






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   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				# Initialise PyTorch model.
				# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
				# TapasConfig to False.

				# initialize configuration from json file
				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__    =			0.66_46_94
								lowercase__    =			0.20_79_51
								lowercase__    =			0.12_11_94
								lowercase__    =			True
								lowercase__    =			True
								lowercase__    =			False
								lowercase__    =			0.0_35_25_13

								lowercase__    =			TapasForQuestionAnswering(config=lowerCamelCase_				)
				elif task == "WIKISQL_SUPERVISED":
								# run_task_main.py hparams
								lowercase__    =			4
								lowercase__    =			False
								# hparam_utils.py hparams
								lowercase__    =			36.45_19
								lowercase__    =			0.90_34_21
								lowercase__    =			2_22.0_88
								lowercase__    =			True
								lowercase__    =			True
								lowercase__    =			True
								lowercase__    =			0.76_31_41

								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[:-10] + '''vocab.txt'''		,   model_max_length=512				)
				tokenizer.save_pretrained(lowerCamelCase_				)

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


if __name__ == "__main__":
							A__	:			str			   = 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.'
							)
							A__	:			Optional[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,
							)












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       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       """"""
				lowercase__											=       """hf-legacy"""  # "hf://"" is reserved for hffs




				def __init__(     self						:    List[str],  lowerCamelCase						:    Optional[DatasetInfo] = None,  lowerCamelCase						:    Optional[str] = None,  **lowerCamelCase						:    str,  ):

								'''simple docstring'''
								super().__init__(self,  **lowerCamelCase  )
								lowercase__    =			repo_info
								lowercase__    =			token
								lowercase__    =			None




				def 				lowercase__   (     self						:    Optional[Any]  ):

								'''simple docstring'''
								if self.dir_cache is None:
												lowercase__    =			{}
												for hf_file in self.repo_info.siblings:
																# TODO(QL): add sizes
																lowercase__    =			{
																    '''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 				lowercase__   (     self						:    Any,  lowerCamelCase						:    str,  lowerCamelCase						:    str = "rb",  **lowerCamelCase						:    Any,  ):

								'''simple docstring'''
								if not isinstance(self.repo_info,  lowerCamelCase  ):
												raise NotImplementedError(F"""Open is only implemented for dataset repositories, but got {self.repo_info}"""  )
								lowercase__    =			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 				lowercase__   (     self						:    Dict,  lowerCamelCase						:    Any,  **lowerCamelCase						:    int  ):

								'''simple docstring'''
								self._get_dirs()
								lowercase__    =			self._strip_protocol(lowerCamelCase  )
								if path in self.dir_cache:
												return self.dir_cache[path]
								else:
												raise FileNotFoundError(lowerCamelCase  )




				def 				lowercase__   (     self						:    Optional[Any],  lowerCamelCase						:    List[str],  lowerCamelCase						:    Optional[int]=False,  **lowerCamelCase						:    str  ):

								'''simple docstring'''
								self._get_dirs()
								lowercase__    =			PurePosixPath(path.strip('''/'''  )  )
								lowercase__    =			{}
								for p, f in self.dir_cache.items():
												lowercase__    =			PurePosixPath(p.strip('''/'''  )  )
												lowercase__    =			p.parent
												if root == path:
																lowercase__    =			f
								lowercase__    =			list(paths.values()  )
								if detail:
												return out
								else:
												return sorted(f['''name'''] for f in out  )




