






from typing import TYPE_CHECKING

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


__snake_case      :      Optional[int]             =       {
    """configuration_mega""": ["""MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegaConfig""", """MegaOnnxConfig"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case      :      Tuple             =       [
					    """MEGA_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MegaForCausalLM""",
					    """MegaForMaskedLM""",
					    """MegaForMultipleChoice""",
					    """MegaForQuestionAnswering""",
					    """MegaForSequenceClassification""",
					    """MegaForTokenClassification""",
					    """MegaModel""",
					    """MegaPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mega import (
										    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MegaForCausalLM,
										    MegaForMaskedLM,
										    MegaForMultipleChoice,
										    MegaForQuestionAnswering,
										    MegaForSequenceClassification,
										    MegaForTokenClassification,
										    MegaModel,
										    MegaPreTrainedModel,
										)

else:
					import sys

					__snake_case      :      List[str]             =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)













# 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       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   '''microsoft/speecht5_tts'''
						_SCREAMING_SNAKE_CASE		:    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.'''
						)
						_SCREAMING_SNAKE_CASE		:    int						=   '''text_reader'''
						_SCREAMING_SNAKE_CASE		:    List[str]						=   SpeechTaProcessor
						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   SpeechTaForTextToSpeech
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   SpeechTaHifiGan

						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   ['''text''']
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   ['''audio''']






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												if self.post_processor is None:
																		lowerCAmelCase__        =  'microsoft/speecht5_hifigan'
												super().setup()






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase=None				):






												"""simple docstring"""
												lowerCAmelCase__        =  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.'				)

																		lowerCAmelCase__        =  load_dataset('Matthijs/cmu-arctic-xvectors'       ,  split='validation'				)
																		lowerCAmelCase__        =  torch.tensor(embeddings_dataset[73_05]['xvector']				).unsqueeze(0				)

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






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												with torch.no_grad():
																		return self.model.generate_speech(**_UpperCamelCase				)







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												with torch.no_grad():
																		return self.post_processor(_UpperCamelCase				).cpu().detach()





