





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_  							=				logging.get_logger(__name__)

UpperCAmelCase_  							=				'▁'

UpperCAmelCase_  							=				{'vocab_file': 'sentencepiece.bpe.model'}

UpperCAmelCase_  							=				{
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

UpperCAmelCase_  							=				{
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}






class 					lowercase__ (			a__       ):

   '''simple docstring'''


   a		:							Dict     	=	VOCAB_FILES_NAMES
   a		:							List[Any]     	=	PRETRAINED_VOCAB_FILES_MAP
   a		:							Dict     	=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a		:							List[str]     	=	["input_ids", "attention_mask"]




   def __init__(					self,	__magic_name__,	__magic_name__="<s>",	__magic_name__="</s>",	__magic_name__="</s>",	__magic_name__="<s>",	__magic_name__="<unk>",	__magic_name__="<pad>",	__magic_name__="<mask>",	__magic_name__ = None,	**__magic_name__,	)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       str      =   AddedToken(_A,	lstrip=_A,	rstrip=_A							) if isinstance(_A,	_A							) else mask_token

       UpperCamelCase__							:       int      =   {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=_A,	eos_token=_A,	unk_token=_A,	sep_token=_A,	cls_token=_A,	pad_token=_A,	mask_token=_A,	sp_model_kwargs=self.sp_model_kwargs,	**_A,	)

       UpperCamelCase__							:       Tuple      =   spm.SentencePieceProcessor(**self.sp_model_kwargs							)
       self.sp_model.Load(str(_A							)							)
       UpperCamelCase__							:       Optional[Any]      =   vocab_file

       # Original fairseq vocab and spm vocab must be "aligned":
       # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
       # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
       # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
       # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

       # Mimic fairseq token-to-id alignment for the first 4 token
       UpperCamelCase__							:       int      =   {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

       # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
       UpperCamelCase__							:       List[str]      =   1

       UpperCamelCase__							:       Union[str, Any]      =   len(self.sp_model							) + self.fairseq_offset
       UpperCamelCase__							:       Optional[int]      =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}




   def __getstate__(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       Any      =   self.__dict__.copy()
       UpperCamelCase__							:       List[Any]      =   None
       UpperCamelCase__							:       str      =   self.sp_model.serialized_model_proto()
       return state




   def __setstate__(					self,	__magic_name__							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   d

       # for backward compatibility
       if not hasattr(self,	'''sp_model_kwargs'''							):
           UpperCamelCase__							:       int      =   {}

       UpperCamelCase__							:       Optional[Any]      =   spm.SentencePieceProcessor(**self.sp_model_kwargs							)
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None							)				->   Union[str, Any]:






       """simple docstring"""

       if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       UpperCamelCase__							:       Tuple      =   [self.cls_token_id]
       UpperCamelCase__							:       Tuple      =   [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None,	__magic_name__ = False							)				->   Optional[int]:






       """simple docstring"""

       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=_A,	token_ids_a=_A,	already_has_special_tokens=_A							)

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




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:       int      =   [self.sep_token_id]
       UpperCamelCase__							:       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 + sep + token_ids_a + sep							) * [0]




   @property
   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       return len(self.sp_model							) + self.fairseq_offset + 1  # Add the <mask> token




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       int      =   {self.convert_ids_to_tokens(_A							): i for i in range(self.vocab_size							)}
       vocab.update(self.added_tokens_encoder							)
       return vocab




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   int:






       """simple docstring"""
       return self.sp_model.encode(_A,	out_type=_A							)




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   str:






       """simple docstring"""

       if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
       UpperCamelCase__							:       Any      =   self.sp_model.PieceToId(_A							)

       # Need to return unknown token if the SP model returned 0
       return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   Dict:






       """simple docstring"""

       if index in self.fairseq_ids_to_tokens:
           return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset							)




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       int      =   ''.join(_A							).replace(_A,	''' '''							).strip()
       return out_string




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None							)				->   Optional[int]:






       """simple docstring"""

       if not os.path.isdir(_A							):
           logger.error(f"Vocabulary path ({save_directory}) should be a directory"							)
           return
       UpperCamelCase__							:       Optional[Any]      =   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							) and os.path.isfile(self.vocab_file							):
           copyfile(self.vocab_file,	_A							)
       elif not os.path.isfile(self.vocab_file							):
           with open(_A,	'''wb'''							) as fi:
               UpperCamelCase__							:       Tuple      =   self.sp_model.serialized_model_proto()
               fi.write(_A							)

       return (out_vocab_file,)








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 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[Any]	,     __UpperCAmelCase:    List[str]	,     __UpperCAmelCase:    Dict	,     __UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    str   )   ->			Optional[int]:
    # 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
    UpperCamelCase__							:       Any      =   TapasConfig.from_json_file(__UpperCAmelCase   )
    # set absolute/relative position embeddings parameter
    UpperCamelCase__							:       Optional[Any]      =   reset_position_index_per_cell

    # set remaining parameters of TapasConfig as well as the model based on the task
    if task == "SQA":
        UpperCamelCase__							:       List[Any]      =   TapasForQuestionAnswering(config=__UpperCAmelCase   )
    elif task == "WTQ":
        # run_task_main.py hparams
        UpperCamelCase__							:       Any      =   4
        UpperCamelCase__							:       List[str]      =   True
        # hparam_utils.py hparams
        UpperCamelCase__							:       int      =   0.664694
        UpperCamelCase__							:       Union[str, Any]      =   0.207951
        UpperCamelCase__							:       Any      =   0.121194
        UpperCamelCase__							:       int      =   True
        UpperCamelCase__							:       Any      =   True
        UpperCamelCase__							:       str      =   False
        UpperCamelCase__							:       int      =   0.0352513

        UpperCamelCase__							:       Dict      =   TapasForQuestionAnswering(config=__UpperCAmelCase   )
    elif task == "WIKISQL_SUPERVISED":
        # run_task_main.py hparams
        UpperCamelCase__							:       Any      =   4
        UpperCamelCase__							:       List[str]      =   False
        # hparam_utils.py hparams
        UpperCamelCase__							:       int      =   36.4519
        UpperCamelCase__							:       int      =   0.903421
        UpperCamelCase__							:       List[str]      =   222.088
        UpperCamelCase__							:       Dict      =   True
        UpperCamelCase__							:       List[str]      =   True
        UpperCamelCase__							:       Any      =   True
        UpperCamelCase__							:       int      =   0.763141

        UpperCamelCase__							:       Union[str, Any]      =   TapasForQuestionAnswering(config=__UpperCAmelCase   )
    elif task == "TABFACT":
        UpperCamelCase__							:       List[Any]      =   TapasForSequenceClassification(config=__UpperCAmelCase   )
    elif task == "MLM":
        UpperCamelCase__							:       Optional[Any]      =   TapasForMaskedLM(config=__UpperCAmelCase   )
    elif task == "INTERMEDIATE_PRETRAINING":
        UpperCamelCase__							:       str      =   TapasModel(config=__UpperCAmelCase   )
    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(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )

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

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

    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,
  )

