code: 
  
  
  """simple docstring"""
  import argparse
  
  from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  def 							_snake_case			(      __snake_case       :			Union[str, Any]	,   __snake_case       :			Dict	,   __snake_case       :			Union[str, Any]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Optional[int]      =		TaConfig.from_json_file(__snake_case      )
     print(F'Building PyTorch model from configuration: {config}'      )
     _lowerCamelCase  :				Any      =		TaForConditionalGeneration(__snake_case      )
  
     # Load weights from tf checkpoint
     load_tf_weights_in_ta(__snake_case	,   __snake_case	,   __snake_case      )
  
     # Save pytorch-model
     print(F'Save PyTorch model to {pytorch_dump_path}'      )
     model.save_pretrained(__snake_case      )
  
  
  if __name__ == "__main__":
       UpperCAmelCase   =	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."""
       )
       UpperCAmelCase   =	parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
style_context: 
  
  
  """simple docstring"""
  import os
  
  from apache_beam.io.filesystems import FileSystems
  from apache_beam.pipeline import Pipeline
  
  from .logging import get_logger
  
  
  UpperCAmelCase   =	2 << 20  # 2mb
  UpperCAmelCase   =	get_logger(__name__)
  
  
  
  
  class   lowercase__							(				A_							):
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Dict:
        _lowerCamelCase  :				Tuple      =		self._options.get_all_options().get("""runner""")
        return runner in [None, "DirectRunner", "PortableRunner"]
  
  
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			Union[str, Any]	,   __snake_case       :			List[Any]	,   __snake_case       :			Any=False      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Any      =		FileSystems
     if fs.exists(__snake_case      ):
        if force_upload:
           logger.info(F'Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.'      )
        else:
           logger.info(F'Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.'      )
           return
     with fs.create(__snake_case      ) as remote_file:
        with open(__snake_case	,   """rb"""      ) as local_file:
           _lowerCamelCase  :				Optional[Any]      =		local_file.read(__snake_case      )
           while chunk:
              remote_file.write(__snake_case      )
              _lowerCamelCase  :				Tuple      =		local_file.read(__snake_case      )
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			Union[str, Any]	,   __snake_case       :			List[Any]	,   __snake_case       :			Union[str, Any]=False      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				int      =		FileSystems
     if os.path.exists(__snake_case      ):
        if force_download:
           logger.info(F'Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.'      )
        else:
           logger.info(F'Local path already exist: {remote_file_path}. Skipping it as force_upload=False.'      )
           return
     with fs.open(__snake_case      ) as remote_file:
        with open(__snake_case	,   """wb"""      ) as local_file:
           _lowerCamelCase  :				str      =		remote_file.read(__snake_case      )
           while chunk:
              local_file.write(__snake_case      )
              _lowerCamelCase  :				Any      =		remote_file.read(__snake_case      )
  
  