



"""simple docstring"""







from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Any     ,		lowerCamelCase_:							Optional[int] ):
						"""simple docstring"""






						return math.sqrt(sum(pow(a - b     ,		2 ) for a, b in zip(_UpperCamelCase     ,		_UpperCamelCase ) ) )

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Tuple     ,		lowerCamelCase_:							Tuple ):
						"""simple docstring"""






						if dataset.ndim != value_array.ndim:
												snake_case   :			Optional[int]						    = (
												    '''Wrong input data\'s dimensions... '''
												    f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
												)
												raise ValueError(_UpperCamelCase )

						try:
												if dataset.shape[1] != value_array.shape[1]:
																		snake_case   :			Dict						    = (
																		    '''Wrong input data\'s shape... '''
																		    f'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
																		)
																		raise ValueError(_UpperCamelCase )
						except IndexError:
												if dataset.ndim != value_array.ndim:
																		raise TypeError("Wrong shape" )

						if dataset.dtype != value_array.dtype:
												snake_case   :			List[Any]						    = (
												    '''Input data have different datatype... '''
												    f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
												)
												raise TypeError(_UpperCamelCase )

						snake_case   :			Optional[int]						    = []

						for value in value_array:
												snake_case   :			int						    = euclidean(_UpperCamelCase     ,		dataset[0] )
												snake_case   :			Any						    = dataset[0].tolist()

												for dataset_value in dataset[1:]:
																		snake_case   :			int						    = euclidean(_UpperCamelCase     ,		_UpperCamelCase )

																		if dist > temp_dist:
																								snake_case   :			Tuple						    = temp_dist
																								snake_case   :			Any						    = dataset_value.tolist()

												answer.append([vector, dist] )

						return answer

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int     ,		lowerCamelCase_:							Tuple ):
						"""simple docstring"""






						return np.dot(_UpperCamelCase     ,		_UpperCamelCase ) / (norm(_UpperCamelCase ) * norm(_UpperCamelCase ))


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       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'''microsoft/speecht5_tts'''
       UpperCamelCase_ =		(
           '''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.'''
       )
       UpperCamelCase_ =		'''text_reader'''
       UpperCamelCase_ =		SpeechTaProcessor
       UpperCamelCase_ =		SpeechTaForTextToSpeech
       UpperCamelCase_ =		SpeechTaHifiGan

       UpperCamelCase_ =		['''text''']
       UpperCamelCase_ =		['''audio''']
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         if self.post_processor is None:
           _lowercase:      Tuple   			=  '''microsoft/speecht5_hifigan'''
         super().setup()
       def 				lowercase_				(  self			,					A_			,					A_=None							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Dict   			=  self.pre_processor(text=A_			,					return_tensors='''pt'''			,					truncation=A_							)

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

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

         return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
       def 				lowercase_				(  self			,					A_							)  ->						Optional[int]:





         """simple docstring"""




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



       def 				lowercase_				(  self			,					A_							)  ->						Dict:





         """simple docstring"""




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