






# 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      lowercase__     (			_UpperCAmelCase	):
	a_         ="""microsoft/speecht5_tts"""
	a_         =(
	    """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."""
	)
	a_         ="""text_reader"""
	a_         =SpeechTaProcessor
	a_         =SpeechTaForTextToSpeech
	a_         =SpeechTaHifiGan

	a_         =["""text"""]
	a_         =["""audio"""]

	def 		UpperCAmelCase  (							self       )->					Optional[int]:







							'''simple docstring'''





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

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase=None       )->					List[Any]:







							'''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[7305]["xvector"]       ).unsqueeze(0       )

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

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					str:







							'''simple docstring'''





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


	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					Tuple:







							'''simple docstring'''





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














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_							=    {'''configuration_ibert''': ['''IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''IBertConfig''', '''IBertOnnxConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    '''IBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''IBertForMaskedLM''',
	    '''IBertForMultipleChoice''',
	    '''IBertForQuestionAnswering''',
	    '''IBertForSequenceClassification''',
	    '''IBertForTokenClassification''',
	    '''IBertModel''',
	    '''IBertPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_ibert import (
		    IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    IBertForMaskedLM,
		    IBertForMultipleChoice,
		    IBertForQuestionAnswering,
		    IBertForSequenceClassification,
		    IBertForTokenClassification,
		    IBertModel,
		    IBertPreTrainedModel,
		)

else:
	import sys

	a_							=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






