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        A	(							__snake_case:   Any   ,    __snake_case:   Optional[Any]   ,    __snake_case:   Optional[Any]     )						->		Optional[Any]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						TaConfig.from_json_file(__snake_case     )
         print(F"""Building PyTorch model from configuration: {config}"""     )
         __magic_name__										=						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__":
   snake_case   :   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(
       """--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."""
   )
   snake_case   :   Optional[int]    		=		parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  snake_case   :   Optional[int]    		=		{
      """configuration_splinter""": ["""SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SplinterConfig"""],
      """tokenization_splinter""": ["""SplinterTokenizer"""],
  }
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   snake_case   :   Dict    		=		["""SplinterTokenizerFast"""]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   snake_case   :   Dict    		=		[
       """SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """SplinterForQuestionAnswering""",
       """SplinterForPreTraining""",
       """SplinterLayer""",
       """SplinterModel""",
       """SplinterPreTrainedModel""",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
   from .tokenization_splinter import SplinterTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_splinter_fast import SplinterTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_splinter import (
        SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SplinterForPreTraining,
        SplinterForQuestionAnswering,
        SplinterLayer,
        SplinterModel,
        SplinterPreTrainedModel,
    )
  
  
  else:
   import sys
  
   snake_case   :   List[str]    		=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)