"""simple docstring"""






import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()


def      UpperCAmelCase__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:Optional[int]   ,						lowerCAmelCase__	:str   ,						lowerCAmelCase__	:Any   ,						lowerCAmelCase__	:Any						)					->       str:
      '''simple docstring'''


      lowercase								=      TapasConfig.from_json_file(lowerCAmelCase__						)
      # set absolute/relative position embeddings parameter
      lowercase								=      reset_position_index_per_cell

      # set remaining parameters of TapasConfig as well as the model based on the task
      if task == "SQA":
            lowercase								=      TapasForQuestionAnswering(config=lowerCAmelCase__						)
      elif task == "WTQ":
            # run_task_main.py hparams
            lowercase								=      4
            lowercase								=      True
            # hparam_utils.py hparams
            lowercase								=      0.664_694
            lowercase								=      0.207_951
            lowercase								=      0.121_194
            lowercase								=      True
            lowercase								=      True
            lowercase								=      False
            lowercase								=      0.0_352_513

            lowercase								=      TapasForQuestionAnswering(config=lowerCAmelCase__						)
      elif task == "WIKISQL_SUPERVISED":
            # run_task_main.py hparams
            lowercase								=      4
            lowercase								=      False
            # hparam_utils.py hparams
            lowercase								=      36.4_519
            lowercase								=      0.903_421
            lowercase								=      222.088
            lowercase								=      True
            lowercase								=      True
            lowercase								=      True
            lowercase								=      0.763_141

            lowercase								=      TapasForQuestionAnswering(config=lowerCAmelCase__						)
      elif task == "TABFACT":
            lowercase								=      TapasForSequenceClassification(config=lowerCAmelCase__						)
      elif task == "MLM":
            lowercase								=      TapasForMaskedLM(config=lowerCAmelCase__						)
      elif task == "INTERMEDIATE_PRETRAINING":
            lowercase								=      TapasModel(config=lowerCAmelCase__						)
      else:
            raise ValueError(f'Task {task} not supported.'						)

      print(f'Building PyTorch model from configuration: {config}'						)
      # Load weights from tf checkpoint
      load_tf_weights_in_tapas(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__						)

      # Save pytorch-model (weights and configuration)
      print(f'Save PyTorch model to {pytorch_dump_path}'						)
      model.save_pretrained(lowerCAmelCase__						)

      # Save tokenizer files
      print(f'Save tokenizer files to {pytorch_dump_path}'						)
      lowercase								=      TapasTokenizer(vocab_file=tf_checkpoint_path[:-1_0] + """vocab.txt"""   ,						model_max_length=5_1_2						)
      tokenizer.save_pretrained(lowerCAmelCase__						)

      print("""Used relative position embeddings:"""   ,						model.config.reset_position_index_per_cell						)


if __name__ == "__main__":
       __lowerCAmelCase       :			Any   	=argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA."""
       )
       parser.add_argument(
           """--reset_position_index_per_cell""",
           default=False,
           action="""store_true""",
           help="""Whether to use relative position embeddings or not. Defaults to True.""",
       )
       parser.add_argument(
           """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
       )
       parser.add_argument(
           """--tapas_config_file""",
           default=None,
           type=str,
           required=True,
           help=(
               """The config json file corresponding to the pre-trained TAPAS 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."""
       )
       __lowerCAmelCase       :			List[Any]   	=parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.task,
           args.reset_position_index_per_cell,
           args.tf_checkpoint_path,
           args.tapas_config_file,
           args.pytorch_dump_path,
       )










"""simple docstring"""






import functools






def      lowercase__ (					lowerCAmelCase__   :			str							,			lowerCAmelCase__   :			str			)    ->			int:



			'''simple docstring'''
			a__  :	Any		 =			len(lowerCAmelCase__			)
			a__  :	Optional[int]		 =			len(lowerCAmelCase__			)

			@functools.cache
			def min_distance(lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int			) -> int:
						# if first word index is overflow - delete all from the second word
						if indexa >= len_worda:
									return len_worda - indexa
						# if second word index is overflow - delete all from the first word
						if indexa >= len_worda:
									return len_worda - indexa
						a__  :	List[Any]		 =			int(worda[indexa] != worda[indexa]			)  # current letters not identical
						return min(
						    1 + min_distance(indexa + 1							,			lowerCAmelCase__			)							,			1 + min_distance(lowerCAmelCase__							,			indexa + 1			)							,			diff + min_distance(indexa + 1							,			indexa + 1			)							,			)

			return min_distance(0							,			0			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()