






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   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Tuple   =   TapasConfig.from_json_file(snake_case			)
				# set absolute/relative position embeddings parameter
				__magic_name__		:Optional[int]   =   reset_position_index_per_cell

				# set remaining parameters of TapasConfig as well as the model based on the task
				if task == "SQA":
								__magic_name__		:Any   =   TapasForQuestionAnswering(config=snake_case			)
				elif task == "WTQ":
								# run_task_main.py hparams
								__magic_name__		:Any   =   4
								__magic_name__		:int   =   True
								# hparam_utils.py hparams
								__magic_name__		:int   =   0.66_4694
								__magic_name__		:str   =   0.20_7951
								__magic_name__		:List[Any]   =   0.12_1194
								__magic_name__		:List[Any]   =   True
								__magic_name__		:int   =   True
								__magic_name__		:Tuple   =   False
								__magic_name__		:Tuple   =   0.035_2513

								__magic_name__		:str   =   TapasForQuestionAnswering(config=snake_case			)
				elif task == "WIKISQL_SUPERVISED":
								# run_task_main.py hparams
								__magic_name__		:Optional[Any]   =   4
								__magic_name__		:Union[str, Any]   =   False
								# hparam_utils.py hparams
								__magic_name__		:Optional[int]   =   36.4519
								__magic_name__		:Dict   =   0.90_3421
								__magic_name__		:Any   =   222.088
								__magic_name__		:str   =   True
								__magic_name__		:int   =   True
								__magic_name__		:int   =   True
								__magic_name__		:List[str]   =   0.76_3141

								__magic_name__		:Optional[Any]   =   TapasForQuestionAnswering(config=snake_case			)
				elif task == "TABFACT":
								__magic_name__		:str   =   TapasForSequenceClassification(config=snake_case			)
				elif task == "MLM":
								__magic_name__		:Any   =   TapasForMaskedLM(config=snake_case			)
				elif task == "INTERMEDIATE_PRETRAINING":
								__magic_name__		:str   =   TapasModel(config=snake_case			)
				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(snake_case,	snake_case,	snake_case			)

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

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

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      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."""
					)
					SCREAMING_SNAKE_CASE__   :      int				=		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,
					)














from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	''''''
				a__       							=	'''hf-legacy'''  # "hf://"" is reserved for hffs







				def __init__(    self							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								super().__init__(self							,       **__lowerCAmelCase  )
								__magic_name__		:List[Any]   =   repo_info
								__magic_name__		:Dict   =   token
								__magic_name__		:Optional[Any]   =   None







				def       A						(    self  ):
								"""simple docstring"""
								if self.dir_cache is None:
												__magic_name__		:Any   =   {}
												for hf_file in self.repo_info.siblings:
																# TODO(QL): add sizes
																__magic_name__		:Optional[int]   =   {
																    '''name''': hf_file.rfilename,
																    '''size''': None,
																    '''type''': '''file''',
																}
																self.dir_cache.update(
																    {
																        str(__lowerCAmelCase  ): {'''name''': str(__lowerCAmelCase  ), '''size''': None, '''type''': '''directory'''}
																        for d in list(PurePosixPath(hf_file.rfilename  ).parents  )[:-1]
																    }  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase = "rb"							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								if not isinstance(self.repo_info							,       __lowerCAmelCase  ):
												raise NotImplementedError(F'''Open is only implemented for dataset repositories, but got {self.repo_info}'''  )
								__magic_name__		:Union[str, Any]   =   hf_hub_url(self.repo_info.id							,       __lowerCAmelCase							,       revision=self.repo_info.sha  )
								return fsspec.open(
								    __lowerCAmelCase							,       mode=__lowerCAmelCase							,       headers=get_authentication_headers_for_url(__lowerCAmelCase							,       use_auth_token=self.token  )							,       client_kwargs={'''trust_env''': True}							,       ).open()







				def       A						(    self							,       __lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								self._get_dirs()
								__magic_name__		:str   =   self._strip_protocol(__lowerCAmelCase  )
								if path in self.dir_cache:
												return self.dir_cache[path]
								else:
												raise FileNotFoundError(__lowerCAmelCase  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase=False							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								self._get_dirs()
								__magic_name__		:Union[str, Any]   =   PurePosixPath(path.strip('''/'''  )  )
								__magic_name__		:Dict   =   {}
								for p, f in self.dir_cache.items():
												__magic_name__		:int   =   PurePosixPath(p.strip('''/'''  )  )
												__magic_name__		:Tuple   =   p.parent
												if root == path:
																__magic_name__		:Optional[Any]   =   f
								__magic_name__		:List[Any]   =   list(paths.values()  )
								if detail:
												return out
								else:
												return sorted(f['''name'''] for f in out  )






