


"""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_big_bird import BigBirdTokenizer
else:
 __lowercase    =				None

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

__lowercase    =				{
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    },
    """tokenizer_file""": {
        """google/bigbird-roberta-base""": (
            """https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"""
        ),
    },
}

__lowercase    =				{
    """google/bigbird-roberta-base""": 4096,
    """google/bigbird-roberta-large""": 4096,
    """google/bigbird-base-trivia-itc""": 4096,
}


__lowercase    =				"""▁"""






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Optional[Any]     =	VOCAB_FILES_NAMES
    UpperCAmelCase	:							Any     =	PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase	:							List[Any]     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase	:							Optional[int]     =	BigBirdTokenizer
    UpperCAmelCase	:							List[str]     =	["""input_ids""", """attention_mask"""]
    UpperCAmelCase	:							List[int]     =	[]






    def __init__( self    :   List[Any]    ,			__UpperCAmelCase    :   Any=None    ,			__UpperCAmelCase    :   int=None    ,			__UpperCAmelCase    :   Optional[Any]="<unk>"    ,			__UpperCAmelCase    :   Optional[int]="<s>"    ,			__UpperCAmelCase    :   Optional[Any]="</s>"    ,			__UpperCAmelCase    :   List[Any]="<pad>"    ,			__UpperCAmelCase    :   List[str]="[SEP]"    ,			__UpperCAmelCase    :   List[str]="[MASK]"    ,			__UpperCAmelCase    :   List[str]="[CLS]"    ,			**__UpperCAmelCase    :   int    ,			):
      a						:		Optional[int]											=      AddedToken(__UpperCAmelCase    ,			lstrip=__UpperCAmelCase    ,			rstrip=__UpperCAmelCase) if isinstance(__UpperCAmelCase    ,			__UpperCAmelCase) else bos_token
      a						:		Tuple											=      AddedToken(__UpperCAmelCase    ,			lstrip=__UpperCAmelCase    ,			rstrip=__UpperCAmelCase) if isinstance(__UpperCAmelCase    ,			__UpperCAmelCase) else eos_token
      a						:		Optional[int]											=      AddedToken(__UpperCAmelCase    ,			lstrip=__UpperCAmelCase    ,			rstrip=__UpperCAmelCase) if isinstance(__UpperCAmelCase    ,			__UpperCAmelCase) else unk_token
      a						:		str											=      AddedToken(__UpperCAmelCase    ,			lstrip=__UpperCAmelCase    ,			rstrip=__UpperCAmelCase) if isinstance(__UpperCAmelCase    ,			__UpperCAmelCase) else pad_token
      a						:		Union[str, Any]											=      AddedToken(__UpperCAmelCase    ,			lstrip=__UpperCAmelCase    ,			rstrip=__UpperCAmelCase) if isinstance(__UpperCAmelCase    ,			__UpperCAmelCase) else cls_token
      a						:		Optional[Any]											=      AddedToken(__UpperCAmelCase    ,			lstrip=__UpperCAmelCase    ,			rstrip=__UpperCAmelCase) if isinstance(__UpperCAmelCase    ,			__UpperCAmelCase) else sep_token

      # Mask token behave like a normal word, i.e. include the space before it
      a						:		Optional[Any]											=      AddedToken(__UpperCAmelCase    ,			lstrip=__UpperCAmelCase    ,			rstrip=__UpperCAmelCase) if isinstance(__UpperCAmelCase    ,			__UpperCAmelCase) else mask_token

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

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






    def      __snake_case							( self    :   str    ,			__UpperCAmelCase    :   List[int]    ,			__UpperCAmelCase    :   Optional[List[int]] = None):
      a						:		Tuple											=      [self.sep_token_id]
      a						:		List[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      __snake_case							( self    :   List[str]    ,			__UpperCAmelCase    :   List[int]    ,			__UpperCAmelCase    :   Optional[List[int]] = None    ,			__UpperCAmelCase    :   bool = False):

      if already_has_special_tokens:
        if token_ids_a is not None:
          raise ValueError(
              "You should not supply a second sequence if the provided sequence of "
              "ids is already formatted with special tokens for the model.")
        return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

      if token_ids_a is None:
        return [1] + ([0] * len(__UpperCAmelCase)) + [1]
      return [1] + ([0] * len(__UpperCAmelCase)) + [1] + ([0] * len(__UpperCAmelCase)) + [1]






    def      __snake_case							( self    :   List[Any]    ,			__UpperCAmelCase    :   List[int]    ,			__UpperCAmelCase    :   Optional[List[int]] = None):
      a						:		str											=      [self.sep_token_id]
      a						:		Optional[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      __snake_case							( self    :   List[Any]    ,			__UpperCAmelCase    :   str    ,			__UpperCAmelCase    :   Optional[str] = None):
      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(__UpperCAmelCase):
        logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
        return
      a						:		Optional[Any]											=      os.path.join(
          __UpperCAmelCase    ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

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

      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 					lowercase      (    A_  ,       A_  ,       A_  ,       A_  ,       A_	)->				Optional[Any]:
  '''simple docstring'''







  a						:		Optional[int]											=      TapasConfig.from_json_file(A_	)
  # set absolute/relative position embeddings parameter
  a						:		str											=      reset_position_index_per_cell

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

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

    a						:		Any											=      TapasForQuestionAnswering(config=A_	)
  elif task == "TABFACT":
    a						:		int											=      TapasForSequenceClassification(config=A_	)
  elif task == "MLM":
    a						:		int											=      TapasForMaskedLM(config=A_	)
  elif task == "INTERMEDIATE_PRETRAINING":
    a						:		List[Any]											=      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}'''	)
  a						:		Optional[int]											=      TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"  ,       model_max_length=512	)
  tokenizer.save_pretrained(A_	)

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


if __name__ == "__main__":
 __lowercase    =				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."""
 )
 __lowercase    =				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,
 )






