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#
# 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__			):
      snake_case__				:     int        =						'''microsoft/speecht5_tts'''
      snake_case__				:     List[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.'''
      )
      snake_case__				:     Optional[Any]        =						'''text_reader'''
      snake_case__				:     int        =						SpeechTaProcessor
      snake_case__				:     Any        =						SpeechTaForTextToSpeech
      snake_case__				:     List[str]        =						SpeechTaHifiGan

      snake_case__				:     int        =						['''text''']
      snake_case__				:     Dict        =						['''audio''']
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			Tuple:
             if self.post_processor is None:
                    a_					:		List[Any]							=  'microsoft/speecht5_hifigan'
             super().setup()
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=None     )		->			str:
             a_					:		Union[str, Any]							=  self.pre_processor(text=SCREAMING_SNAKE_CASE__     ,							return_tensors='pt'     ,							truncation=SCREAMING_SNAKE_CASE__     )

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

                    a_					:		str							=  load_dataset('Matthijs/cmu-arctic-xvectors'     ,							split='validation'     )
                    a_					:		List[str]							=  torch.tensor(embeddings_dataset[7_3_0_5]['xvector']     ).unsqueeze(0     )

             return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       int     )		->			Any:
             with torch.no_grad():
                    return self.model.generate_speech(**SCREAMING_SNAKE_CASE__     )

      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       List[str]     )		->			List[str]:
             with torch.no_grad():
                    return self.post_processor(SCREAMING_SNAKE_CASE__     ).cpu().detach()






from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments







def        SCREAMING_SNAKE_CASE_						(			)	->		Any:

       """simple docstring"""

       a_					:		Optional[Any]							=  HfArgumentParser(__A				)
       a_					:		Optional[int]							=  parser.parse_args_into_dataclasses()[0]
       a_					:		List[Any]							=  TensorFlowBenchmark(args=__A				)
       try:
              a_					:		List[str]							=  parser.parse_args_into_dataclasses()[0]
       except ValueError as e:
              a_					:		Dict							=  'Arg --no_{0} is no longer used, please use --no-{0} instead.'
              a_					:		Dict							=  ' '.join(str(__A				).split(' '				)[:-1]				)
              a_					:		int							=  ''
              a_					:		int							=  eval(str(__A				).split(' '				)[-1]				)
              a_					:		Any							=  []
              for arg in depreciated_args:
                     # arg[2:] removes '--'
                     if arg[2:] in TensorFlowBenchmark.deprecated_args:
                            # arg[5:] removes '--no_'
                            full_error_msg += arg_error_msg.format(arg[5:]				)
                     else:
                            wrong_args.append(__A				)
              if len(__A				) > 0:
                     a_					:		str							=  full_error_msg + begin_error_msg + str(__A				)
              raise ValueError(__A				)
       benchmark.run()


if __name__ == "__main__":
   main()


