



"""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


SCREAMING_SNAKE_CASE					:  Any        =						logging.get_logger(__name__)


SCREAMING_SNAKE_CASE					:  Dict        =						{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all LED models at https://huggingface.co/models?filter=LED
SCREAMING_SNAKE_CASE					:  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''',
    },
}

SCREAMING_SNAKE_CASE					:  List[str]        =						{
    '''allenai/led-base-16384''': 1_6_3_8_4,
}






@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 						__UpperCAmelCase       (							)				->						List[str]:






   """simple docstring"""


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






def 						__UpperCAmelCase       (							snake_case_	:	Optional[Any] )				->						List[Any]:






   """simple docstring"""


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



class    __lowerCamelCase						( lowerCamelCase__     ):
   __UpperCamelCase						= VOCAB_FILES_NAMES
   __UpperCamelCase						= PRETRAINED_VOCAB_FILES_MAP
   __UpperCamelCase						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCamelCase						= ['input_ids', 'attention_mask']







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




      '''simple docstring'''




      _lowerCAmelCase         =		AddedToken(a__							,						lstrip=a__							,						rstrip=a__					) if isinstance(a__							,						a__					) else bos_token
      _lowerCAmelCase         =		AddedToken(a__							,						lstrip=a__							,						rstrip=a__					) if isinstance(a__							,						a__					) else eos_token
      _lowerCAmelCase         =		AddedToken(a__							,						lstrip=a__							,						rstrip=a__					) if isinstance(a__							,						a__					) else sep_token
      _lowerCAmelCase         =		AddedToken(a__							,						lstrip=a__							,						rstrip=a__					) if isinstance(a__							,						a__					) else cls_token
      _lowerCAmelCase         =		AddedToken(a__							,						lstrip=a__							,						rstrip=a__					) if isinstance(a__							,						a__					) else unk_token
      _lowerCAmelCase         =		AddedToken(a__							,						lstrip=a__							,						rstrip=a__					) if isinstance(a__							,						a__					) else pad_token

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

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

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

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      _lowerCAmelCase         =		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 				A__    (self					):




      '''simple docstring'''




      return len(self.encoder					)







   def 				A__    (self					):




      '''simple docstring'''




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







   def 				A__    (self							,						lowerCamelCase					):




      '''simple docstring'''




      if token in self.cache:
         return self.cache[token]
      _lowerCAmelCase         =		tuple(a__					)
      _lowerCAmelCase         =		get_pairs(a__					)

      if not pairs:
         return token

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

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







   def 				A__    (self							,						lowerCamelCase					):




      '''simple docstring'''




      _lowerCAmelCase         =		[]
      for token in re.findall(self.pat							,						a__					):
         _lowerCAmelCase         =		''''''.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(a__					).split(""" """					)					)
      return bpe_tokens







   def 				A__    (self							,						lowerCamelCase					):




      '''simple docstring'''




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







   def 				A__    (self							,						lowerCamelCase					):




      '''simple docstring'''




      return self.decoder.get(a__					)







   def 				A__    (self							,						lowerCamelCase					):




      '''simple docstring'''




      _lowerCAmelCase         =		''''''.join(a__					)
      _lowerCAmelCase         =		bytearray([self.byte_decoder[c] for c in text]					).decode("""utf-8"""							,						errors=self.errors					)
      return text







   def 				A__    (self							,						lowerCamelCase							,						lowerCamelCase = None					):




      '''simple docstring'''




      if not os.path.isdir(a__					):
         logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
         return
      _lowerCAmelCase         =		os.path.join(
          a__							,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)
      _lowerCAmelCase         =		os.path.join(
          a__							,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]					)

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

      _lowerCAmelCase         =		0
      with open(a__							,						"""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 lowerCamelCase					: 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!"""					)
               _lowerCAmelCase         =		token_index
            writer.write(""" """.join(a__					) + """\n"""					)
            index += 1

      return vocab_file, merge_file







   def 				A__    (self							,						lowerCamelCase							,						lowerCamelCase = None					):




      '''simple docstring'''




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







   def 				A__    (self							,						lowerCamelCase							,						lowerCamelCase = None							,						lowerCamelCase = False					):




      '''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 				A__    (self							,						lowerCamelCase							,						lowerCamelCase = None					):




      '''simple docstring'''




      _lowerCAmelCase         =		[self.sep_token_id]
      _lowerCAmelCase         =		[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 				A__    (self							,						lowerCamelCase							,						lowerCamelCase=False							,						**lowerCamelCase					):




      '''simple docstring'''




      _lowerCAmelCase         =		kwargs.pop("""add_prefix_space"""							,						self.add_prefix_space					)
      if (is_split_into_words or add_prefix_space) and (len(a__					) > 0 and not text[0].isspace()):
         _lowerCAmelCase         =		''' ''' + text
      return (text, kwargs)







   def 				A__    (self							,						lowerCamelCase							,						lowerCamelCase = None							,						lowerCamelCase = PaddingStrategy.DO_NOT_PAD							,						lowerCamelCase = None							,						lowerCamelCase = None							,						):




      '''simple docstring'''




      _lowerCAmelCase         =		super()._pad(
          encoded_inputs=a__							,						max_length=a__							,						padding_strategy=a__							,						pad_to_multiple_of=a__							,						return_attention_mask=a__							,						)

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

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

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

      return encoded_inputs






from __future__ import annotations




def  __UpperCAmelCase(   lowercase_			,     lowercase_ = None			,     lowercase_ = None			,     lowercase_ = False			,     ):
       _lowerCamelCase				:  Tuple						=		cipher_alphabet or [chr(lowercase_   ) for i in range(97			,     1_23   )]

       # If the argument is None or the user provided an empty dictionary
       if not frequencies_dict:
              # Frequencies of letters in the english language (how much they show up)
              _lowerCamelCase				:  Dict						=		{
                  '''a''': 0.0_8_4_9_7,
                  '''b''': 0.0_1_4_9_2,
                  '''c''': 0.0_2_2_0_2,
                  '''d''': 0.0_4_2_5_3,
                  '''e''': 0.1_1_1_6_2,
                  '''f''': 0.0_2_2_2_8,
                  '''g''': 0.0_2_0_1_5,
                  '''h''': 0.0_6_0_9_4,
                  '''i''': 0.0_7_5_4_6,
                  '''j''': 0.0_0_1_5_3,
                  '''k''': 0.0_1_2_9_2,
                  '''l''': 0.0_4_0_2_5,
                  '''m''': 0.0_2_4_0_6,
                  '''n''': 0.0_6_7_4_9,
                  '''o''': 0.0_7_5_0_7,
                  '''p''': 0.0_1_9_2_9,
                  '''q''': 0.0_0_0_9_5,
                  '''r''': 0.0_7_5_8_7,
                  '''s''': 0.0_6_3_2_7,
                  '''t''': 0.0_9_3_5_6,
                  '''u''': 0.0_2_7_5_8,
                  '''v''': 0.0_0_9_7_8,
                  '''w''': 0.0_2_5_6_0,
                  '''x''': 0.0_0_1_5_0,
                  '''y''': 0.0_1_9_9_4,
                  '''z''': 0.0_0_0_7_7,
              }
       else:
              # Custom frequencies dictionary
              _lowerCamelCase				:  Tuple						=		frequencies_dict

       if not case_sensitive:
              _lowerCamelCase				:  Optional[int]						=		ciphertext.lower()

       # Chi squared statistic values
       _lowerCamelCase				:  dict[int, tuple[float, str]]						=		{}

       # cycle through all of the shifts
       for shift in range(len(lowercase_   )   ):
              _lowerCamelCase				:  Any						=		''''''

              # decrypt the message with the shift
              for letter in ciphertext:
                     try:
                            # Try to index the letter in the alphabet
                            _lowerCamelCase				:  int						=		(alphabet_letters.index(letter.lower()   ) - shift) % len(
                                lowercase_   )
                            decrypted_with_shift += (
                                alphabet_letters[new_key].upper()
                                if case_sensitive and letter.isupper()
                                else alphabet_letters[new_key]
                            )
                     except ValueError:
                            # Append the character if it isn't in the alphabet
                            decrypted_with_shift += letter

              _lowerCamelCase				:  Optional[Any]						=		0.0

              # Loop through each letter in the decoded message with the shift
              for letter in decrypted_with_shift:
                     if case_sensitive:
                            _lowerCamelCase				:  Any						=		letter.lower()
                            if letter in frequencies:
                                   # Get the amount of times the letter occurs in the message
                                   _lowerCamelCase				:  List[str]						=		decrypted_with_shift.lower().count(lowercase_   )

                                   # Get the excepcted amount of times the letter should appear based
                                   # on letter frequencies
                                   _lowerCamelCase				:  Optional[Any]						=		frequencies[letter] * occurrences

                                   # Complete the chi squared statistic formula
                                   _lowerCamelCase				:  int						=		((occurrences - expected) ** 2) / expected

                                   # Add the margin of error to the total chi squared statistic
                                   chi_squared_statistic += chi_letter_value
                     else:
                            if letter.lower() in frequencies:
                                   # Get the amount of times the letter occurs in the message
                                   _lowerCamelCase				:  Union[str, Any]						=		decrypted_with_shift.count(lowercase_   )

                                   # Get the excepcted amount of times the letter should appear based
                                   # on letter frequencies
                                   _lowerCamelCase				:  List[str]						=		frequencies[letter] * occurrences

                                   # Complete the chi squared statistic formula
                                   _lowerCamelCase				:  str						=		((occurrences - expected) ** 2) / expected

                                   # Add the margin of error to the total chi squared statistic
                                   chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
              _lowerCamelCase				:  Tuple						=		(
                  chi_squared_statistic,
                  decrypted_with_shift,
              )

       # Get the most likely cipher by finding the cipher with the smallest chi squared
       # statistic
       def chi_squared_statistic_values_sorting_key(lowercase_   ) -> tuple[float, str]:
              return chi_squared_statistic_values[key]

       _lowerCamelCase				:  int						=		min(
           lowercase_			,     key=lowercase_			,     )

       # Get all the data from the most likely cipher (key, decoded message)
       (
           (
           _lowerCamelCase
       ),	(
           _lowerCamelCase
       ),	
       )				:  Optional[int]						=		chi_squared_statistic_values[most_likely_cipher]

       # Return the data on the most likely shift
       return (
           most_likely_cipher,
           most_likely_cipher_chi_squared_value,
           decoded_most_likely_cipher,
       )

