
"""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


UpperCamelCase_					:						Optional[Any]				= logging.get_logger(__name__)


UpperCamelCase_					:						Any				= {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all BART models at https://huggingface.co/models?filter=bart
UpperCamelCase_					:						str				= {
    '''vocab_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
    },
}

UpperCamelCase_					:						int				= {
    '''facebook/bart-base''': 1024,
    '''facebook/bart-large''': 1024,
    '''facebook/bart-large-mnli''': 1024,
    '''facebook/bart-large-cnn''': 1024,
    '''facebook/bart-large-xsum''': 1024,
    '''yjernite/bart_eli5''': 1024,
}
@lru_cache()
def 							A_     ():

					'''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(__a				)
															cs.append(2**8 + n				)
															n += 1
					A_				 =						[chr(__a				) for n in cs]
					return dict(zip(__a				,   __a				)				)
def 							A_     (__a				):

					'''simple docstring'''





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




class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			snake_case	      =				VOCAB_FILES_NAMES
			snake_case	      =				PRETRAINED_VOCAB_FILES_MAP
			snake_case	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case	      =				["input_ids", "attention_mask"]




			def __init__(				self						:					List[str]       ,   _snake_case						:					Optional[Any]       ,   _snake_case						:					Dict       ,   _snake_case						:					int="replace"       ,   _snake_case						:					Union[str, Any]="<s>"       ,   _snake_case						:					int="</s>"       ,   _snake_case						:					int="</s>"       ,   _snake_case						:					Any="<s>"       ,   _snake_case						:					int="<unk>"       ,   _snake_case						:					List[Any]="<pad>"       ,   _snake_case						:					Any="<mask>"       ,   _snake_case						:					Optional[Any]=False       ,   **_snake_case						:					Any       ,   )      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						AddedToken(_snake_case       ,   lstrip=_snake_case       ,   rstrip=_snake_case	) if isinstance(_snake_case       ,   _snake_case	) else bos_token
								A_				 =						AddedToken(_snake_case       ,   lstrip=_snake_case       ,   rstrip=_snake_case	) if isinstance(_snake_case       ,   _snake_case	) else eos_token
								A_				 =						AddedToken(_snake_case       ,   lstrip=_snake_case       ,   rstrip=_snake_case	) if isinstance(_snake_case       ,   _snake_case	) else sep_token
								A_				 =						AddedToken(_snake_case       ,   lstrip=_snake_case       ,   rstrip=_snake_case	) if isinstance(_snake_case       ,   _snake_case	) else cls_token
								A_				 =						AddedToken(_snake_case       ,   lstrip=_snake_case       ,   rstrip=_snake_case	) if isinstance(_snake_case       ,   _snake_case	) else unk_token
								A_				 =						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
								A_				 =						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:
													A_				 =						json.load(_snake_case	)
								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(_snake_case       ,   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(_snake_case       ,   range(len(_snake_case	)	)	)	)
								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
			def      lowerCamelCase__     (				self						:					str	)      -> str:







								"""simple docstring"""
								return len(self.encoder	)




			def      lowerCamelCase__     (				self						:					Dict	)      -> Tuple:







								"""simple docstring"""
								return dict(self.encoder       ,   **self.added_tokens_encoder	)




			def      lowerCamelCase__     (				self						:					Optional[int]       ,   _snake_case						:					int	)      -> int:







								"""simple docstring"""
								if token in self.cache:
													return self.cache[token]
								A_				 =						tuple(_snake_case	)
								A_				 =						get_pairs(_snake_case	)

								if not pairs:
													return token

								while True:
													A_				 =						min(_snake_case       ,   key=lambda _snake_case	: self.bpe_ranks.get(_snake_case       ,   float("inf"	)	)	)
													if bigram not in self.bpe_ranks:
																		break
													A_ ,    A_				 =						bigram
													A_				 =						[]
													A_				 =						0
													while i < len(_snake_case	):
																		try:
																							A_				 =						word.index(_snake_case       ,   _snake_case	)
																		except ValueError:
																							new_word.extend(word[i:]	)
																							break
																		else:
																							new_word.extend(word[i:j]	)
																							A_				 =						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
													A_				 =						tuple(_snake_case	)
													A_				 =						new_word
													if len(_snake_case	) == 1:
																		break
													else:
																		A_				 =						get_pairs(_snake_case	)
								A_				 =						" ".join(_snake_case	)
								A_				 =						word
								return word




			def      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					Union[str, Any]	)      -> List[str]:







								"""simple docstring"""
								A_				 =						[]
								for token in re.findall(self.pat       ,   _snake_case	):
													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(_snake_case	).split(" "	)	)
								return bpe_tokens




			def      lowerCamelCase__     (				self						:					Optional[Any]       ,   _snake_case						:					int	)      -> List[str]:







								"""simple docstring"""
								return self.encoder.get(_snake_case       ,   self.encoder.get(self.unk_token	)	)




			def      lowerCamelCase__     (				self						:					Optional[Any]       ,   _snake_case						:					List[str]	)      -> Optional[int]:







								"""simple docstring"""
								return self.decoder.get(_snake_case	)




			def      lowerCamelCase__     (				self						:					int       ,   _snake_case						:					Union[str, Any]	)      -> Any:







								"""simple docstring"""
								A_				 =						"".join(_snake_case	)
								A_				 =						bytearray([self.byte_decoder[c] for c in text]	).decode("utf-8"       ,   errors=self.errors	)
								return text




			def      lowerCamelCase__     (				self						:					Optional[int]       ,   _snake_case						:					str       ,   _snake_case						:					Optional[str] = None	)      -> Tuple[str]:







								"""simple docstring"""
								if not os.path.isdir(_snake_case	):
													logger.error(F'Vocabulary path ({save_directory}) should be a directory'	)
													return
								A_				 =						os.path.join(
								    _snake_case       ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
								A_				 =						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"	)

								A_				 =						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!"	)
																							A_				 =						token_index
																		writer.write(" ".join(_snake_case	) + "\n"	)
																		index += 1

								return vocab_file, merge_file




			def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					List[int]       ,   _snake_case						:					Optional[List[int]] = None	)      -> List[int]:







								"""simple docstring"""
								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      lowerCamelCase__     (				self						:					Optional[Any]       ,   _snake_case						:					List[int]       ,   _snake_case						:					Optional[List[int]] = None       ,   _snake_case						:					bool = False	)      -> List[int]:







								"""simple docstring"""
								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      lowerCamelCase__     (				self						:					Optional[int]       ,   _snake_case						:					List[int]       ,   _snake_case						:					Optional[List[int]] = None	)      -> List[int]:







								"""simple docstring"""
								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      lowerCamelCase__     (				self						:					Optional[int]       ,   _snake_case						:					Any       ,   _snake_case						:					int=False       ,   **_snake_case						:					Tuple	)      -> Tuple:







								"""simple docstring"""
								A_				 =						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()):
													A_				 =						" " + text
								return (text, kwargs)






"""simple docstring"""





from __future__ import annotations
def 							A_     (__a				,   __a = None				,   __a = None				,   __a = False				,   ):

					'''simple docstring'''





					A_				 =						cipher_alphabet or [chr(__a				) for i in range(97				,   123				)]

					# 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)
										A_				 =						{
										    "a": 0.08497,
										    "b": 0.01492,
										    "c": 0.02202,
										    "d": 0.04253,
										    "e": 0.11162,
										    "f": 0.02228,
										    "g": 0.02015,
										    "h": 0.06094,
										    "i": 0.07546,
										    "j": 0.00153,
										    "k": 0.01292,
										    "l": 0.04025,
										    "m": 0.02406,
										    "n": 0.06749,
										    "o": 0.07507,
										    "p": 0.01929,
										    "q": 0.00095,
										    "r": 0.07587,
										    "s": 0.06327,
										    "t": 0.09356,
										    "u": 0.02758,
										    "v": 0.00978,
										    "w": 0.02560,
										    "x": 0.00150,
										    "y": 0.01994,
										    "z": 0.00077,
										}
					else:
										# Custom frequencies dictionary
										A_				 =						frequencies_dict

					if not case_sensitive:
										A_				 =						ciphertext.lower()

					# Chi squared statistic values
					A_				 =						{}

					# cycle through all of the shifts
					for shift in range(len(__a				)				):
										A_				 =						""

										# decrypt the message with the shift
										for letter in ciphertext:
															try:
																				# Try to index the letter in the alphabet
																				A_				 =						(alphabet_letters.index(letter.lower()				) - shift) % len(
																				    __a				)
																				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

										A_				 =						0.0

										# Loop through each letter in the decoded message with the shift
										for letter in decrypted_with_shift:
															if case_sensitive:
																				A_				 =						letter.lower()
																				if letter in frequencies:
																									# Get the amount of times the letter occurs in the message
																									A_				 =						decrypted_with_shift.lower().count(__a				)

																									# Get the excepcted amount of times the letter should appear based
																									# on letter frequencies
																									A_				 =						frequencies[letter] * occurrences

																									# Complete the chi squared statistic formula
																									A_				 =						((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
																									A_				 =						decrypted_with_shift.count(__a				)

																									# Get the excepcted amount of times the letter should appear based
																									# on letter frequencies
																									A_				 =						frequencies[letter] * occurrences

																									# Complete the chi squared statistic formula
																									A_				 =						((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
										A_				 =						(
										    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(__a				) -> tuple[float, str]:
										return chi_squared_statistic_values[key]

					A_				 =						min(
					    __a				,   key=__a				,   )

					# Get all the data from the most likely cipher (key, decoded message)
					(
					    (
					    A_
					) ,    (
					    A_
					) ,    
					)				 =						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,
					)




