


"""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   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			str:
							# 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
							snake_case_          =						TapasConfig.from_json_file(_SCREAMING_SNAKE_CASE			)
							# set absolute/relative position embeddings parameter
							snake_case_          =						reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														snake_case_          =						TapasForQuestionAnswering(config=_SCREAMING_SNAKE_CASE			)
							elif task == "WTQ":
														# run_task_main.py hparams
														snake_case_          =						4
														snake_case_          =						True
														# hparam_utils.py hparams
														snake_case_          =						0.66_4694
														snake_case_          =						0.20_7951
														snake_case_          =						0.12_1194
														snake_case_          =						True
														snake_case_          =						True
														snake_case_          =						False
														snake_case_          =						0.035_2513

														snake_case_          =						TapasForQuestionAnswering(config=_SCREAMING_SNAKE_CASE			)
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														snake_case_          =						4
														snake_case_          =						False
														# hparam_utils.py hparams
														snake_case_          =						36.4519
														snake_case_          =						0.90_3421
														snake_case_          =						222.088
														snake_case_          =						True
														snake_case_          =						True
														snake_case_          =						True
														snake_case_          =						0.76_3141

														snake_case_          =						TapasForQuestionAnswering(config=_SCREAMING_SNAKE_CASE			)
							elif task == "TABFACT":
														snake_case_          =						TapasForSequenceClassification(config=_SCREAMING_SNAKE_CASE			)
							elif task == "MLM":
														snake_case_          =						TapasForMaskedLM(config=_SCREAMING_SNAKE_CASE			)
							elif task == "INTERMEDIATE_PRETRAINING":
														snake_case_          =						TapasModel(config=_SCREAMING_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(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)

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

							# Save tokenizer files
							print(f"""Save tokenizer files to {pytorch_dump_path}"""			)
							snake_case_          =						TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""			,	model_max_length=512			)
							tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE			)

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


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      Optional[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							:      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,
				)







"""simple docstring"""
import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						Any							 =      ["""image_processor""", """tokenizer"""]
							__lowercase:						Dict							 =      """AutoImageProcessor"""
							__lowercase:						List[Any]							 =      """AutoTokenizer"""



							def __init__(			self							: Tuple						,    UpperCAmelCase_							: int=None						,    UpperCAmelCase_							: Tuple=None						,    **UpperCAmelCase_							: str    )   ->Optional[int]:



														"""simple docstring"""



														snake_case_          =						None
														if "feature_extractor" in kwargs:
																					warnings.warn(
																					    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
																					    """ instead."""						,    UpperCAmelCase_						,    )
																					snake_case_          =						kwargs.pop("""feature_extractor"""    )

														snake_case_          =						image_processor if image_processor is not None else feature_extractor
														if image_processor is None:
																					raise ValueError("""You need to specify an `image_processor`."""    )
														if tokenizer is None:
																					raise ValueError("""You need to specify a `tokenizer`."""    )

														super().__init__(UpperCAmelCase_						,    UpperCAmelCase_    )
														snake_case_          =						self.image_processor
														snake_case_          =						False



							def __call__(			self							: Any						,    *UpperCAmelCase_							: Optional[int]						,    **UpperCAmelCase_							: str    )   ->List[str]:



														"""simple docstring"""



														if self._in_target_context_manager:
																					return self.current_processor(*UpperCAmelCase_						,    **UpperCAmelCase_    )

														snake_case_          =						kwargs.pop("""images"""						,    UpperCAmelCase_    )
														snake_case_          =						kwargs.pop("""text"""						,    UpperCAmelCase_    )
														if len(UpperCAmelCase_    ) > 0:
																					snake_case_          =						args[0]
																					snake_case_          =						args[1:]

														if images is None and text is None:
																					raise ValueError("""You need to specify either an `images` or `text` input to process."""    )

														if images is not None:
																					snake_case_          =						self.image_processor(UpperCAmelCase_						,    *UpperCAmelCase_						,    **UpperCAmelCase_    )
														if text is not None:
																					snake_case_          =						self.tokenizer(UpperCAmelCase_						,    **UpperCAmelCase_    )

														if text is None:
																					return inputs
														elif images is None:
																					return encodings
														else:
																					snake_case_          =						encodings["""input_ids"""]
																					return inputs



							def 						lowerCAmelCase    (			self							: Optional[Any]						,    *UpperCAmelCase_							: Optional[Any]						,    **UpperCAmelCase_							: Tuple    )   ->Optional[int]:



														"""simple docstring"""



														return self.tokenizer.batch_decode(*UpperCAmelCase_						,    **UpperCAmelCase_    )



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



														"""simple docstring"""



														return self.tokenizer.decode(*UpperCAmelCase_						,    **UpperCAmelCase_    )



							@contextmanager
							def 						lowerCAmelCase    (			self							: List[str]    )   ->Dict:



														"""simple docstring"""



														warnings.warn(
														    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
														    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
														    """your images inputs, or in a separate call."""    )
														snake_case_          =						True
														snake_case_          =						self.tokenizer
														yield
														snake_case_          =						self.image_processor
														snake_case_          =						False



							def 						lowerCAmelCase    (			self							: int						,    UpperCAmelCase_							: Union[str, Any]						,    UpperCAmelCase_							: str=False						,    UpperCAmelCase_							: str=None    )   ->Tuple:



														"""simple docstring"""



														if added_vocab is None:
																					snake_case_          =						self.tokenizer.get_added_vocab()

														snake_case_          =						{}

														while tokens:
																					snake_case_          =						re.search(R"""<s_(.*?)>"""						,    UpperCAmelCase_						,    re.IGNORECASE    )
																					if start_token is None:
																												break
																					snake_case_          =						start_token.group(1    )
																					snake_case_          =						re.search(RF"""</s_{key}>"""						,    UpperCAmelCase_						,    re.IGNORECASE    )
																					snake_case_          =						start_token.group()
																					if end_token is None:
																												snake_case_          =						tokens.replace(UpperCAmelCase_						,    """"""    )
																					else:
																												snake_case_          =						end_token.group()
																												snake_case_          =						re.escape(UpperCAmelCase_    )
																												snake_case_          =						re.escape(UpperCAmelCase_    )
																												snake_case_          =						re.search(F"""{start_token_escaped}(.*?){end_token_escaped}"""						,    UpperCAmelCase_						,    re.IGNORECASE    )
																												if content is not None:
																																			snake_case_          =						content.group(1    ).strip()
																																			if r"<s_" in content and r"</s_" in content:  # non-leaf node
																																										snake_case_          =						self.tokenajson(UpperCAmelCase_						,    is_inner_value=UpperCAmelCase_						,    added_vocab=UpperCAmelCase_    )
																																										if value:
																																																	if len(UpperCAmelCase_    ) == 1:
																																																								snake_case_          =						value[0]
																																																	snake_case_          =						value
																																			else:  # leaf nodes
																																										snake_case_          =						[]
																																										for leaf in content.split(R"""<sep/>"""    ):
																																																	snake_case_          =						leaf.strip()
																																																	if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																																																								snake_case_          =						leaf[1:-2]  # for categorical special tokens
																																																	output[key].append(UpperCAmelCase_    )
																																										if len(output[key]    ) == 1:
																																																	snake_case_          =						output[key][0]

																												snake_case_          =						tokens[tokens.find(UpperCAmelCase_    ) + len(UpperCAmelCase_    ) :].strip()
																												if tokens[:6] == r"<sep/>":  # non-leaf nodes
																																			return [output] + self.tokenajson(tokens[6:]						,    is_inner_value=UpperCAmelCase_						,    added_vocab=UpperCAmelCase_    )

														if len(UpperCAmelCase_    ):
																					return [output] if is_inner_value else output
														else:
																					return [] if is_inner_value else {"text_sequence": tokens}



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



														"""simple docstring"""



														warnings.warn(
														    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""						,    UpperCAmelCase_						,    )
														return self.image_processor_class



							@property
							def 						lowerCAmelCase    (			self							: List[str]    )   ->str:



														"""simple docstring"""



														warnings.warn(
														    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""						,    UpperCAmelCase_						,    )
														return self.image_processor



