





'''simple docstring'''




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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


lowercase__       : Tuple         =       logging.get_logger(__name__)


lowercase__       : Optional[Any]         =       {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all LED models at https://huggingface.co/models?filter=LED
lowercase__       : Optional[int]         =       {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

lowercase__       : Dict         =       {
    """allenai/led-base-16384""": 1_63_84,
}






@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 			_lowerCAmelCase     (    )	->	List[Any]:
     __A		:					List[Any]						=			(
         list(range(ord('!'     )					,				ord('~'     ) + 1     )     ) + list(range(ord('¡'     )					,				ord('¬'     ) + 1     )     ) + list(range(ord('®'     )					,				ord('ÿ'     ) + 1     )     )
     )
     __A		:					Optional[int]						=			bs[:]
     __A		:					Optional[int]						=			0
     for b in range(2**8     ):
          if b not in bs:
               bs.append(SCREAMING_SNAKE_CASE__     )
               cs.append(2**8 + n     )
               n += 1
     __A		:					Optional[int]						=			[chr(SCREAMING_SNAKE_CASE__     ) for n in cs]
     return dict(zip(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__     )     )






def 			_lowerCAmelCase     (    __snake_case    :					Optional[Any]     )	->	Optional[int]:
     __A		:					str						=			set()
     __A		:					Optional[int]						=			word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)     )
          __A		:					Tuple						=			char
     return pairs







class 					SCREAMING_SNAKE_CASE       (_lowerCAmelCase				):
     lowerCAmelCase						     =			VOCAB_FILES_NAMES
     lowerCAmelCase						     =			PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase						     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase						     =			['''input_ids''', '''attention_mask''']


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase="replace"		,      _UpperCAmelCase="<s>"		,      _UpperCAmelCase="</s>"		,      _UpperCAmelCase="</s>"		,      _UpperCAmelCase="<s>"		,      _UpperCAmelCase="<unk>"		,      _UpperCAmelCase="<pad>"		,      _UpperCAmelCase="<mask>"		,      _UpperCAmelCase=False		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          __A		:					Tuple						=			AddedToken(_UpperCAmelCase		,      lstrip=_UpperCAmelCase		,      rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else bos_token
          __A		:					Tuple						=			AddedToken(_UpperCAmelCase		,      lstrip=_UpperCAmelCase		,      rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else eos_token
          __A		:					List[str]						=			AddedToken(_UpperCAmelCase		,      lstrip=_UpperCAmelCase		,      rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else sep_token
          __A		:					Union[str, Any]						=			AddedToken(_UpperCAmelCase		,      lstrip=_UpperCAmelCase		,      rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else cls_token
          __A		:					Union[str, Any]						=			AddedToken(_UpperCAmelCase		,      lstrip=_UpperCAmelCase		,      rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else unk_token
          __A		:					Dict						=			AddedToken(_UpperCAmelCase		,      lstrip=_UpperCAmelCase		,      rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else pad_token

          # Mask token behave like a normal word, i.e. include the space before it
          __A		:					Union[str, Any]						=			AddedToken(_UpperCAmelCase		,      lstrip=_UpperCAmelCase		,      rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else mask_token

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

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

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


     @property
     # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return len(self.encoder)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return dict(self.encoder		,      **self.added_tokens_encoder)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          if token in self.cache:
               return self.cache[token]
          __A		:					List[str]						=			tuple(_UpperCAmelCase)
          __A		:					Optional[Any]						=			get_pairs(_UpperCAmelCase)

          if not pairs:
               return token

          while True:
               __A		:					Tuple						=			min(_UpperCAmelCase		,      key=lambda _UpperCAmelCase: self.bpe_ranks.get(_UpperCAmelCase		,      float('inf')))
               if bigram not in self.bpe_ranks:
                    break
               __A					,__A		:					Optional[int]						=			bigram
               __A		:					str						=			[]
               __A		:					List[Any]						=			0
               while i < len(_UpperCAmelCase):
                    try:
                         __A		:					Tuple						=			word.index(_UpperCAmelCase		,      _UpperCAmelCase)
                    except ValueError:
                         new_word.extend(word[i:])
                         break
                    else:
                         new_word.extend(word[i:j])
                         __A		:					List[Any]						=			j

                    if word[i] == first and i < len(_UpperCAmelCase) - 1 and word[i + 1] == second:
                         new_word.append(first + second)
                         i += 2
                    else:
                         new_word.append(word[i])
                         i += 1
               __A		:					List[Any]						=			tuple(_UpperCAmelCase)
               __A		:					Any						=			new_word
               if len(_UpperCAmelCase) == 1:
                    break
               else:
                    __A		:					Union[str, Any]						=			get_pairs(_UpperCAmelCase)
          __A		:					str						=			' '.join(_UpperCAmelCase)
          __A		:					Optional[Any]						=			word
          return word


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					List[str]						=			[]
          for token in re.findall(self.pat		,      _UpperCAmelCase):
               __A		:					List[str]						=			''.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(_UpperCAmelCase).split(' '))
          return bpe_tokens


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          return self.encoder.get(_UpperCAmelCase		,      self.encoder.get(self.unk_token))


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          return self.decoder.get(_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Optional[Any]						=			''.join(_UpperCAmelCase)
          __A		:					Union[str, Any]						=			bytearray([self.byte_decoder[c] for c in text]).decode('utf-8'		,      errors=self.errors)
          return text


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None):

          '''simple docstring'''






          if not os.path.isdir(_UpperCAmelCase):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory')
               return
          __A		:					Optional[Any]						=			os.path.join(
              _UpperCAmelCase		,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
          __A		:					Optional[Any]						=			os.path.join(
              _UpperCAmelCase		,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

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

          __A		:					Optional[int]						=			0
          with open(_UpperCAmelCase		,      '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 _UpperCAmelCase: 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!')
                         __A		:					List[Any]						=			token_index
                    writer.write(' '.join(_UpperCAmelCase) + '\n')
                    index += 1

          return vocab_file, merge_file


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None):

          '''simple docstring'''






          if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          __A		:					Union[str, Any]						=			[self.cls_token_id]
          __A		:					str						=			[self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None		,      _UpperCAmelCase = False):

          '''simple docstring'''






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

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


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None):

          '''simple docstring'''






          __A		:					Dict						=			[self.sep_token_id]
          __A		:					Dict						=			[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 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase=False		,      **_UpperCAmelCase):

          '''simple docstring'''






          __A		:					int						=			kwargs.pop('add_prefix_space'		,      self.add_prefix_space)
          if (is_split_into_words or add_prefix_space) and (len(_UpperCAmelCase) > 0 and not text[0].isspace()):
               __A		:					Optional[int]						=			' ' + text
          return (text, kwargs)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None		,      _UpperCAmelCase = PaddingStrategy.DO_NOT_PAD		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      ):

          '''simple docstring'''






          __A		:					List[Any]						=			super()._pad(
              encoded_inputs=_UpperCAmelCase		,      max_length=_UpperCAmelCase		,      padding_strategy=_UpperCAmelCase		,      pad_to_multiple_of=_UpperCAmelCase		,      return_attention_mask=_UpperCAmelCase		,      )

          # Load from model defaults
          if return_attention_mask is None:
               __A		:					Tuple						=			'attention_mask' in self.model_input_names

          if return_attention_mask and "global_attention_mask" in encoded_inputs:
               __A		:					List[str]						=			encoded_inputs[self.model_input_names[0]]
               # `global_attention_mask` need to have the same length as other (sequential) inputs.
               __A		:					int						=			len(encoded_inputs['global_attention_mask']) != len(_UpperCAmelCase)

               if needs_to_be_padded:
                    __A		:					Tuple						=			len(_UpperCAmelCase) - len(encoded_inputs['global_attention_mask'])

                    if self.padding_side == "right":
                         # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                         __A		:					Optional[int]						=			(
                             encoded_inputs['global_attention_mask'] + [-1] * difference
                         )
                    elif self.padding_side == "left":
                         __A		:					Any						=			[-1] * difference + encoded_inputs[
                             'global_attention_mask'
                         ]
                    else:
                         raise ValueError('Invalid padding strategy:' + str(self.padding_side))

          return encoded_inputs







'''simple docstring'''



import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[Any],       SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						Dict     )	->      List[Any]:

   """simple docstring"""
   # Initialise PyTorch model
   __a					  =     RemBertConfig.from_json_file(SCREAMING_SNAKE_CASE__     )
   print('Building PyTorch model from configuration: {}'.format(str(SCREAMING_SNAKE_CASE__     )     )     )
   __a					  =     RemBertModel(SCREAMING_SNAKE_CASE__     )

   # Load weights from tf checkpoint
   load_tf_weights_in_rembert(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )

   # Save pytorch-model
   print('Save PyTorch model to {}'.format(SCREAMING_SNAKE_CASE__     )     )
   torch.save(model.state_dict(),       SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 __UpperCamelCase   :							List[str]         =   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(
     """--rembert_config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained RemBERT 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   :							List[str]         =   parser.parse_args()
 convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)