




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__ (snake_case   :		Dict		,	snake_case   :		Optional[int]		,	snake_case   :		Any		,	snake_case   :		Union[str, Any]		,	snake_case   :		Tuple				)     ->						Any:
					# 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
					__UpperCamelCase					:	Dict             =      TapasConfig.from_json_file(snake_case				)
					# set absolute/relative position embeddings parameter
					__UpperCamelCase					:	Union[str, Any]             =      reset_position_index_per_cell

					# set remaining parameters of TapasConfig as well as the model based on the task
					if task == "SQA":
										__UpperCamelCase					:	Tuple             =      TapasForQuestionAnswering(config=snake_case				)
					elif task == "WTQ":
										# run_task_main.py hparams
										__UpperCamelCase					:	Dict             =      4
										__UpperCamelCase					:	Any             =      True
										# hparam_utils.py hparams
										__UpperCamelCase					:	Optional[int]             =      0.664694
										__UpperCamelCase					:	Dict             =      0.207951
										__UpperCamelCase					:	List[Any]             =      0.121194
										__UpperCamelCase					:	Optional[Any]             =      True
										__UpperCamelCase					:	List[Any]             =      True
										__UpperCamelCase					:	Optional[Any]             =      False
										__UpperCamelCase					:	List[Any]             =      0.0352513

										__UpperCamelCase					:	Tuple             =      TapasForQuestionAnswering(config=snake_case				)
					elif task == "WIKISQL_SUPERVISED":
										# run_task_main.py hparams
										__UpperCamelCase					:	int             =      4
										__UpperCamelCase					:	Tuple             =      False
										# hparam_utils.py hparams
										__UpperCamelCase					:	Tuple             =      36.4519
										__UpperCamelCase					:	List[Any]             =      0.903421
										__UpperCamelCase					:	Dict             =      222.088
										__UpperCamelCase					:	List[Any]             =      True
										__UpperCamelCase					:	Union[str, Any]             =      True
										__UpperCamelCase					:	Optional[Any]             =      True
										__UpperCamelCase					:	int             =      0.763141

										__UpperCamelCase					:	List[str]             =      TapasForQuestionAnswering(config=snake_case				)
					elif task == "TABFACT":
										__UpperCamelCase					:	Optional[int]             =      TapasForSequenceClassification(config=snake_case				)
					elif task == "MLM":
										__UpperCamelCase					:	str             =      TapasForMaskedLM(config=snake_case				)
					elif task == "INTERMEDIATE_PRETRAINING":
										__UpperCamelCase					:	Union[str, Any]             =      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}'''				)
					__UpperCamelCase					:	List[str]             =      TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""		,	model_max_length=5_12				)
					tokenizer.save_pretrained(snake_case				)

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


if __name__ == "__main__":
	a__		     =     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__		     =     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,
	)












import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


a__		     =     logging.get_logger(__name__)


class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       int      	=		['input_ids', 'attention_mask']






	def __init__(					self		:						List[Any]					,     lowerCAmelCase		:						Union[str, Any]="</s>"					,     lowerCAmelCase		:						Tuple="<unk>"					,     lowerCAmelCase		:						Union[str, Any]="<pad>"					,     lowerCAmelCase		:						Tuple=125					,     lowerCAmelCase		:						str=None					,     **lowerCAmelCase		:						Optional[Any]					,     )	->       None:







						"""simple docstring"""







						if extra_ids > 0 and additional_special_tokens is None:
											__UpperCamelCase					:	str             =      [F'''<extra_id_{i}>''' for i in range(lowerCAmelCase	)]
						elif extra_ids > 0 and additional_special_tokens is not None:
											# Check that we have the right number of extra_id special tokens
											__UpperCamelCase					:	Tuple             =      len(set(filter(lambda lowerCAmelCase	: bool("""extra_id""" in str(lowerCAmelCase	)	)					,     lowerCAmelCase	)	)	)
											if extra_tokens != extra_ids:
																raise ValueError(
																    F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
																    """ provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"""
																    """ extra_ids tokens"""	)

						__UpperCamelCase					:	Tuple             =      AddedToken(lowerCAmelCase					,     lstrip=lowerCAmelCase					,     rstrip=lowerCAmelCase	) if isinstance(lowerCAmelCase					,     lowerCAmelCase	) else pad_token
						__UpperCamelCase					:	Tuple             =      AddedToken(lowerCAmelCase					,     lstrip=lowerCAmelCase					,     rstrip=lowerCAmelCase	) if isinstance(lowerCAmelCase					,     lowerCAmelCase	) else eos_token
						__UpperCamelCase					:	int             =      AddedToken(lowerCAmelCase					,     lstrip=lowerCAmelCase					,     rstrip=lowerCAmelCase	) if isinstance(lowerCAmelCase					,     lowerCAmelCase	) else unk_token

						super().__init__(
						    eos_token=lowerCAmelCase					,     unk_token=lowerCAmelCase					,     pad_token=lowerCAmelCase					,     extra_ids=lowerCAmelCase					,     additional_special_tokens=lowerCAmelCase					,     **lowerCAmelCase					,     )

						__UpperCamelCase					:	Any             =      extra_ids

						__UpperCamelCase					:	List[Any]             =      2**8  # utf is 8 bits

						# define special tokens dict
						__UpperCamelCase					:	Dict[int, str]             =      {
						    self.pad_token: 0,
						    self.eos_token: 1,
						    self.unk_token: 2,
						}
						__UpperCamelCase					:	int             =      len(self.special_tokens_encoder	)
						__UpperCamelCase					:	Tuple             =      len(lowerCAmelCase	)
						for i, token in enumerate(lowerCAmelCase	):
											__UpperCamelCase					:	Optional[int]             =      self.vocab_size + i - n
						__UpperCamelCase					:	Dict[str, int]             =      {v: k for k, v in self.special_tokens_encoder.items()}






	@property
	def 					lowerCamelCase__     (					self		:						Any	)	->       Optional[int]:







						"""simple docstring"""







						return self._utf_vocab_size + self._num_special_tokens + self._extra_ids






	def 					lowerCamelCase__     (					self		:						Optional[int]					,     lowerCAmelCase		:						List[int]					,     lowerCAmelCase		:						Optional[List[int]] = None					,     lowerCAmelCase		:						bool = False	)	->       List[int]:







						"""simple docstring"""







						if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=lowerCAmelCase					,     token_ids_a=lowerCAmelCase					,     already_has_special_tokens=lowerCAmelCase	)

						# normal case: some special tokens
						if token_ids_a is None:
											return ([0] * len(lowerCAmelCase	)) + [1]
						return ([0] * len(lowerCAmelCase	)) + [1] + ([0] * len(lowerCAmelCase	)) + [1]






	def 					lowerCamelCase__     (					self		:						Tuple					,     lowerCAmelCase		:						List[int]	)	->       List[int]:







						"""simple docstring"""







						if len(lowerCAmelCase	) > 0 and token_ids[-1] == self.eos_token_id:
											warnings.warn(
											    F'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
											    """ eos tokens being added."""	)
											return token_ids
						else:
											return token_ids + [self.eos_token_id]






	def 					lowerCamelCase__     (					self		:						List[Any]					,     lowerCAmelCase		:						List[int]					,     lowerCAmelCase		:						Optional[List[int]] = None	)	->       List[int]:







						"""simple docstring"""







						__UpperCamelCase					:	Any             =      [self.eos_token_id]

						if token_ids_a is None:
											return len(token_ids_a + eos	) * [0]
						return len(token_ids_a + eos + token_ids_a + eos	) * [0]






	def 					lowerCamelCase__     (					self		:						List[str]					,     lowerCAmelCase		:						List[int]					,     lowerCAmelCase		:						Optional[List[int]] = None	)	->       List[int]:







						"""simple docstring"""







						__UpperCamelCase					:	List[str]             =      self._add_eos_if_not_present(lowerCAmelCase	)
						if token_ids_a is None:
											return token_ids_a
						else:
											__UpperCamelCase					:	List[Any]             =      self._add_eos_if_not_present(lowerCAmelCase	)
											return token_ids_a + token_ids_a






	def 					lowerCamelCase__     (					self		:						str					,     lowerCAmelCase		:						str	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[int]             =      [chr(lowerCAmelCase	) for i in text.encode("""utf-8"""	)]
						return tokens






	def 					lowerCamelCase__     (					self		:						Union[str, Any]					,     lowerCAmelCase		:						Optional[int]	)	->       int:







						"""simple docstring"""







						if token in self.special_tokens_encoder:
											__UpperCamelCase					:	Dict             =      self.special_tokens_encoder[token]
						elif token in self.added_tokens_encoder:
											__UpperCamelCase					:	Tuple             =      self.added_tokens_encoder[token]
						elif len(lowerCAmelCase	) != 1:
											__UpperCamelCase					:	Dict             =      self.unk_token_id
						else:
											__UpperCamelCase					:	Union[str, Any]             =      ord(lowerCAmelCase	) + self._num_special_tokens
						return token_id






	def 					lowerCamelCase__     (					self		:						Union[str, Any]					,     lowerCAmelCase		:						Dict	)	->       Optional[Any]:







						"""simple docstring"""







						if index in self.special_tokens_decoder:
											__UpperCamelCase					:	str             =      self.special_tokens_decoder[index]
						else:
											__UpperCamelCase					:	Any             =      chr(index - self._num_special_tokens	)
						return token






	def 					lowerCamelCase__     (					self		:						str					,     lowerCAmelCase		:						List[Any]	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	Union[str, Any]             =      b""""""
						for token in tokens:
											if token in self.special_tokens_decoder:
																__UpperCamelCase					:	Optional[Any]             =      self.special_tokens_decoder[token].encode("""utf-8"""	)
											elif token in self.added_tokens_decoder:
																__UpperCamelCase					:	Optional[int]             =      self.special_tokens_decoder[token].encode("""utf-8"""	)
											elif token in self.special_tokens_encoder:
																__UpperCamelCase					:	Tuple             =      token.encode("""utf-8"""	)
											elif token in self.added_tokens_encoder:
																__UpperCamelCase					:	Tuple             =      token.encode("""utf-8"""	)
											else:
																__UpperCamelCase					:	Union[str, Any]             =      bytes([ord(lowerCAmelCase	)]	)
											bstring += tok_string
						__UpperCamelCase					:	Optional[int]             =      bstring.decode("""utf-8"""					,     errors="""ignore"""	)
						return string






	def 					lowerCamelCase__     (					self		:						Optional[int]					,     lowerCAmelCase		:						str					,     lowerCAmelCase		:						Optional[str] = None	)	->       Tuple[str]:







						"""simple docstring"""







						return ()






