


"""simple docstring"""
import os
import unicodedata
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 SPIECE_UNDERLINE, logging


UpperCAmelCase				=						logging.get_logger(__name__)

UpperCAmelCase				=						{"""vocab_file""": """spiece.model"""}

UpperCAmelCase				=						{
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    }
}

UpperCAmelCase				=						{
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

# Segments (not really needed)
UpperCAmelCase				=						0
UpperCAmelCase				=						1
UpperCAmelCase				=						2
UpperCAmelCase				=						3
UpperCAmelCase				=						4




class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					VOCAB_FILES_NAMES
   __UpperCAmelCase							       =					PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase							       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase							       =					'''left'''





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=False      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=False      ,   SCREAMING_SNAKE_CASE="<s>"      ,   SCREAMING_SNAKE_CASE="</s>"      ,   SCREAMING_SNAKE_CASE="<unk>"      ,   SCREAMING_SNAKE_CASE="<sep>"      ,   SCREAMING_SNAKE_CASE="<pad>"      ,   SCREAMING_SNAKE_CASE="<cls>"      ,   SCREAMING_SNAKE_CASE="<mask>"      ,   SCREAMING_SNAKE_CASE=["<eop>", "<eod>"]      ,   SCREAMING_SNAKE_CASE = None      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  None:
      # Mask token behave like a normal word, i.e. include the space before it
      _lowerCamelCase	:					Any   =		AddedToken(SCREAMING_SNAKE_CASE      ,   lstrip=SCREAMING_SNAKE_CASE      ,   rstrip=SCREAMING_SNAKE_CASE) if isinstance(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE) else mask_token

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

      super().__init__(
          do_lower_case=SCREAMING_SNAKE_CASE      ,   remove_space=SCREAMING_SNAKE_CASE      ,   keep_accents=SCREAMING_SNAKE_CASE      ,   bos_token=SCREAMING_SNAKE_CASE      ,   eos_token=SCREAMING_SNAKE_CASE      ,   unk_token=SCREAMING_SNAKE_CASE      ,   sep_token=SCREAMING_SNAKE_CASE      ,   pad_token=SCREAMING_SNAKE_CASE      ,   cls_token=SCREAMING_SNAKE_CASE      ,   mask_token=SCREAMING_SNAKE_CASE      ,   additional_special_tokens=SCREAMING_SNAKE_CASE      ,   sp_model_kwargs=self.sp_model_kwargs      ,   **SCREAMING_SNAKE_CASE      ,   )

      _lowerCamelCase	:					Union[str, Any]   =		3

      _lowerCamelCase	:					Dict   =		do_lower_case
      _lowerCamelCase	:					str   =		remove_space
      _lowerCamelCase	:					Dict   =		keep_accents
      _lowerCamelCase	:					Optional[Any]   =		vocab_file

      _lowerCamelCase	:					str   =		spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(SCREAMING_SNAKE_CASE)





   @property
   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      return len(self.sp_model)





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					Union[str, Any]   =		{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE): i for i in range(self.vocab_size)}
      vocab.update(self.added_tokens_encoder)
      return vocab





   def __getstate__(							self)  ->  Optional[int]:
      _lowerCamelCase	:					Dict   =		self.__dict__.copy()
      _lowerCamelCase	:					Optional[Any]   =		None
      return state





   def __setstate__(							self      ,   SCREAMING_SNAKE_CASE)  ->  Optional[Any]:
      _lowerCamelCase	:					str   =		d

      # for backward compatibility
      if not hasattr(self      ,   """sp_model_kwargs"""):
         _lowerCamelCase	:					Union[str, Any]   =		{}

      _lowerCamelCase	:					Optional[Any]   =		spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(self.vocab_file)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      if self.remove_space:
         _lowerCamelCase	:					List[Any]   =		""" """.join(inputs.strip().split())
      else:
         _lowerCamelCase	:					Dict   =		inputs
      _lowerCamelCase	:					Optional[int]   =		outputs.replace("""``"""      ,   """\"""").replace("""''"""      ,   """\"""")

      if not self.keep_accents:
         _lowerCamelCase	:					Optional[int]   =		unicodedata.normalize("""NFKD"""      ,   SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					List[Any]   =		"""""".join([c for c in outputs if not unicodedata.combining(SCREAMING_SNAKE_CASE)])
      if self.do_lower_case:
         _lowerCamelCase	:					Tuple   =		outputs.lower()

      return outputs





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      _lowerCamelCase	:					str   =		self.preprocess_text(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[str]   =		self.sp_model.encode(SCREAMING_SNAKE_CASE      ,   out_type=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Dict   =		[]
      for piece in pieces:
         if len(SCREAMING_SNAKE_CASE) > 1 and piece[-1] == str(""",""") and piece[-2].isdigit():
            _lowerCamelCase	:					Dict   =		self.sp_model.EncodeAsPieces(piece[:-1].replace(SCREAMING_SNAKE_CASE      ,   """"""))
            if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
               if len(cur_pieces[0]) == 1:
                  _lowerCamelCase	:					List[str]   =		cur_pieces[1:]
               else:
                  _lowerCamelCase	:					Tuple   =		cur_pieces[0][1:]
            cur_pieces.append(piece[-1])
            new_pieces.extend(SCREAMING_SNAKE_CASE)
         else:
            new_pieces.append(SCREAMING_SNAKE_CASE)

      return new_pieces





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  int:
      return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[Any]:
      _lowerCamelCase	:					Optional[int]   =		"""""".join(SCREAMING_SNAKE_CASE).replace(SCREAMING_SNAKE_CASE      ,   """ """).strip()
      return out_string





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = False      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = True      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  str:
      _lowerCamelCase	:					Optional[int]   =		kwargs.pop("""use_source_tokenizer"""      ,   SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Tuple   =		self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE      ,   skip_special_tokens=SCREAMING_SNAKE_CASE)

      # To avoid mixing byte-level and unicode for byte-level BPT
      # we need to build string separately for added tokens and byte-level tokens
      # cf. https://github.com/huggingface/transformers/issues/1133
      _lowerCamelCase	:					Optional[int]   =		[]
      _lowerCamelCase	:					str   =		[]
      for token in filtered_tokens:
         if skip_special_tokens and token in self.all_special_ids:
            continue
         if token in self.added_tokens_encoder:
            if current_sub_text:
               sub_texts.append(self.convert_tokens_to_string(SCREAMING_SNAKE_CASE))
               _lowerCamelCase	:					int   =		[]
            sub_texts.append(SCREAMING_SNAKE_CASE)
         else:
            current_sub_text.append(SCREAMING_SNAKE_CASE)
      if current_sub_text:
         sub_texts.append(self.convert_tokens_to_string(SCREAMING_SNAKE_CASE))

      # Mimic the behavior of the Rust tokenizer:
      # By default, there are no spaces between special tokens
      _lowerCamelCase	:					Optional[int]   =		"""""".join(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					List[str]   =		(
          clean_up_tokenization_spaces
          if clean_up_tokenization_spaces is not None
          else self.clean_up_tokenization_spaces
      )
      if clean_up_tokenization_spaces:
         _lowerCamelCase	:					Tuple   =		self.clean_up_tokenization(SCREAMING_SNAKE_CASE)
         return clean_text
      else:
         return text





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None)  ->  List[int]:
      _lowerCamelCase	:					int   =		[self.sep_token_id]
      _lowerCamelCase	:					Dict   =		[self.cls_token_id]
      if token_ids_a is None:
         return token_ids_a + sep + cls
      return token_ids_a + sep + token_ids_a + sep + cls





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = False)  ->  List[int]:

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

      if token_ids_a is not None:
         return ([0] * len(SCREAMING_SNAKE_CASE)) + [1] + ([0] * len(SCREAMING_SNAKE_CASE)) + [1, 1]
      return ([0] * len(SCREAMING_SNAKE_CASE)) + [1, 1]





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None)  ->  List[int]:
      _lowerCamelCase	:					Union[str, Any]   =		[self.sep_token_id]
      _lowerCamelCase	:					List[str]   =		[2]

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





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None)  ->  Tuple[str]:
      if not os.path.isdir(SCREAMING_SNAKE_CASE):
         logger.error(F'Vocabulary path ({save_directory}) should be a directory')
         return
      _lowerCamelCase	:					Optional[Any]   =		os.path.join(
          SCREAMING_SNAKE_CASE      ,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

      if os.path.abspath(self.vocab_file) != os.path.abspath(SCREAMING_SNAKE_CASE) and os.path.isfile(self.vocab_file):
         copyfile(self.vocab_file      ,   SCREAMING_SNAKE_CASE)
      elif not os.path.isfile(self.vocab_file):
         with open(SCREAMING_SNAKE_CASE      ,   """wb""") as fi:
            _lowerCamelCase	:					Optional[int]   =		self.sp_model.serialized_model_proto()
            fi.write(SCREAMING_SNAKE_CASE)

      return (out_vocab_file,)




import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str		,				_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	Tuple						)		->				Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      BigBirdConfig.from_json_file(_UpperCamelCase						)
 print(f"""Building PyTorch model from configuration: {config}"""						)

 if is_trivia_qa:
  SCREAMING_SNAKE_CASE		    =      BigBirdForQuestionAnswering(_UpperCamelCase						)
 else:
  SCREAMING_SNAKE_CASE		    =      BigBirdForPreTraining(_UpperCamelCase						)

 # Load weights from tf checkpoint
 load_tf_weights_in_big_bird(_UpperCamelCase		,				_UpperCamelCase		,				is_trivia_qa=_UpperCamelCase						)

 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""						)
 model.save_pretrained(_UpperCamelCase						)


if __name__ == "__main__":
      a_     :    int     		=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--big_bird_config_file",
          default=None,
          type=str,
          required=True,
          help=(
              "The config json file corresponding to the pre-trained BERT 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."
      )
      parser.add_argument(
          "--is_trivia_qa", action="store_true", help="Whether to convert a model with a trivia_qa head."
      )
      a_     :    Any     		=   parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
      )

