"""simple docstring"""





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_camembert import CamembertTokenizer
else:
			lowerCAmelCase_     =	None


lowerCAmelCase_     =	logging.get_logger(__name__)

lowerCAmelCase_     =	{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase_     =	{
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

lowerCAmelCase_     =	{
    '''camembert-base''': 512,
}

lowerCAmelCase_     =	'''▁'''




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						VOCAB_FILES_NAMES
				a			=						PRETRAINED_VOCAB_FILES_MAP
				a			=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				a			=						["input_ids", "attention_mask"]
				a			=						CamembertTokenizer





				def __init__(      self							:							str      ,    _A							:							Optional[int]=None      ,    _A							:							List[str]=None      ,    _A							:							int="<s>"      ,    _A							:							Union[str, Any]="</s>"      ,    _A							:							List[Any]="</s>"      ,    _A							:							List[Any]="<s>"      ,    _A							:							Union[str, Any]="<unk>"      ,    _A							:							Tuple="<pad>"      ,    _A							:							Any="<mask>"      ,    _A							:							Optional[int]=["<s>NOTUSED", "</s>NOTUSED"]      ,    **_A							:							Dict      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    AddedToken(_A      ,    lstrip=_A      ,    rstrip=_A) if isinstance(_A      ,    _A) else mask_token

											super().__init__(
											    _A      ,    tokenizer_file=_A      ,    bos_token=_A      ,    eos_token=_A      ,    sep_token=_A      ,    cls_token=_A      ,    unk_token=_A      ,    pad_token=_A      ,    mask_token=_A      ,    additional_special_tokens=_A      ,    **_A      ,    )

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    vocab_file
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    False if not self.vocab_file else True





				def 		_lowerCAmelCase		(      self							:							Any      ,    _A							:							List[int]      ,    _A							:							Optional[List[int]] = None):



											"""simple docstring"""







											if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    [self.cls_token_id]
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    [self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep





				def 		_lowerCAmelCase		(      self							:							List[Any]      ,    _A							:							List[int]      ,    _A							:							Optional[List[int]] = None):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    [self.sep_token_id]
											_SCREAMING_SNAKE_CASE		:						int			  =    [self.cls_token_id]

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





				def 		_lowerCAmelCase		(      self							:							Dict      ,    _A							:							str      ,    _A							:							Optional[str] = None):



											"""simple docstring"""







											if not self.can_save_slow_tokenizer:
																		raise ValueError(
																		    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
																		    """tokenizer.""")

											if not os.path.isdir(_A):
																		logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
																		return
											_SCREAMING_SNAKE_CASE		:						Dict			  =    os.path.join(
											    _A      ,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

											if os.path.abspath(self.vocab_file) != os.path.abspath(_A):
																		copyfile(self.vocab_file      ,    _A)

											return (out_vocab_file,)



"""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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							# 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
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    TapasConfig.from_json_file(__SCREAMING_SNAKE_CASE  )
							# set absolute/relative position embeddings parameter
							_SCREAMING_SNAKE_CASE		:						Dict			  =    reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														_SCREAMING_SNAKE_CASE		:						str			  =    TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
							elif task == "WTQ":
														# run_task_main.py hparams
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    4
														_SCREAMING_SNAKE_CASE		:						Any			  =    True
														# hparam_utils.py hparams
														_SCREAMING_SNAKE_CASE		:						Any			  =    0.66_46_94
														_SCREAMING_SNAKE_CASE		:						str			  =    0.20_79_51
														_SCREAMING_SNAKE_CASE		:						str			  =    0.12_11_94
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    True
														_SCREAMING_SNAKE_CASE		:						str			  =    True
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    False
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    0.0_35_25_13

														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														_SCREAMING_SNAKE_CASE		:						int			  =    4
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    False
														# hparam_utils.py hparams
														_SCREAMING_SNAKE_CASE		:						Any			  =    36.45_19
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    0.90_34_21
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    2_22.0_88
														_SCREAMING_SNAKE_CASE		:						Any			  =    True
														_SCREAMING_SNAKE_CASE		:						str			  =    True
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    True
														_SCREAMING_SNAKE_CASE		:						Dict			  =    0.76_31_41

														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
							elif task == "TABFACT":
														_SCREAMING_SNAKE_CASE		:						int			  =    TapasForSequenceClassification(config=__SCREAMING_SNAKE_CASE  )
							elif task == "MLM":
														_SCREAMING_SNAKE_CASE		:						int			  =    TapasForMaskedLM(config=__SCREAMING_SNAKE_CASE  )
							elif task == "INTERMEDIATE_PRETRAINING":
														_SCREAMING_SNAKE_CASE		:						int			  =    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}"""  )
							_SCREAMING_SNAKE_CASE		:						str			  =    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__":
			lowerCAmelCase_     =	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.'''
			)
			lowerCAmelCase_     =	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,
			)


