code: 
  
  
  
  '''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.
  from typing import TYPE_CHECKING
  
  from ..utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
  )
  
  
  a__						:			Union[str, Any]										=			{
      'agents': ['Agent', 'AzureOpenAiAgent', 'HfAgent', 'LocalAgent', 'OpenAiAgent'],
      'base': ['PipelineTool', 'RemoteTool', 'Tool', 'launch_gradio_demo', 'load_tool'],
  }
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a__						:			List[str]										=			['DocumentQuestionAnsweringTool']
   a__						:			Dict										=			['ImageCaptioningTool']
   a__						:			Optional[int]										=			['ImageQuestionAnsweringTool']
   a__						:			Optional[int]										=			['ImageSegmentationTool']
   a__						:			List[Any]										=			['SpeechToTextTool']
   a__						:			Tuple										=			['TextClassificationTool']
   a__						:			Dict										=			['TextQuestionAnsweringTool']
   a__						:			Optional[Any]										=			['TextSummarizationTool']
   a__						:			Optional[Any]										=			['TextToSpeechTool']
   a__						:			List[Any]										=			['TranslationTool']
  
  if TYPE_CHECKING:
   from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
   from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .document_question_answering import DocumentQuestionAnsweringTool
    from .image_captioning import ImageCaptioningTool
    from .image_question_answering import ImageQuestionAnsweringTool
    from .image_segmentation import ImageSegmentationTool
    from .speech_to_text import SpeechToTextTool
    from .text_classification import TextClassificationTool
    from .text_question_answering import TextQuestionAnsweringTool
    from .text_summarization import TextSummarizationTool
    from .text_to_speech import TextToSpeechTool
    from .translation import TranslationTool
  else:
   import sys
  
   a__						:			Tuple										=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  TaConfig.from_json_file(__A       )
   print(f"Building PyTorch model from configuration: {config}"       )
   __UpperCamelCase				      =  TaForConditionalGeneration(__A       )
  
   # Load weights from tf checkpoint
   load_tf_weights_in_ta(__A							,__A							,__A       )
  
   # Save pytorch-model
   print(f"Save PyTorch model to {pytorch_dump_path}"       )
   model.save_pretrained(__A       )
  
  
  if __name__ == "__main__":
   a__						:			int										=			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(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help=(
           'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   a__						:			Optional[Any]										=			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  
  
  
  