

'''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   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:Any      	=					"""microsoft/speecht5_tts"""
 __SCREAMING_SNAKE_CASE					:List[str]      	=					(
     """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					:Tuple      	=					SpeechTaProcessor
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					SpeechTaForTextToSpeech
 __SCREAMING_SNAKE_CASE					:Optional[int]      	=					SpeechTaHifiGan

 __SCREAMING_SNAKE_CASE					:List[Any]      	=					["""text"""]
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					["""audio"""]



 def 			snake_case__  (   self		:   List[Any]      ):
      if self.post_processor is None:
           __magic_name__						       =    '''microsoft/speecht5_hifigan'''
      super().setup()



 def 			snake_case__  (   self		:   int			,				a__		:   Dict			,				a__		:   Optional[Any]=None      ):
      __magic_name__						       =    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.'''      )

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

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



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   Any      ):
      with torch.no_grad():
           return self.model.generate_speech(**a__      )



 def 			snake_case__  (   self		:   Any			,				a__		:   str      ):
      with torch.no_grad():
           return self.post_processor(a__      ).cpu().detach()









'''simple docstring'''




from pathlib import Path

import fire
from tqdm import tqdm




def 						UpperCamelCase	(			a="ro"		,						a="en"		,						a="wmt16"		,						a=None			)				->     None:






     '''simple docstring'''
     try:
          import datasets
     except (ModuleNotFoundError, ImportError):
          raise ImportError('''run pip install datasets'''			)
     __magic_name__						       =    F'''{src_lang}-{tgt_lang}'''
     print(F'''Converting {dataset}-{pair}'''			)
     __magic_name__						       =    datasets.load_dataset(a		,						a			)
     if save_dir is None:
          __magic_name__						       =    F'''{dataset}-{pair}'''
     __magic_name__						       =    Path(a			)
     save_dir.mkdir(exist_ok=a			)

     for split in ds.keys():
          print(F'''Splitting {split} with {ds[split].num_rows} records'''			)

          # to save to val.source, val.target like summary datasets
          __magic_name__						       =    '''val''' if split == '''validation''' else split
          __magic_name__						       =    save_dir.joinpath(F'''{fn}.source'''			)
          __magic_name__						       =    save_dir.joinpath(F'''{fn}.target'''			)
          __magic_name__						       =    src_path.open('''w+'''			)
          __magic_name__						       =    tgt_path.open('''w+'''			)

          # reader is the bottleneck so writing one record at a time doesn't slow things down
          for x in tqdm(ds[split]			):
               __magic_name__						       =    x['''translation''']
               src_fp.write(ex[src_lang] + '''\n'''			)
               tgt_fp.write(ex[tgt_lang] + '''\n'''			)

     print(F'''Saved {dataset} dataset to {save_dir}'''			)


if __name__ == "__main__":
      fire.Fire(download_wmt_dataset)






