


'''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


a_   :      str				    =      logging.get_logger(__name__)


a_   :      Dict				    =      {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all LED models at https://huggingface.co/models?filter=LED
a_   :      str				    =      {
    "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",
    },
}

a_   :      Union[str, Any]				    =      {
    "allenai/led-base-16384": 1_6_3_8_4,
}






@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 		_A			()				->					Optional[Any]:




     '''simple docstring'''



     _a      =							(
         list(range(ord('!'						)			,  ord('~'						) + 1						)						) + list(range(ord('¡'						)			,  ord('¬'						) + 1						)						) + list(range(ord('®'						)			,  ord('ÿ'						) + 1						)						)
     )
     _a      =							bs[:]
     _a      =							0
     for b in range(2**8						):
          if b not in bs:
               bs.append(lowerCAmelCase__						)
               cs.append(2**8 + n						)
               n += 1
     _a      =							[chr(lowerCAmelCase__						) for n in cs]
     return dict(zip(lowerCAmelCase__			,  lowerCAmelCase__						)						)






def 		_A			(lowerCAmelCase__					:Dict						)				->					Any:




     '''simple docstring'''



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






class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		VOCAB_FILES_NAMES
 _lowerCAmelCase	   =		PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase	   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase	   =		["""input_ids""", """attention_mask"""]
 def __init__(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__="replace" ,			__magic_name__="<s>" ,			__magic_name__="</s>" ,			__magic_name__="</s>" ,			__magic_name__="<s>" ,			__magic_name__="<unk>" ,			__magic_name__="<pad>" ,			__magic_name__="<mask>" ,			__magic_name__=False ,			**__magic_name__ ,			)   ->							List[Any]:
      _a      =							AddedToken(__magic_name__ ,			lstrip=__magic_name__ ,			rstrip=__magic_name__  ) if isinstance(__magic_name__ ,			__magic_name__  ) else bos_token
      _a      =							AddedToken(__magic_name__ ,			lstrip=__magic_name__ ,			rstrip=__magic_name__  ) if isinstance(__magic_name__ ,			__magic_name__  ) else eos_token
      _a      =							AddedToken(__magic_name__ ,			lstrip=__magic_name__ ,			rstrip=__magic_name__  ) if isinstance(__magic_name__ ,			__magic_name__  ) else sep_token
      _a      =							AddedToken(__magic_name__ ,			lstrip=__magic_name__ ,			rstrip=__magic_name__  ) if isinstance(__magic_name__ ,			__magic_name__  ) else cls_token
      _a      =							AddedToken(__magic_name__ ,			lstrip=__magic_name__ ,			rstrip=__magic_name__  ) if isinstance(__magic_name__ ,			__magic_name__  ) else unk_token
      _a      =							AddedToken(__magic_name__ ,			lstrip=__magic_name__ ,			rstrip=__magic_name__  ) if isinstance(__magic_name__ ,			__magic_name__  ) else pad_token

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

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

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

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      _a      =							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 							__UpperCAmelCase						(	self  )   ->							int:
      return len(self.encoder  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      return dict(self.encoder ,			**self.added_tokens_encoder  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[str]:
      if token in self.cache:
           return self.cache[token]
      _a      =							tuple(__magic_name__  )
      _a      =							get_pairs(__magic_name__  )

      if not pairs:
           return token

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

                if word[i] == first and i < len(__magic_name__  ) - 1 and word[i + 1] == second:
                     new_word.append(first + second  )
                     i += 2
                else:
                     new_word.append(word[i]  )
                     i += 1
           _a      =							tuple(__magic_name__  )
           _a      =							new_word
           if len(__magic_name__  ) == 1:
                break
           else:
                _a      =							get_pairs(__magic_name__  )
      _a      =							' '.join(__magic_name__  )
      _a      =							word
      return word
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							int:
      _a      =							[]
      for token in re.findall(self.pat ,			__magic_name__  ):
           _a      =							''.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(__magic_name__  ).split(' '  )  )
      return bpe_tokens
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[str]:
      return self.encoder.get(__magic_name__ ,			self.encoder.get(self.unk_token  )  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Dict:
      return self.decoder.get(__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Dict:
      _a      =							''.join(__magic_name__  )
      _a      =							bytearray([self.byte_decoder[c] for c in text]  ).decode('utf-8' ,			errors=self.errors  )
      return text
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None  )   ->							Tuple[str]:
      if not os.path.isdir(__magic_name__  ):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'  )
           return
      _a      =							os.path.join(
          __magic_name__ ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )
      _a      =							os.path.join(
          __magic_name__ ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']  )

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

      _a      =							0
      with open(__magic_name__ ,			'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 __magic_name__  : 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      =							token_index
                writer.write(' '.join(__magic_name__  ) + '\n'  )
                index += 1

      return vocab_file, merge_file
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None  )   ->							List[int]:
      if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      _a      =							[self.cls_token_id]
      _a      =							[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  )   ->							List[int]:
      if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=__magic_name__ ,			token_ids_a=__magic_name__ ,			already_has_special_tokens=__magic_name__  )

      if token_ids_a is None:
           return [1] + ([0] * len(__magic_name__  )) + [1]
      return [1] + ([0] * len(__magic_name__  )) + [1, 1] + ([0] * len(__magic_name__  )) + [1]
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None  )   ->							List[int]:
      _a      =							[self.sep_token_id]
      _a      =							[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 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__=False ,			**__magic_name__  )   ->							Optional[Any]:
      _a      =							kwargs.pop('add_prefix_space' ,			self.add_prefix_space  )
      if (is_split_into_words or add_prefix_space) and (len(__magic_name__  ) > 0 and not text[0].isspace()):
           _a      =							' ' + text
      return (text, kwargs)




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None ,			__magic_name__ = PaddingStrategy.DO_NOT_PAD ,			__magic_name__ = None ,			__magic_name__ = None ,			)   ->							dict:
      _a      =							super()._pad(
          encoded_inputs=__magic_name__ ,			max_length=__magic_name__ ,			padding_strategy=__magic_name__ ,			pad_to_multiple_of=__magic_name__ ,			return_attention_mask=__magic_name__ ,			)

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

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

           if needs_to_be_padded:
                _a      =							len(__magic_name__  ) - 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      =							(
                         encoded_inputs['global_attention_mask'] + [-1] * difference
                     )
                elif self.padding_side == "left":
                     _a      =							[-1] * difference + encoded_inputs[
                         'global_attention_mask'
                     ]
                else:
                     raise ValueError('Invalid padding strategy:' + str(self.padding_side  )  )

      return encoded_inputs





'''simple docstring'''






def 		_A			(lowerCAmelCase__					:list						)				->					float:




     '''simple docstring'''



     _a      =							0
     while len(lowerCAmelCase__						) > 1:
          _a      =							0
          # Consider two files with minimum cost to be merged
          for _ in range(2						):
               _a      =							files.index(min(lowerCAmelCase__						)						)
               temp += files[min_index]
               files.pop(lowerCAmelCase__						)
          files.append(lowerCAmelCase__						)
          optimal_merge_cost += temp
     return optimal_merge_cost


if __name__ == "__main__":
 import doctest

 doctest.testmod()

