





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=						{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_SCREAMING_SNAKE_CASE 		=						{
    """vocab_file""": {
        """distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt""",
        """distilbert-base-uncased-distilled-squad""": (
            """https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"""
        ),
        """distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt""",
        """distilbert-base-cased-distilled-squad""": (
            """https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"""
        ),
        """distilbert-base-german-cased""": """https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt""",
        """distilbert-base-multilingual-cased""": (
            """https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json""",
        """distilbert-base-uncased-distilled-squad""": (
            """https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"""
        ),
        """distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json""",
        """distilbert-base-cased-distilled-squad""": (
            """https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"""
        ),
        """distilbert-base-german-cased""": (
            """https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"""
        ),
        """distilbert-base-multilingual-cased""": (
            """https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"""
        ),
    },
}

_SCREAMING_SNAKE_CASE 		=						{
    """distilbert-base-uncased""": 5_12,
    """distilbert-base-uncased-distilled-squad""": 5_12,
    """distilbert-base-cased""": 5_12,
    """distilbert-base-cased-distilled-squad""": 5_12,
    """distilbert-base-german-cased""": 5_12,
    """distilbert-base-multilingual-cased""": 5_12,
}


_SCREAMING_SNAKE_CASE 		=						{
    """distilbert-base-uncased""": {"""do_lower_case""": True},
    """distilbert-base-uncased-distilled-squad""": {"""do_lower_case""": True},
    """distilbert-base-cased""": {"""do_lower_case""": False},
    """distilbert-base-cased-distilled-squad""": {"""do_lower_case""": False},
    """distilbert-base-german-cased""": {"""do_lower_case""": False},
    """distilbert-base-multilingual-cased""": {"""do_lower_case""": False},
}
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     str	 =    VOCAB_FILES_NAMES
						__magic_name__:     int	 =    PRETRAINED_VOCAB_FILES_MAP
						__magic_name__:     List[str]	 =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__magic_name__:     Dict	 =    PRETRAINED_INIT_CONFIGURATION
						__magic_name__:     Tuple	 =    ["input_ids", "attention_mask"]
						__magic_name__:     str	 =    DistilBertTokenizer






						def __init__( self : Union[str, Any]						,				_A : str=None						,				_A : Any=None						,				_A : int=True						,				_A : List[Any]="[UNK]"						,				_A : Optional[int]="[SEP]"						,				_A : int="[PAD]"						,				_A : Tuple="[CLS]"						,				_A : List[str]="[MASK]"						,				_A : Tuple=True						,				_A : str=None						,				**_A : int						,				)							->      Dict:







							"""simple docstring"""


							super().__init__(
							    _A						,				tokenizer_file=_A						,				do_lower_case=_A						,				unk_token=_A						,				sep_token=_A						,				pad_token=_A						,				cls_token=_A						,				mask_token=_A						,				tokenize_chinese_chars=_A						,				strip_accents=_A						,				**_A						,				)

							snake_case_						:     Optional[int]     =		json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
							if (
							    normalizer_state.get('lowercase'						,				_A		) != do_lower_case
							    or normalizer_state.get('strip_accents'						,				_A		) != strip_accents
							    or normalizer_state.get('handle_chinese_chars'						,				_A		) != tokenize_chinese_chars
							):
								snake_case_						:     Dict     =		getattr(_A						,				normalizer_state.pop('type'		)		)
								snake_case_						:     Union[str, Any]     =		do_lower_case
								snake_case_						:     Union[str, Any]     =		strip_accents
								snake_case_						:     Any     =		tokenize_chinese_chars
								snake_case_						:     int     =		normalizer_class(**_A		)

							snake_case_						:     str     =		do_lower_case






						def UpperCAmelCase_			( self : str						,				_A : Any						,				_A : Any=None		)							->      Tuple:







							"""simple docstring"""


							snake_case_						:     int     =		[self.cls_token_id] + token_ids_a + [self.sep_token_id]

							if token_ids_a:
								output += token_ids_a + [self.sep_token_id]

							return output






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







							"""simple docstring"""


							snake_case_						:     Tuple     =		self._tokenizer.model.save(_A						,				name=_A		)
							return tuple(_A		)








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,
						)

