


"""simple docstring"""







class 						_A  :







    """simple docstring"""




    def __init__(					self				:    Optional[Any]				, __UpperCAmelCase				:    int):
      a						:		Tuple      							=			n
      a						:		int      							=			[None] * self.n
      a						:		Union[str, Any]      							=			0  # index of the first element
      a						:		Union[str, Any]      							=			0
      a						:		int      							=			0






    def __len__(					self				:    Optional[Any]):
      return self.size






    def 	__snake_case							(					self				:    Optional[int]):
      return self.size == 0






    def 	__snake_case							(					self				:    str):
      return False if self.is_empty() else self.array[self.front]






    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    Dict):
      if self.size >= self.n:
        raise Exception("QUEUE IS FULL")

      a						:		Union[str, Any]      							=			data
      a						:		List[str]      							=			(self.rear + 1) % self.n
      self.size += 1
      return self






    def 	__snake_case							(					self				:    List[str]):
      if self.size == 0:
        raise Exception("UNDERFLOW")

      a						:		Union[str, Any]      							=			self.array[self.front]
      a						:		Any      							=			None
      a						:		List[Any]      							=			(self.front + 1) % self.n
      self.size -= 1
      return temp












import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Dict:
       # Initialise PyTorch model
       lowerCamelCase   :      Any			 =					TaConfig.from_json_file(_SCREAMING_SNAKE_CASE    )
       print(f'''Building PyTorch model from configuration: {config}'''    )
       lowerCamelCase   :      str			 =					TaForConditionalGeneration(_SCREAMING_SNAKE_CASE    )

       # Load weights from tf checkpoint
       load_tf_weights_in_ta(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

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


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      str  =   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.'
       )
       SCREAMING_SNAKE_CASE__       :      str  =   parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






