






# 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   (   _A						):
      _a							:						Any										=							'microsoft/speecht5_tts'
      _a							:						Union[str, 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.'
      )
      _a							:						List[Any]										=							'text_reader'
      _a							:						Any										=							SpeechTaProcessor
      _a							:						Dict										=							SpeechTaForTextToSpeech
      _a							:						Optional[Any]										=							SpeechTaHifiGan

      _a							:						Optional[int]										=							['text']
      _a							:						Optional[Any]										=							['audio']
      def 						lowercase__					(     self   ):
       if self.post_processor is None:
        snake_case__  :    int							       ="""microsoft/speecht5_hifigan"""
       super().setup()
      def 						lowercase__					(     self				,			a				,			a=None   ):
       snake_case__  :    Dict							       =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."""   )

        snake_case__  :    int							       =load_dataset("""Matthijs/cmu-arctic-xvectors"""				,			split="""validation"""   )
        snake_case__  :    Tuple							       =torch.tensor(embeddings_dataset[7_3_0_5]["""xvector"""]   ).unsqueeze(0   )

       return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
      def 						lowercase__					(     self				,			a   ):
       with torch.no_grad():
        return self.model.generate_speech(**a   )



      def 						lowercase__					(     self				,			a   ):
       with torch.no_grad():
        return self.post_processor(a   ).cpu().detach()













import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()







def 			A__							(      _a  :					int		,  _a  :					Any		,  _a  :					Union[str, Any]		,  _a  :					Tuple	):





 '''simple docstring'''







 snake_case__  :    Any							       =BigBirdConfig.from_json_file(_a	)
 print(f"Building PyTorch model from configuration: {config}"	)

 if is_trivia_qa:
  snake_case__  :    str							       =BigBirdForQuestionAnswering(_a	)
 else:
  snake_case__  :    Optional[int]							       =BigBirdForPreTraining(_a	)

 # Load weights from tf checkpoint
 load_tf_weights_in_big_bird(_a		,  _a		,  is_trivia_qa=_a	)

 # Save pytorch-model
 print(f"Save PyTorch model to {pytorch_dump_path}"	)
 model.save_pretrained(_a	)


if __name__ == "__main__":
       __lowerCamelCase	:		Union[str, Any]										=   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
       )
       parser.add_argument(
           """--big_bird_config_file""",
           default=None,
           type=str,
           required=True,
           help=(
               """The config json file corresponding to the pre-trained BERT model. \n"""
               """This specifies the model architecture."""
           ),
       )
       parser.add_argument(
           """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
       )
       parser.add_argument(
           """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
       )
       __lowerCamelCase	:		Tuple										=   parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
       )





