




from __future__ import annotations

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

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						NDArray[floataa]							,						__UpperCamelCase :						NDArray[floataa]							,						__UpperCamelCase :						list[int]							,						__UpperCamelCase :						int							,						) ->       list[float]:

				"""simple docstring"""







				A__  :	Tuple         =							coefficient_matrix.shape
				A__  :	List[str]         =							constant_matrix.shape

				if rowsa != colsa:
								A__  :	Optional[Any]         =							F"Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"
								raise ValueError(__UpperCamelCase       )

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

				if rowsa != rowsa:
								A__  :	Union[str, Any]         =							(
								    '''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:
								A__  :	Dict         =							(
								    '''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'''       )

				A__  :	NDArray[floataa]         =							np.concatenate(
				    (coefficient_matrix, constant_matrix)							,						axis=1       )

				A__  :	Union[str, Any]         =							table.shape

				strictly_diagonally_dominant(__UpperCamelCase       )

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

				return [float(__UpperCamelCase       ) for i in new_val]

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						NDArray[floataa]       ) ->       bool:

				"""simple docstring"""







				A__  :	Union[str, Any]         =							table.shape

				A__  :	Union[str, Any]         =							True

				for i in range(0							,						__UpperCamelCase       ):
								A__  :	Union[str, Any]         =							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  UpperCamelCase__		(    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   __snake_case				(			self		):
					if self.post_processor is None:
									A__  :	int         =							'''microsoft/speecht5_hifigan'''
					super().setup()





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__=None		):
					A__  :	List[Any]         =							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.'''		)

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

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





	def   __snake_case				(			self				,       UpperCamelCase__		):
					with torch.no_grad():
									return self.model.generate_speech(**UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					with torch.no_grad():
									return self.post_processor(UpperCamelCase__		).cpu().detach()