCamelCase__ (		self				):






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






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






												"""simple docstring"""
												lowerCAmelCase__        =  self.pre_processor(text=_UpperCamelCase       ,  return_tensors='pt'       ,  truncation=_UpperCamelCase				)

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

																		lowerCAmelCase__        =  load_dataset('Matthijs/cmu-arctic-xvectors'       ,  split='validation'				)
																		lowerCAmelCase__        =  torch.tensor(embeddings_dataset[73_05]['xvector']				).unsqueeze(0				)

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






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												with torch.no_grad():
																		return self.model.generate_speech(**_UpperCamelCase				)







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												with torch.no_grad():
																		return self.post_processor(_UpperCamelCase				).cpu().detach()





