



'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


lowerCamelCase	 =       logging.get_logger(__name__)


lowerCamelCase	 =       {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all BART models at https://huggingface.co/models?filter=bart
lowerCamelCase	 =       {
    """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""",
    },
    """tokenizer_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json""",
    },
}

lowerCamelCase	 =       {
    """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,
}





class _UpperCamelCase		(     A		):





				'''simple docstring'''





				lowerCAmelCase__					    =    VOCAB_FILES_NAMES
				lowerCAmelCase__					    =    PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase__					    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase__					    =    ["""input_ids""", """attention_mask"""]
				lowerCAmelCase__					    =    BartTokenizer

				def __init__(							self  :  Optional[int]  ,       _lowerCAmelCase  :  Any=None  ,       _lowerCAmelCase  :  Tuple=None  ,       _lowerCAmelCase  :  Optional[int]=None  ,       _lowerCAmelCase  :  str="replace"  ,       _lowerCAmelCase  :  List[Any]="<s>"  ,       _lowerCAmelCase  :  int="</s>"  ,       _lowerCAmelCase  :  Dict="</s>"  ,       _lowerCAmelCase  :  Optional[int]="<s>"  ,       _lowerCAmelCase  :  str="<unk>"  ,       _lowerCAmelCase  :  List[str]="<pad>"  ,       _lowerCAmelCase  :  Dict="<mask>"  ,       _lowerCAmelCase  :  Tuple=False  ,       _lowerCAmelCase  :  str=True  ,       **_lowerCAmelCase  :  Tuple  ,       ):







											'''simple docstring'''

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

											__lowercase       	=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
											if pre_tok_state.get('add_prefix_space'  ,       _lowerCAmelCase) != add_prefix_space:
																		__lowercase       	=getattr(_lowerCAmelCase  ,       pre_tok_state.pop('type'))
																		__lowercase       	=add_prefix_space
																		__lowercase       	=pre_tok_class(**_lowerCAmelCase)

											__lowercase       	=add_prefix_space

											# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
											__lowercase       	='post_processor'
											__lowercase       	=getattr(self.backend_tokenizer  ,       _lowerCAmelCase  ,       _lowerCAmelCase)
											if tokenizer_component_instance:
																		__lowercase       	=json.loads(tokenizer_component_instance.__getstate__())

																		# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																		if "sep" in state:
																									__lowercase       	=tuple(state['sep'])
																		if "cls" in state:
																									__lowercase       	=tuple(state['cls'])

																		__lowercase       	=False

																		if state.get('add_prefix_space'  ,       _lowerCAmelCase) != add_prefix_space:
																									__lowercase       	=add_prefix_space
																									__lowercase       	=True

																		if state.get('trim_offsets'  ,       _lowerCAmelCase) != trim_offsets:
																									__lowercase       	=trim_offsets
																									__lowercase       	=True

																		if changes_to_apply:
																									__lowercase       	=getattr(_lowerCAmelCase  ,       state.pop('type'))
																									__lowercase       	=component_class(**_lowerCAmelCase)
																									setattr(self.backend_tokenizer  ,       _lowerCAmelCase  ,       _lowerCAmelCase)

				@property
				def 				__lowerCamelCase     (							self  :  List[str]):







											'''simple docstring'''

											if self._mask_token is None:
																		if self.verbose:
																									logger.error('Using mask_token, but it is not set yet.')
																		return None
											return str(self._mask_token)

				@mask_token.setter
				def 				__lowerCamelCase     (							self  :  Union[str, Any]  ,       _lowerCAmelCase  :  Optional[int]):







											'''simple docstring'''

											__lowercase       	=AddedToken(_lowerCAmelCase  ,       lstrip=_lowerCAmelCase  ,       rstrip=_lowerCAmelCase) if isinstance(_lowerCAmelCase  ,       _lowerCAmelCase) else value
											__lowercase       	=value

				def 				__lowerCamelCase     (							self  :  List[Any]  ,       *_lowerCAmelCase  :  int  ,       **_lowerCAmelCase  :  str):







											'''simple docstring'''

											__lowercase       	=kwargs.get('is_split_into_words'  ,       _lowerCAmelCase)

											if is_split_into_words and not self.add_prefix_space:
																		raise ValueError(
																		    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
																		    'to use it with pretokenized inputs.')

											return super()._batch_encode_plus(*_lowerCAmelCase  ,       **_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  List[Any]  ,       *_lowerCAmelCase  :  List[str]  ,       **_lowerCAmelCase  :  Tuple):







											'''simple docstring'''

											__lowercase       	=kwargs.get('is_split_into_words'  ,       _lowerCAmelCase)

											if is_split_into_words and not self.add_prefix_space:
																		raise ValueError(
																		    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
																		    'to use it with pretokenized inputs.')

											return super()._encode_plus(*_lowerCAmelCase  ,       **_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  int  ,       _lowerCAmelCase  :  str  ,       _lowerCAmelCase  :  Optional[str] = None):







											'''simple docstring'''

											__lowercase       	=self._tokenizer.model.save(_lowerCAmelCase  ,       name=_lowerCAmelCase)
											return tuple(_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  str  ,       _lowerCAmelCase  :  Union[str, Any]  ,       _lowerCAmelCase  :  List[str]=None):







											'''simple docstring'''

											__lowercase       	=[self.bos_token_id] + token_ids_a + [self.eos_token_id]
											if token_ids_a is None:
																		return output

											return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]

				def 				__lowerCamelCase     (							self  :  List[str]  ,       _lowerCAmelCase  :  List[int]  ,       _lowerCAmelCase  :  Optional[List[int]] = None):







											'''simple docstring'''

											__lowercase       	=[self.sep_token_id]
											__lowercase       	=[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]









'''simple docstring'''



from __future__ import annotations



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase = None  ,   _lowerCAmelCase = None  ,   _lowerCAmelCase = False  ,   ):



							"""simple docstring"""



							__lowercase       	=cipher_alphabet or [chr(_lowerCAmelCase					) 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)
														__lowercase       	={
														    'a': 0.0_84_97,
														    'b': 0.0_14_92,
														    'c': 0.0_22_02,
														    'd': 0.0_42_53,
														    'e': 0.1_11_62,
														    'f': 0.0_22_28,
														    'g': 0.0_20_15,
														    'h': 0.0_60_94,
														    'i': 0.0_75_46,
														    'j': 0.0_01_53,
														    'k': 0.0_12_92,
														    'l': 0.0_40_25,
														    'm': 0.0_24_06,
														    'n': 0.0_67_49,
														    'o': 0.0_75_07,
														    'p': 0.0_19_29,
														    'q': 0.0_00_95,
														    'r': 0.0_75_87,
														    's': 0.0_63_27,
														    't': 0.0_93_56,
														    'u': 0.0_27_58,
														    'v': 0.0_09_78,
														    'w': 0.0_25_60,
														    'x': 0.0_01_50,
														    'y': 0.0_19_94,
														    'z': 0.0_00_77,
														}
							else:
														# Custom frequencies dictionary
														__lowercase       	=frequencies_dict

							if not case_sensitive:
														__lowercase       	=ciphertext.lower()

							# Chi squared statistic values
							__lowercase       	={}

							# cycle through all of the shifts
							for shift in range(len(_lowerCAmelCase					)					):
														__lowercase       	=''

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

														__lowercase       	=0.0

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

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

																																			# Complete the chi squared statistic formula
																																			__lowercase       	=((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
																																			__lowercase       	=decrypted_with_shift.count(_lowerCAmelCase					)

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

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

							__lowercase       	=min(
							    _lowerCAmelCase  ,   key=_lowerCAmelCase  ,   )

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



