

# 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 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  Optional[Any]										=				'''microsoft/speecht5_tts'''
							_UpperCAmelCase   :  Optional[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.'''
							)
							_UpperCAmelCase   :  int										=				'''text_reader'''
							_UpperCAmelCase   :  Optional[int]										=				SpeechTaProcessor
							_UpperCAmelCase   :  int										=				SpeechTaForTextToSpeech
							_UpperCAmelCase   :  Optional[int]										=				SpeechTaHifiGan

							_UpperCAmelCase   :  Dict										=				['''text''']
							_UpperCAmelCase   :  Optional[Any]										=				['''audio''']
							def    lowerCAmelCase					(		self		:      Any):
								if self.post_processor is None:
									__lowerCamelCase    :    int 	=			'microsoft/speecht5_hifigan'
								super().setup()
							def    lowerCAmelCase					(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      str=None):
								__lowerCamelCase    :    int 	=			self.pre_processor(text=SCREAMING_SNAKE_CASE__       ,return_tensors='pt'       ,truncation=SCREAMING_SNAKE_CASE__)

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

									__lowerCamelCase    :    int 	=			load_dataset('Matthijs/cmu-arctic-xvectors'       ,split='validation')
									__lowerCamelCase    :    List[Any] 	=			torch.tensor(embeddings_dataset[7_3_0_5]['xvector']).unsqueeze(0)

								return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
							def    lowerCAmelCase					(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Dict):
								with torch.no_grad():
									return self.model.generate_speech(**SCREAMING_SNAKE_CASE__)


							def    lowerCAmelCase					(		self		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      Tuple):
								with torch.no_grad():
									return self.post_processor(SCREAMING_SNAKE_CASE__).cpu().detach()







from __future__ import annotations






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					bool:
	if len(lowerCamelCase__      ) == 0:
		return False
	__lowerCamelCase    :    List[Any] 	=			len(lowerCamelCase__      ) // 2
	if a_list[midpoint] == item:
		return True
	if item < a_list[midpoint]:
		return binary_search(a_list[:midpoint]     ,			lowerCamelCase__      )
	else:
		return binary_search(a_list[midpoint + 1 :]     ,			lowerCamelCase__      )


if __name__ == "__main__":
					a		=input("""Enter numbers separated by comma:\n""").strip()
					a		=[int(item.strip()) for item in user_input.split(""",""")]
					a		=int(input("""Enter the number to be found in the list:\n""").strip())
					a		="""""" if binary_search(sequence, target) else """not """
					print(F"""{target} was {not_str}found in {sequence}""")




