





# 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_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       "microsoft/speecht5_tts"
							lowerCamelCase_							     =       (
							    "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_							     =       "text_reader"
							lowerCamelCase_							     =       SpeechTaProcessor
							lowerCamelCase_							     =       SpeechTaForTextToSpeech
							lowerCamelCase_							     =       SpeechTaHifiGan

							lowerCamelCase_							     =       ["text"]
							lowerCamelCase_							     =       ["audio"]


							def   _snake_case	(				self     :Dict		) -> List[str]:



									"""simple docstring"""



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


							def   _snake_case	(				self     :List[Any]				,					__A     :Union[str, Any]				,					__A     :Optional[Any]=None		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.pre_processor(text=__A				,					return_tensors="""pt"""				,					truncation=__A		)

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

											SCREAMING_SNAKE_CASE__								=   load_dataset("""Matthijs/cmu-arctic-xvectors"""				,					split="""validation"""		)
											SCREAMING_SNAKE_CASE__								=   torch.tensor(embeddings_dataset[7305]["""xvector"""]		).unsqueeze(0		)

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


							def   _snake_case	(				self     :Any				,					__A     :Tuple		) -> Optional[Any]:



									"""simple docstring"""



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


							def   _snake_case	(				self     :int				,					__A     :int		) -> List[Any]:



									"""simple docstring"""



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





'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


lowerCAmelCase_      :  Optional[Any]				     =  {}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  Any				     =  ['GPTSw3Tokenizer']


if TYPE_CHECKING:
 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpt_swa import GPTSwaTokenizer

else:
 import sys

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