



'''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  snake_case_     (					_lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						Union[str, Any]  )      ->   List[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
							UpperCAmelCase :       List[str] 							=		TapasConfig.from_json_file(_lowerCAmelCase  )
							# set absolute/relative position embeddings parameter
							UpperCAmelCase :       Optional[Any] 							=		reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														UpperCAmelCase :       Any 							=		TapasForQuestionAnswering(config=_lowerCAmelCase  )
							elif task == "WTQ":
														# run_task_main.py hparams
														UpperCAmelCase :       int 							=		4
														UpperCAmelCase :       int 							=		True
														# hparam_utils.py hparams
														UpperCAmelCase :       Union[str, Any] 							=		0.6_6_4_6_9_4
														UpperCAmelCase :       Tuple 							=		0.2_0_7_9_5_1
														UpperCAmelCase :       Dict 							=		0.1_2_1_1_9_4
														UpperCAmelCase :       Optional[int] 							=		True
														UpperCAmelCase :       str 							=		True
														UpperCAmelCase :       List[Any] 							=		False
														UpperCAmelCase :       Tuple 							=		0.0_3_5_2_5_1_3

														UpperCAmelCase :       Optional[Any] 							=		TapasForQuestionAnswering(config=_lowerCAmelCase  )
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														UpperCAmelCase :       Optional[Any] 							=		4
														UpperCAmelCase :       Tuple 							=		False
														# hparam_utils.py hparams
														UpperCAmelCase :       Union[str, Any] 							=		3_6.4_5_1_9
														UpperCAmelCase :       Optional[Any] 							=		0.9_0_3_4_2_1
														UpperCAmelCase :       Dict 							=		2_2_2.0_8_8
														UpperCAmelCase :       int 							=		True
														UpperCAmelCase :       Tuple 							=		True
														UpperCAmelCase :       Tuple 							=		True
														UpperCAmelCase :       Any 							=		0.7_6_3_1_4_1

														UpperCAmelCase :       Tuple 							=		TapasForQuestionAnswering(config=_lowerCAmelCase  )
							elif task == "TABFACT":
														UpperCAmelCase :       List[str] 							=		TapasForSequenceClassification(config=_lowerCAmelCase  )
							elif task == "MLM":
														UpperCAmelCase :       List[str] 							=		TapasForMaskedLM(config=_lowerCAmelCase  )
							elif task == "INTERMEDIATE_PRETRAINING":
														UpperCAmelCase :       List[Any] 							=		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}"""  )
							UpperCAmelCase :       Dict 							=		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__":
						UpperCamelCase__:   Tuple								=			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."
						)
						UpperCamelCase__:   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 warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       """MCTCTFeatureExtractor"""
	lowerCamelCase__			 =       """AutoTokenizer"""







	def __init__(						self			:     Dict		,			__snake_case			:     Optional[int]		,			__snake_case			:     List[str]		)			->		str:
								super().__init__(__snake_case		,			__snake_case		)
								UpperCAmelCase :       List[Any] 							=		self.feature_extractor
								UpperCAmelCase :       Union[str, Any] 							=		False







	def __call__(						self			:     Any		,			*__snake_case			:     List[str]		,			**__snake_case			:     Any		)			->		List[Any]:
								# For backward compatibility
								if self._in_target_context_manager:
															return self.current_processor(*__snake_case		,			**__snake_case		)

								if "raw_speech" in kwargs:
															warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''		)
															UpperCAmelCase :       int 							=		kwargs.pop('''raw_speech'''		)
								else:
															UpperCAmelCase :       Union[str, Any] 							=		kwargs.pop('''audio'''		,			__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		kwargs.pop('''sampling_rate'''		,			__snake_case		)
								UpperCAmelCase :       Dict 							=		kwargs.pop('''text'''		,			__snake_case		)
								if len(__snake_case		) > 0:
															UpperCAmelCase :       Any 							=		args[0]
															UpperCAmelCase :       Optional[int] 							=		args[1:]

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

								if audio is not None:
															UpperCAmelCase :       List[str] 							=		self.feature_extractor(__snake_case		,			*__snake_case		,			sampling_rate=__snake_case		,			**__snake_case		)
								if text is not None:
															UpperCAmelCase :       int 							=		self.tokenizer(__snake_case		,			**__snake_case		)

								if text is None:
															return inputs
								elif audio is None:
															return encodings
								else:
															UpperCAmelCase :       str 							=		encodings['''input_ids''']
															return inputs







	def      A	(						self			:     List[Any]		,			*__snake_case			:     List[Any]		,			**__snake_case			:     List[Any]		)			->		str:
								return self.tokenizer.batch_decode(*__snake_case		,			**__snake_case		)







	def      A	(						self			:     List[Any]		,			*__snake_case			:     int		,			**__snake_case			:     Optional[int]		)			->		Any:
								# For backward compatibility
								if self._in_target_context_manager:
															return self.current_processor.pad(*__snake_case		,			**__snake_case		)

								UpperCAmelCase :       List[Any] 							=		kwargs.pop('''input_features'''		,			__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		kwargs.pop('''labels'''		,			__snake_case		)
								if len(__snake_case		) > 0:
															UpperCAmelCase :       List[str] 							=		args[0]
															UpperCAmelCase :       List[Any] 							=		args[1:]

								if input_features is not None:
															UpperCAmelCase :       Tuple 							=		self.feature_extractor.pad(__snake_case		,			*__snake_case		,			**__snake_case		)
								if labels is not None:
															UpperCAmelCase :       Optional[int] 							=		self.tokenizer.pad(__snake_case		,			**__snake_case		)

								if labels is None:
															return input_features
								elif input_features is None:
															return labels
								else:
															UpperCAmelCase :       List[str] 							=		labels['''input_ids''']
															return input_features







	def      A	(						self			:     Union[str, Any]		,			*__snake_case			:     Optional[Any]		,			**__snake_case			:     Optional[int]		)			->		Optional[Any]:
								return self.tokenizer.decode(*__snake_case		,			**__snake_case		)







	@contextmanager
	def      A	(						self			:     Any		)			->		Optional[int]:
								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 audio inputs, or in a separate call.'''		)
								UpperCAmelCase :       Dict 							=		True
								UpperCAmelCase :       List[Any] 							=		self.tokenizer
								yield
								UpperCAmelCase :       Tuple 							=		self.feature_extractor
								UpperCAmelCase :       List[Any] 							=		False





