






"""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			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	TapasConfig.from_json_file(_snake_case							)
      # 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=_snake_case							)
      elif task == "WTQ":
            # run_task_main.py hparams
            UpperCAmelCase          =	4
            UpperCAmelCase          =	True
            # hparam_utils.py hparams
            UpperCAmelCase          =	0.664694
            UpperCAmelCase          =	0.207951
            UpperCAmelCase          =	0.121194
            UpperCAmelCase          =	True
            UpperCAmelCase          =	True
            UpperCAmelCase          =	False
            UpperCAmelCase          =	0.0352513

            UpperCAmelCase          =	TapasForQuestionAnswering(config=_snake_case							)
      elif task == "WIKISQL_SUPERVISED":
            # run_task_main.py hparams
            UpperCAmelCase          =	4
            UpperCAmelCase          =	False
            # hparam_utils.py hparams
            UpperCAmelCase          =	36.4519
            UpperCAmelCase          =	0.903421
            UpperCAmelCase          =	222.088
            UpperCAmelCase          =	True
            UpperCAmelCase          =	True
            UpperCAmelCase          =	True
            UpperCAmelCase          =	0.763141

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

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

      # 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(_snake_case							)

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


if __name__ == "__main__":
  _UpperCamelCase					      =  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					      =  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 os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_pegasus import PegasusTokenizer
else:
  _UpperCamelCase					      =  None


_UpperCamelCase					      =  logging.get_logger(__name__)


_UpperCamelCase					      =  """▁"""

_UpperCamelCase					      =  {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCamelCase					      =  {
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
    """tokenizer_file""": {
        """google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
    },
}

_UpperCamelCase					      =  {
    """google/pegasus-xsum""": 512,
}







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE		=      PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE		=      PegasusTokenizer
    SCREAMING_SNAKE_CASE		=      ['''input_ids''', '''attention_mask''']






    def __init__(			self     ,A=None     ,A=None     ,A="<pad>"     ,A="</s>"     ,A="<unk>"     ,A="<mask_2>"     ,A="<mask_1>"     ,A=None     ,A=103     ,**A     ,):
          UpperCAmelCase          =	offset

          if additional_special_tokens is not None:
                if not isinstance(A     ,A ):
                      raise TypeError(
                          F'''additional_special_tokens should be of type {type(A )}, but is'''
                          F''' {type(A )}''' )

                UpperCAmelCase          =	(
                    ([mask_token_sent] + additional_special_tokens)
                    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                    else additional_special_tokens
                )
                # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                additional_special_tokens_extended += [
                    F'''<unk_{i}>''' for i in range(len(A )     ,self.offset - 1 )
                ]

                if len(set(A ) ) != len(A ):
                      raise ValueError(
                          """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
                          F''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''' )
                UpperCAmelCase          =	additional_special_tokens_extended
          else:
                UpperCAmelCase          =	[mask_token_sent] if mask_token_sent is not None else []
                additional_special_tokens += [F'''<unk_{i}>''' for i in range(2     ,self.offset )]

          super().__init__(
              A     ,tokenizer_file=A     ,pad_token=A     ,eos_token=A     ,unk_token=A     ,mask_token=A     ,mask_token_sent=A     ,offset=A     ,additional_special_tokens=A     ,**A     ,)
          UpperCAmelCase          =	vocab_file
          UpperCAmelCase          =	False if not self.vocab_file else True






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	set(self.all_special_ids )  # call it once instead of inside list comp
          all_special_ids.remove(self.unk_token_id )  # <unk> is only sometimes special

          if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
                raise ValueError(
                    """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
                    F''' {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}''' )

          return [1 if x in all_special_ids else 0 for x in seq]






    def     _UpperCamelCase							(			self     ,A     ,A = None     ,A = False ):
          if already_has_special_tokens:
                return self._special_token_mask(A )
          elif token_ids_a is None:
                return self._special_token_mask(A ) + [1]
          else:
                return self._special_token_mask(token_ids_a + token_ids_a ) + [1]






    def     _UpperCamelCase							(			self     ,A     ,A=None ):
          if token_ids_a is None:
                return token_ids_a + [self.eos_token_id]
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return token_ids_a + token_ids_a + [self.eos_token_id]






    def     _UpperCamelCase							(			self     ,A     ,A = 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(A ):
                logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
                return
          UpperCAmelCase          =	os.path.join(
              A     ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

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

          return (out_vocab_file,)



