"""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 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = "microsoft/speecht5_tts"
					snake_case__				    = (
					    "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."
					)
					snake_case__				    = "text_reader"
					snake_case__				    = SpeechTaProcessor
					snake_case__				    = SpeechTaForTextToSpeech
					snake_case__				    = SpeechTaHifiGan

					snake_case__				    = ["text"]
					snake_case__				    = ["audio"]







					def 		__lowerCAmelCase						(				self   :	Dict			):
									if self.post_processor is None:
													UpperCAmelCase__             =							'microsoft/speecht5_hifigan'
									super().setup()







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	str    ,lowerCamelCase__   :	Dict=None			):
									UpperCAmelCase__             =							self.pre_processor(text=lowerCamelCase__    ,return_tensors='pt'    ,truncation=lowerCamelCase__			)

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

													UpperCAmelCase__             =							load_dataset('Matthijs/cmu-arctic-xvectors'    ,split='validation'			)
													UpperCAmelCase__             =							torch.tensor(embeddings_dataset[7_305]['xvector']			).unsqueeze(0			)

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







					def 		__lowerCAmelCase						(				self   :	Tuple    ,lowerCamelCase__   :	List[str]			):
									with torch.no_grad():
													return self.model.generate_speech(**lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Any    ,lowerCamelCase__   :	Optional[int]			):
									with torch.no_grad():
													return self.post_processor(lowerCamelCase__			).cpu().detach()




"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase__  :  Tuple						     =	{'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  :  List[Any]						     =	[
						    'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'ViTMAEForPreTraining',
						    'ViTMAELayer',
						    'ViTMAEModel',
						    'ViTMAEPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  :  int						     =	[
						    'TFViTMAEForPreTraining',
						    'TFViTMAEModel',
						    'TFViTMAEPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vit_mae import (
												    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ViTMAEForPreTraining,
												    ViTMAELayer,
												    ViTMAEModel,
												    ViTMAEPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
						import sys

						lowerCAmelCase__  :  int						     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



