





'''simple docstring'''




from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


__SCREAMING_SNAKE_CASE     :Any       =				logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :str       =				{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__SCREAMING_SNAKE_CASE     :List[str]       =				{
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

__SCREAMING_SNAKE_CASE     :Optional[Any]       =				{'''allegro/herbert-base-cased''': 514}
__SCREAMING_SNAKE_CASE     :Optional[int]       =				{}







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Tuple           =							VOCAB_FILES_NAMES
 _lowerCamelCase  : List[str]           =							PRETRAINED_VOCAB_FILES_MAP
 _lowerCamelCase  : int           =							PRETRAINED_INIT_CONFIGURATION
 _lowerCamelCase  : Optional[Any]           =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCamelCase  : Tuple           =							HerbertTokenizer




 def __init__(					self       :    Dict	,			snake_case_       :    Union[str, Any]=None	,			snake_case_       :    Any=None	,			snake_case_       :    Dict=None	,			snake_case_       :    List[Any]="<s>"	,			snake_case_       :    Tuple="<unk>"	,			snake_case_       :    Dict="<pad>"	,			snake_case_       :    List[str]="<mask>"	,			snake_case_       :    int="</s>"	,			**snake_case_       :    str	,			):
      super().__init__(
          snake_case_	,			snake_case_	,			tokenizer_file=snake_case_	,			cls_token=snake_case_	,			unk_token=snake_case_	,			pad_token=snake_case_	,			mask_token=snake_case_	,			sep_token=snake_case_	,			**snake_case_	,			)




 def      lowercase	(					self       :    str	,			snake_case_       :    List[int]	,			snake_case_       :    Optional[List[int]] = None    ):
      _UpperCAmelCase     							=   [self.cls_token_id]
      _UpperCAmelCase     							=   [self.sep_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      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    List[int]	,			snake_case_       :    Optional[List[int]] = None	,			snake_case_       :    bool = False    ):
      if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=snake_case_	,			token_ids_a=snake_case_	,			already_has_special_tokens=snake_case_    )

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




 def      lowercase	(					self       :    Any	,			snake_case_       :    List[int]	,			snake_case_       :    Optional[List[int]] = None    ):
      _UpperCAmelCase     							=   [self.sep_token_id]
      _UpperCAmelCase     							=   [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      lowercase	(					self       :    List[Any]	,			snake_case_       :    str	,			snake_case_       :    Optional[str] = None    ):
      _UpperCAmelCase     							=   self._tokenizer.model.save(snake_case_	,			name=snake_case_    )
      return tuple(snake_case_    )











'''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 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      Dict			,     __lowercase							:      str			,     __lowercase							:      Optional[Any]			,     __lowercase							:      str       )		->       List[str]:


     '''simple docstring'''



     _UpperCAmelCase     							=   TapasConfig.from_json_file(__lowercase       )
     # set absolute/relative position embeddings parameter
     _UpperCAmelCase     							=   reset_position_index_per_cell

     # set remaining parameters of TapasConfig as well as the model based on the task
     if task == "SQA":
          _UpperCAmelCase     							=   TapasForQuestionAnswering(config=__lowercase       )
     elif task == "WTQ":
          # run_task_main.py hparams
          _UpperCAmelCase     							=   4
          _UpperCAmelCase     							=   True
          # hparam_utils.py hparams
          _UpperCAmelCase     							=   0.66_4694
          _UpperCAmelCase     							=   0.20_7951
          _UpperCAmelCase     							=   0.12_1194
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   False
          _UpperCAmelCase     							=   0.035_2513

          _UpperCAmelCase     							=   TapasForQuestionAnswering(config=__lowercase       )
     elif task == "WIKISQL_SUPERVISED":
          # run_task_main.py hparams
          _UpperCAmelCase     							=   4
          _UpperCAmelCase     							=   False
          # hparam_utils.py hparams
          _UpperCAmelCase     							=   36.4519
          _UpperCAmelCase     							=   0.90_3421
          _UpperCAmelCase     							=   222.088
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   0.76_3141

          _UpperCAmelCase     							=   TapasForQuestionAnswering(config=__lowercase       )
     elif task == "TABFACT":
          _UpperCAmelCase     							=   TapasForSequenceClassification(config=__lowercase       )
     elif task == "MLM":
          _UpperCAmelCase     							=   TapasForMaskedLM(config=__lowercase       )
     elif task == "INTERMEDIATE_PRETRAINING":
          _UpperCAmelCase     							=   TapasModel(config=__lowercase       )
     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(__lowercase			,     __lowercase			,     __lowercase       )

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

     # Save tokenizer files
     print(f'Save tokenizer files to {pytorch_dump_path}'       )
     _UpperCAmelCase     							=   TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"			,     model_max_length=512       )
     tokenizer.save_pretrained(__lowercase       )

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


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE     :List[str]       =				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     :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,
  )




