





'''simple docstring'''




import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


__SCREAMING_SNAKE_CASE     :List[str]       =				logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE     :int       =				{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

__SCREAMING_SNAKE_CASE     :List[Any]       =				{
    '''vocab_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
    },
}

__SCREAMING_SNAKE_CASE     :str       =				{
    '''allenai/longformer-base-4096''': 4096,
    '''allenai/longformer-large-4096''': 4096,
    '''allenai/longformer-large-4096-finetuned-triviaqa''': 4096,
    '''allenai/longformer-base-4096-extra.pos.embd.only''': 4096,
    '''allenai/longformer-large-4096-extra.pos.embd.only''': 4096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 	UpperCAmelCase_							(					)		->       Optional[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   (
         list(range(ord("!"       )			,     ord("~"       ) + 1       )       ) + list(range(ord("¡"       )			,     ord("¬"       ) + 1       )       ) + list(range(ord("®"       )			,     ord("ÿ"       ) + 1       )       )
     )
     _UpperCAmelCase     							=   bs[:]
     _UpperCAmelCase     							=   0
     for b in range(2**8       ):
          if b not in bs:
               bs.append(__lowercase       )
               cs.append(2**8 + n       )
               n += 1
     _UpperCAmelCase     							=   [chr(__lowercase       ) for n in cs]
     return dict(zip(__lowercase			,     __lowercase       )       )
def 	UpperCAmelCase_							(					__lowercase							:      List[Any]       )		->       Any:


     '''simple docstring'''



     _UpperCAmelCase     							=   set()
     _UpperCAmelCase     							=   word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)       )
          _UpperCAmelCase     							=   char
     return pairs







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : List[Any]           =							VOCAB_FILES_NAMES
 _lowerCamelCase  : int           =							PRETRAINED_VOCAB_FILES_MAP
 _lowerCamelCase  : Dict           =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCamelCase  : List[Any]           =							["""input_ids""", """attention_mask"""]




 def __init__(					self       :    str	,			snake_case_       :    Tuple	,			snake_case_       :    List[Any]	,			snake_case_       :    Any="replace"	,			snake_case_       :    Any="<s>"	,			snake_case_       :    Any="</s>"	,			snake_case_       :    Optional[Any]="</s>"	,			snake_case_       :    Any="<s>"	,			snake_case_       :    Any="<unk>"	,			snake_case_       :    Dict="<pad>"	,			snake_case_       :    Any="<mask>"	,			snake_case_       :    List[Any]=False	,			**snake_case_       :    Optional[int]	,			):
      _UpperCAmelCase     							=   AddedToken(snake_case_	,			lstrip=snake_case_	,			rstrip=snake_case_    ) if isinstance(snake_case_	,			snake_case_    ) else bos_token
      _UpperCAmelCase     							=   AddedToken(snake_case_	,			lstrip=snake_case_	,			rstrip=snake_case_    ) if isinstance(snake_case_	,			snake_case_    ) else eos_token
      _UpperCAmelCase     							=   AddedToken(snake_case_	,			lstrip=snake_case_	,			rstrip=snake_case_    ) if isinstance(snake_case_	,			snake_case_    ) else sep_token
      _UpperCAmelCase     							=   AddedToken(snake_case_	,			lstrip=snake_case_	,			rstrip=snake_case_    ) if isinstance(snake_case_	,			snake_case_    ) else cls_token
      _UpperCAmelCase     							=   AddedToken(snake_case_	,			lstrip=snake_case_	,			rstrip=snake_case_    ) if isinstance(snake_case_	,			snake_case_    ) else unk_token
      _UpperCAmelCase     							=   AddedToken(snake_case_	,			lstrip=snake_case_	,			rstrip=snake_case_    ) if isinstance(snake_case_	,			snake_case_    ) else pad_token

      # Mask token behave like a normal word, i.e. include the space before it
      _UpperCAmelCase     							=   AddedToken(snake_case_	,			lstrip=snake_case_	,			rstrip=snake_case_    ) if isinstance(snake_case_	,			snake_case_    ) else mask_token

      super().__init__(
          errors=snake_case_	,			bos_token=snake_case_	,			eos_token=snake_case_	,			unk_token=snake_case_	,			sep_token=snake_case_	,			cls_token=snake_case_	,			pad_token=snake_case_	,			mask_token=snake_case_	,			add_prefix_space=snake_case_	,			**snake_case_	,			)

      with open(snake_case_	,			encoding="utf-8"    ) as vocab_handle:
           _UpperCAmelCase     							=   json.load(snake_case_    )
      _UpperCAmelCase     							=   {v: k for k, v in self.encoder.items()}
      _UpperCAmelCase     							=   errors  # how to handle errors in decoding
      _UpperCAmelCase     							=   bytes_to_unicode()
      _UpperCAmelCase     							=   {v: k for k, v in self.byte_encoder.items()}
      with open(snake_case_	,			encoding="utf-8"    ) as merges_handle:
           _UpperCAmelCase     							=   merges_handle.read().split("\n"    )[1:-1]
      _UpperCAmelCase     							=   [tuple(merge.split()    ) for merge in bpe_merges]
      _UpperCAmelCase     							=   dict(zip(snake_case_	,			range(len(snake_case_    )    )    )    )
      _UpperCAmelCase     							=   {}
      _UpperCAmelCase     							=   add_prefix_space

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      _UpperCAmelCase     							=   re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"    )




 @property
 def      lowercase	(					self       :    Optional[int]    ):
      return len(self.encoder    )




 def      lowercase	(					self       :    Dict    ):
      return dict(self.encoder	,			**self.added_tokens_encoder    )




 def      lowercase	(					self       :    Optional[int]	,			snake_case_       :    Dict    ):
      if token in self.cache:
           return self.cache[token]
      _UpperCAmelCase     							=   tuple(snake_case_    )
      _UpperCAmelCase     							=   get_pairs(snake_case_    )

      if not pairs:
           return token

      while True:
           _UpperCAmelCase     							=   min(snake_case_	,			key=lambda snake_case_    : self.bpe_ranks.get(snake_case_	,			float("inf"    )    )    )
           if bigram not in self.bpe_ranks:
                break
           _UpperCAmelCase							,  _UpperCAmelCase     							=   bigram
           _UpperCAmelCase     							=   []
           _UpperCAmelCase     							=   0
           while i < len(snake_case_    ):
                try:
                     _UpperCAmelCase     							=   word.index(snake_case_	,			snake_case_    )
                except ValueError:
                     new_word.extend(word[i:]    )
                     break
                else:
                     new_word.extend(word[i:j]    )
                     _UpperCAmelCase     							=   j

                if word[i] == first and i < len(snake_case_    ) - 1 and word[i + 1] == second:
                     new_word.append(first + second    )
                     i += 2
                else:
                     new_word.append(word[i]    )
                     i += 1
           _UpperCAmelCase     							=   tuple(snake_case_    )
           _UpperCAmelCase     							=   new_word
           if len(snake_case_    ) == 1:
                break
           else:
                _UpperCAmelCase     							=   get_pairs(snake_case_    )
      _UpperCAmelCase     							=   " ".join(snake_case_    )
      _UpperCAmelCase     							=   word
      return word




 def      lowercase	(					self       :    List[str]	,			snake_case_       :    Dict    ):
      _UpperCAmelCase     							=   []
      for token in re.findall(self.pat	,			snake_case_    ):
           _UpperCAmelCase     							=   "".join(
               self.byte_encoder[b] for b in token.encode("utf-8"    )    )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
           bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case_    ).split(" "    )    )
      return bpe_tokens




 def      lowercase	(					self       :    List[str]	,			snake_case_       :    List[str]    ):
      return self.encoder.get(snake_case_	,			self.encoder.get(self.unk_token    )    )




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    Union[str, Any]    ):
      return self.decoder.get(snake_case_    )




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    List[str]    ):
      _UpperCAmelCase     							=   "".join(snake_case_    )
      _UpperCAmelCase     							=   bytearray([self.byte_decoder[c] for c in text]    ).decode("utf-8"	,			errors=self.errors    )
      return text




 def      lowercase	(					self       :    Tuple	,			snake_case_       :    str	,			snake_case_       :    Optional[str] = None    ):
      if not os.path.isdir(snake_case_    ):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'    )
           return
      _UpperCAmelCase     							=   os.path.join(
          snake_case_	,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )
      _UpperCAmelCase     							=   os.path.join(
          snake_case_	,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]    )

      with open(snake_case_	,			"w"	,			encoding="utf-8"    ) as f:
           f.write(json.dumps(self.encoder	,			indent=2	,			sort_keys=snake_case_	,			ensure_ascii=snake_case_    ) + "\n"    )

      _UpperCAmelCase     							=   0
      with open(snake_case_	,			"w"	,			encoding="utf-8"    ) as writer:
           writer.write("#version: 0.2\n"    )
           for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	,			key=lambda snake_case_    : kv[1]    ):
                if index != token_index:
                     logger.warning(
                         f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                         " Please check that the tokenizer is not corrupted!"    )
                     _UpperCAmelCase     							=   token_index
                writer.write(" ".join(snake_case_    ) + "\n"    )
                index += 1

      return vocab_file, merge_file




 def      lowercase	(					self       :    str	,			snake_case_       :    List[int]	,			snake_case_       :    Optional[List[int]] = None    ):
      if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      _UpperCAmelCase     							=   [self.cls_token_id]
      _UpperCAmelCase     							=   [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep




 def      lowercase	(					self       :    Dict	,			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, 1] + ([0] * len(snake_case_    )) + [1]




 def      lowercase	(					self       :    Dict	,			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 + sep + token_ids_a + sep    ) * [0]




 def      lowercase	(					self       :    Any	,			snake_case_       :    str	,			snake_case_       :    int=False	,			**snake_case_       :    Any    ):
      _UpperCAmelCase     							=   kwargs.pop("add_prefix_space"	,			self.add_prefix_space    )
      if (is_split_into_words or add_prefix_space) and (len(snake_case_    ) > 0 and not text[0].isspace()):
           _UpperCAmelCase     							=   " " + text
      return (text, kwargs)











'''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,
  )




