





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_albert import AlbertTokenizer
else:
						_SCREAMING_SNAKE_CASE 		=						None

_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE 		=						{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

_SCREAMING_SNAKE_CASE 		=						{
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
    },
}

_SCREAMING_SNAKE_CASE 		=						{
    '''albert-base-v1''': 5_12,
    '''albert-large-v1''': 5_12,
    '''albert-xlarge-v1''': 5_12,
    '''albert-xxlarge-v1''': 5_12,
    '''albert-base-v2''': 5_12,
    '''albert-large-v2''': 5_12,
    '''albert-xlarge-v2''': 5_12,
    '''albert-xxlarge-v2''': 5_12,
}

_SCREAMING_SNAKE_CASE 		=						'''▁'''
class SCREAMING_SNAKE_CASE_  (   SCREAMING_SNAKE_CASE__   ):
						__magic_name__:     Any	 =    VOCAB_FILES_NAMES
						__magic_name__:     Tuple	 =    PRETRAINED_VOCAB_FILES_MAP
						__magic_name__:     int	 =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__magic_name__:     Union[str, Any]	 =    AlbertTokenizer






						def __init__( self : Any						,				_A : Any=None						,				_A : Dict=None						,				_A : Optional[Any]=True						,				_A : Optional[Any]=True						,				_A : Optional[int]=False						,				_A : List[str]="[CLS]"						,				_A : int="[SEP]"						,				_A : Union[str, Any]="<unk>"						,				_A : Dict="[SEP]"						,				_A : Any="<pad>"						,				_A : str="[CLS]"						,				_A : str="[MASK]"						,				**_A : List[Any]						,				)							->      Any:







									"""simple docstring"""


									snake_case_						:     Dict     =		(
									    AddedToken(_SCREAMING_SNAKE_CASE						,				lstrip=_SCREAMING_SNAKE_CASE						,				rstrip=_SCREAMING_SNAKE_CASE						,				normalized=_SCREAMING_SNAKE_CASE		)
									    if isinstance(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE		)
									    else mask_token
									)

									super().__init__(
									    _SCREAMING_SNAKE_CASE						,				tokenizer_file=_SCREAMING_SNAKE_CASE						,				do_lower_case=_SCREAMING_SNAKE_CASE						,				remove_space=_SCREAMING_SNAKE_CASE						,				keep_accents=_SCREAMING_SNAKE_CASE						,				bos_token=_SCREAMING_SNAKE_CASE						,				eos_token=_SCREAMING_SNAKE_CASE						,				unk_token=_SCREAMING_SNAKE_CASE						,				sep_token=_SCREAMING_SNAKE_CASE						,				pad_token=_SCREAMING_SNAKE_CASE						,				cls_token=_SCREAMING_SNAKE_CASE						,				mask_token=_SCREAMING_SNAKE_CASE						,				**_SCREAMING_SNAKE_CASE						,				)

									snake_case_						:     Optional[int]     =		do_lower_case
									snake_case_						:     Optional[Any]     =		remove_space
									snake_case_						:     Union[str, Any]     =		keep_accents
									snake_case_						:     Union[str, Any]     =		vocab_file
									snake_case_						:     List[str]     =		False if not self.vocab_file else True






						def UpperCAmelCase_			( self : List[Any]						,				_A : int						,				_A : Any = None		)							->      List[int]:







									"""simple docstring"""


									snake_case_						:     Any     =		[self.sep_token_id]
									snake_case_						:     Union[str, Any]     =		[self.cls_token_id]
									if token_ids_a is None:
												return cls + token_ids_a + sep
									return cls + token_ids_a + sep + token_ids_a + sep






						def UpperCAmelCase_			( self : List[str]						,				_A : str						,				_A : Any = None		)							->      List[int]:







									"""simple docstring"""


									snake_case_						:     Dict     =		[self.sep_token_id]
									snake_case_						:     List[Any]     =		[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		) * [0] + len(token_ids_a + sep		) * [1]






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







									"""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(_SCREAMING_SNAKE_CASE		):
												logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
												return
									snake_case_						:     List[Any]     =		os.path.join(
									    _SCREAMING_SNAKE_CASE						,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

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

									return (out_vocab_file,)








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 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				,	__a				,	__a				):
	# 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_						:     Optional[int]     =		TapasConfig.from_json_file(__a				)
	# set absolute/relative position embeddings parameter
	snake_case_						:     List[Any]     =		reset_position_index_per_cell

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

		snake_case_						:     int     =		TapasForQuestionAnswering(config=__a				)
	elif task == "WIKISQL_SUPERVISED":
		# run_task_main.py hparams
		snake_case_						:     int     =		4
		snake_case_						:     Optional[int]     =		False
		# hparam_utils.py hparams
		snake_case_						:     str     =		36.4519
		snake_case_						:     Optional[Any]     =		0.903421
		snake_case_						:     List[Any]     =		222.088
		snake_case_						:     Optional[int]     =		True
		snake_case_						:     Optional[Any]     =		True
		snake_case_						:     str     =		True
		snake_case_						:     int     =		0.763141

		snake_case_						:     str     =		TapasForQuestionAnswering(config=__a				)
	elif task == "TABFACT":
		snake_case_						:     List[Any]     =		TapasForSequenceClassification(config=__a				)
	elif task == "MLM":
		snake_case_						:     Optional[int]     =		TapasForMaskedLM(config=__a				)
	elif task == "INTERMEDIATE_PRETRAINING":
		snake_case_						:     Tuple     =		TapasModel(config=__a				)
	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(__a				,	__a				,	__a				)

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

	# Save tokenizer files
	print(f"""Save tokenizer files to {pytorch_dump_path}"""				)
	snake_case_						:     str     =		TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'				,	model_max_length=5_12				)
	tokenizer.save_pretrained(__a				)

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


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE 		=						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 		=						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,
						)

