"""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     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Any													=		'microsoft/speecht5_tts'
						lowerCAmelCase__  :						Optional[int]													=		(
						    '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__  :						List[Any]													=		'text_reader'
						lowerCAmelCase__  :						List[str]													=		SpeechTaProcessor
						lowerCAmelCase__  :						int													=		SpeechTaForTextToSpeech
						lowerCAmelCase__  :						Optional[int]													=		SpeechTaHifiGan

						lowerCAmelCase__  :						Optional[Any]													=		['text']
						lowerCAmelCase__  :						Any													=		['audio']





						def 				snake_case__	(       self     )					->				Dict:
													if self.post_processor is None:
																				A__	     =							'microsoft/speecht5_hifigan'
													super().setup()





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase=None     )					->				Tuple:
													A__	     =							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__	     =							load_dataset('Matthijs/cmu-arctic-xvectors'		,split='validation'     )
																				A__	     =							torch.tensor(embeddings_dataset[73_05]['xvector']     ).unsqueeze(0     )

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





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				Any:
													with torch.no_grad():
																				return self.model.generate_speech(**__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				List[Any]:
													with torch.no_grad():
																				return self.post_processor(__UpperCAmelCase     ).cpu().detach()






"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


__lowerCamelCase											=  logging.get_logger(__name__)
class     UpperCamelCase__(			__A ):





						def __init__(       self		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				None:
													warnings.warn(
													    'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
													    ' Please use ImageGPTImageProcessor instead.'		,__UpperCAmelCase		,)
													super().__init__(*__UpperCAmelCase		,**__UpperCAmelCase     )





