


'''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     (						_lowercase :     int      , _lowercase :     Union[str, Any]      , _lowercase :     Dict      , _lowercase :     Any      , _lowercase :     int  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Tuple  							=      TapasConfig.from_json_file(__lowerCamelCase  )
     # set absolute/relative position embeddings parameter
     __UpperCAmelCase     :			Union[str, Any]  							=      reset_position_index_per_cell

     # set remaining parameters of TapasConfig as well as the model based on the task
     if task == "SQA":
          __UpperCAmelCase     :			str  							=      TapasForQuestionAnswering(config=__lowerCamelCase  )
     elif task == "WTQ":
          # run_task_main.py hparams
          __UpperCAmelCase     :			List[Any]  							=      4
          __UpperCAmelCase     :			Dict  							=      True
          # hparam_utils.py hparams
          __UpperCAmelCase     :			Optional[int]  							=      0.66_4694
          __UpperCAmelCase     :			List[str]  							=      0.20_7951
          __UpperCAmelCase     :			Union[str, Any]  							=      0.12_1194
          __UpperCAmelCase     :			Dict  							=      True
          __UpperCAmelCase     :			Union[str, Any]  							=      True
          __UpperCAmelCase     :			List[Any]  							=      False
          __UpperCAmelCase     :			str  							=      0.035_2513

          __UpperCAmelCase     :			List[Any]  							=      TapasForQuestionAnswering(config=__lowerCamelCase  )
     elif task == "WIKISQL_SUPERVISED":
          # run_task_main.py hparams
          __UpperCAmelCase     :			Dict  							=      4
          __UpperCAmelCase     :			Union[str, Any]  							=      False
          # hparam_utils.py hparams
          __UpperCAmelCase     :			Optional[int]  							=      36.4519
          __UpperCAmelCase     :			List[Any]  							=      0.90_3421
          __UpperCAmelCase     :			Union[str, Any]  							=      222.088
          __UpperCAmelCase     :			Tuple  							=      True
          __UpperCAmelCase     :			Any  							=      True
          __UpperCAmelCase     :			Dict  							=      True
          __UpperCAmelCase     :			str  							=      0.76_3141

          __UpperCAmelCase     :			Dict  							=      TapasForQuestionAnswering(config=__lowerCamelCase  )
     elif task == "TABFACT":
          __UpperCAmelCase     :			List[str]  							=      TapasForSequenceClassification(config=__lowerCamelCase  )
     elif task == "MLM":
          __UpperCAmelCase     :			Optional[int]  							=      TapasForMaskedLM(config=__lowerCamelCase  )
     elif task == "INTERMEDIATE_PRETRAINING":
          __UpperCAmelCase     :			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      :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."
      )
      __UpperCAmelCase      :List[str]			    =       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 json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


__UpperCAmelCase      :Dict			    =       logging.get_logger(__name__)

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

__UpperCAmelCase      :Optional[int]			    =       {
    "vocab_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/vocab.txt",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/vocab.txt",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt"
        ),
        "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt",
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt"
        ),
        "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt",
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json"
        ),
        "bert-base-multilingual-cased": (
            "https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-cased": (
            "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json"
        ),
    },
}

__UpperCAmelCase      :Union[str, Any]			    =       {
    "bert-base-uncased": 5_1_2,
    "bert-large-uncased": 5_1_2,
    "bert-base-cased": 5_1_2,
    "bert-large-cased": 5_1_2,
    "bert-base-multilingual-uncased": 5_1_2,
    "bert-base-multilingual-cased": 5_1_2,
    "bert-base-chinese": 5_1_2,
    "bert-base-german-cased": 5_1_2,
    "bert-large-uncased-whole-word-masking": 5_1_2,
    "bert-large-cased-whole-word-masking": 5_1_2,
    "bert-large-uncased-whole-word-masking-finetuned-squad": 5_1_2,
    "bert-large-cased-whole-word-masking-finetuned-squad": 5_1_2,
    "bert-base-cased-finetuned-mrpc": 5_1_2,
    "bert-base-german-dbmdz-cased": 5_1_2,
    "bert-base-german-dbmdz-uncased": 5_1_2,
    "TurkuNLP/bert-base-finnish-cased-v1": 5_1_2,
    "TurkuNLP/bert-base-finnish-uncased-v1": 5_1_2,
    "wietsedv/bert-base-dutch-cased": 5_1_2,
}

__UpperCAmelCase      :str			    =       {
    "bert-base-uncased": {"do_lower_case": True},
    "bert-large-uncased": {"do_lower_case": True},
    "bert-base-cased": {"do_lower_case": False},
    "bert-large-cased": {"do_lower_case": False},
    "bert-base-multilingual-uncased": {"do_lower_case": True},
    "bert-base-multilingual-cased": {"do_lower_case": False},
    "bert-base-chinese": {"do_lower_case": False},
    "bert-base-german-cased": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False},
    "bert-base-cased-finetuned-mrpc": {"do_lower_case": False},
    "bert-base-german-dbmdz-cased": {"do_lower_case": False},
    "bert-base-german-dbmdz-uncased": {"do_lower_case": True},
    "TurkuNLP/bert-base-finnish-cased-v1": {"do_lower_case": False},
    "TurkuNLP/bert-base-finnish-uncased-v1": {"do_lower_case": True},
    "wietsedv/bert-base-dutch-cased": {"do_lower_case": False},
}



class      a    (		_a	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE	:			List[Any]							       =       PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE	:			Tuple							       =       PRETRAINED_INIT_CONFIGURATION
     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE	:			Optional[Any]							       =       BertTokenizer

     def __init__(    self					:		Optional[int]							,			snake_case					:		Union[str, Any]=None							,			snake_case					:		str=None							,			snake_case					:		Any=True							,			snake_case					:		Tuple="[UNK]"							,			snake_case					:		int="[SEP]"							,			snake_case					:		Optional[Any]="[PAD]"							,			snake_case					:		int="[CLS]"							,			snake_case					:		Optional[Any]="[MASK]"							,			snake_case					:		Union[str, Any]=True							,			snake_case					:		List[Any]=None							,			**snake_case					:		int							,			)						->							str:
          super().__init__(
              snake_case							,			tokenizer_file=snake_case							,			do_lower_case=snake_case							,			unk_token=snake_case							,			sep_token=snake_case							,			pad_token=snake_case							,			cls_token=snake_case							,			mask_token=snake_case							,			tokenize_chinese_chars=snake_case							,			strip_accents=snake_case							,			**snake_case							,			)

          __UpperCAmelCase     :			List[str]  							=      json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
          if (
              normalizer_state.get('''lowercase'''							,			snake_case      ) != do_lower_case
              or normalizer_state.get('''strip_accents'''							,			snake_case      ) != strip_accents
              or normalizer_state.get('''handle_chinese_chars'''							,			snake_case      ) != tokenize_chinese_chars
          ):
               __UpperCAmelCase     :			List[Any]  							=      getattr(snake_case							,			normalizer_state.pop('''type'''      )      )
               __UpperCAmelCase     :			List[Any]  							=      do_lower_case
               __UpperCAmelCase     :			List[Any]  							=      strip_accents
               __UpperCAmelCase     :			str  							=      tokenize_chinese_chars
               __UpperCAmelCase     :			List[str]  							=      normalizer_class(**snake_case      )

          __UpperCAmelCase     :			Optional[int]  							=      do_lower_case

     def 			lowerCamelCase__			(    self					:		List[Any]							,			snake_case					:		Tuple							,			snake_case					:		List[str]=None      )						->							str:
          __UpperCAmelCase     :			Optional[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 			lowerCamelCase__			(    self					:		str							,			snake_case					:		List[int]							,			snake_case					:		Optional[List[int]] = None      )						->							List[int]:
          __UpperCAmelCase     :			List[str]  							=      [self.sep_token_id]
          __UpperCAmelCase     :			Tuple  							=      [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 			lowerCamelCase__			(    self					:		List[Any]							,			snake_case					:		str							,			snake_case					:		Optional[str] = None      )						->							Tuple[str]:
          __UpperCAmelCase     :			Optional[int]  							=      self._tokenizer.model.save(snake_case							,			name=snake_case      )
          return tuple(snake_case      )