


from __future__ import annotations

def 			lowerCAmelCase__					(     a__:						str     ,    a__:						list[str] | None = None     ,    a__:						dict[str, float] | None = None     ,    a__:						bool = False     ,    )    ->						tuple[int, float, str]:


							'''simple docstring'''





							_UpperCAmelCase											=     cipher_alphabet or [chr(a__			) for i in range(9_7     ,    1_2_3			)]

							# 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)
														_UpperCAmelCase											=     {
														    'a': 0.08_497,
														    'b': 0.01_492,
														    'c': 0.02_202,
														    'd': 0.04_253,
														    'e': 0.11_162,
														    'f': 0.02_228,
														    'g': 0.02_015,
														    'h': 0.06_094,
														    'i': 0.07_546,
														    'j': 0.00_153,
														    'k': 0.01_292,
														    'l': 0.04_025,
														    'm': 0.02_406,
														    'n': 0.06_749,
														    'o': 0.07_507,
														    'p': 0.01_929,
														    'q': 0.00_095,
														    'r': 0.07_587,
														    's': 0.06_327,
														    't': 0.09_356,
														    'u': 0.02_758,
														    'v': 0.00_978,
														    'w': 0.02_560,
														    'x': 0.00_150,
														    'y': 0.01_994,
														    'z': 0.00_077,
														}
							else:
														# Custom frequencies dictionary
														_UpperCAmelCase											=     frequencies_dict

							if not case_sensitive:
														_UpperCAmelCase											=     ciphertext.lower()

							# Chi squared statistic values
							_UpperCAmelCase											=     {}

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

														# decrypt the message with the shift
														for letter in ciphertext:
																					try:
																												# Try to index the letter in the alphabet
																												_UpperCAmelCase											=     (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

														_UpperCAmelCase											=     0.0

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

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

																																			# Complete the chi squared statistic formula
																																			_UpperCAmelCase											=     ((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
																																			_UpperCAmelCase											=     decrypted_with_shift.count(a__			)

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

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

							_UpperCAmelCase											=     min(
							    a__     ,    key=a__     ,    )

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




import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase__    :List[str]       =				logging.getLogger(__name__)

@dataclass(frozen=UpperCAmelCase     )
class 						__a :
		_a      :    str
		_a      :    str
		_a      :    Optional[str]           =      None
		_a      :    Optional[str]           =      None
		_a      :    Optional[str]           =      None



@dataclass(frozen=UpperCAmelCase     )
class 						__a :
		_a      :    List[int]
		_a      :    Optional[List[int]]           =      None
		_a      :    Optional[List[int]]           =      None
		_a      :    Optional[Union[int, float]]           =      None
		_a      :    Optional[int]           =      None


if is_torch_available():
		import torch
		from torch.utils.data import Dataset

		class 						__a (							UpperCAmelCase     ):
				_a      :    List[InputFeatures]
				def __init__(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE=False     ,       _SCREAMING_SNAKE_CASE = False     ,       )					->			List[Any]:




											"""simple docstring"""





											_UpperCAmelCase											=     hans_processors[task]()

											_UpperCAmelCase											=     os.path.join(
											    _SCREAMING_SNAKE_CASE     ,       'cached_{}_{}_{}_{}'.format(
											        'dev' if evaluate else 'train'     ,       tokenizer.__class__.__name__     ,       str(_SCREAMING_SNAKE_CASE		)     ,       _SCREAMING_SNAKE_CASE     ,       )     ,       )
											_UpperCAmelCase											=     processor.get_labels()
											if tokenizer.__class__ in (
											    RobertaTokenizer,
											    RobertaTokenizerFast,
											    XLMRobertaTokenizer,
											    BartTokenizer,
											    BartTokenizerFast,
											):
																		# HACK(label indices are swapped in RoBERTa pretrained model)
																		_UpperCAmelCase						,			_UpperCAmelCase											=     label_list[2], label_list[1]
											_UpperCAmelCase											=     label_list

											# Make sure only the first process in distributed training processes the dataset,
											# and the others will use the cache.
											_UpperCAmelCase											=     cached_features_file + '.lock'
											with FileLock(_SCREAMING_SNAKE_CASE		):
																		if os.path.exists(_SCREAMING_SNAKE_CASE		) and not overwrite_cache:
																									logger.info(f'''Loading features from cached file {cached_features_file}'''		)
																									_UpperCAmelCase											=     torch.load(_SCREAMING_SNAKE_CASE		)
																		else:
																									logger.info(f'''Creating features from dataset file at {data_dir}'''		)

																									_UpperCAmelCase											=     (
																									    processor.get_dev_examples(_SCREAMING_SNAKE_CASE		) if evaluate else processor.get_train_examples(_SCREAMING_SNAKE_CASE		)
																									)

																									logger.info('Training examples: %s'     ,       len(_SCREAMING_SNAKE_CASE		)		)
																									_UpperCAmelCase											=     hans_convert_examples_to_features(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
																									logger.info('Saving features into cached file %s'     ,       _SCREAMING_SNAKE_CASE		)
																									torch.save(self.features     ,       _SCREAMING_SNAKE_CASE		)
				def __len__(     self		)					->			List[str]:




											"""simple docstring"""





											return len(self.features		)
				def __getitem__(     self     ,       _SCREAMING_SNAKE_CASE		)					->			InputFeatures:




											"""simple docstring"""





											return self.features[i]






				def       UpperCAmelCase__							(     self		)					->			Optional[int]:




											"""simple docstring"""





											return self.label_list


if is_tf_available():
		import tensorflow as tf

		class 						__a :
				_a      :    List[InputFeatures]
				def __init__(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = 128     ,       _SCREAMING_SNAKE_CASE=False     ,       _SCREAMING_SNAKE_CASE = False     ,       )					->			Tuple:




											"""simple docstring"""





											_UpperCAmelCase											=     hans_processors[task]()
											_UpperCAmelCase											=     processor.get_labels()
											if tokenizer.__class__ in (
											    RobertaTokenizer,
											    RobertaTokenizerFast,
											    XLMRobertaTokenizer,
											    BartTokenizer,
											    BartTokenizerFast,
											):
																		# HACK(label indices are swapped in RoBERTa pretrained model)
																		_UpperCAmelCase						,			_UpperCAmelCase											=     label_list[2], label_list[1]
											_UpperCAmelCase											=     label_list

											_UpperCAmelCase											=     processor.get_dev_examples(_SCREAMING_SNAKE_CASE		) if evaluate else processor.get_train_examples(_SCREAMING_SNAKE_CASE		)
											_UpperCAmelCase											=     hans_convert_examples_to_features(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)

											def gen():
																		for ex_index, ex in tqdm.tqdm(enumerate(self.features		)     ,       desc='convert examples to features'		):
																									if ex_index % 10000 == 0:
																																logger.info('Writing example %d of %d' % (ex_index, len(_SCREAMING_SNAKE_CASE		))		)

																									yield (
																									    {
																									        "example_id": 0,
																									        "input_ids": ex.input_ids,
																									        "attention_mask": ex.attention_mask,
																									        "token_type_ids": ex.token_type_ids,
																									    },
																									    ex.label,
																									)

											_UpperCAmelCase											=     tf.data.Dataset.from_generator(
											    _SCREAMING_SNAKE_CASE     ,       (
											        {
											            'example_id': tf.intaa,
											            'input_ids': tf.intaa,
											            'attention_mask': tf.intaa,
											            'token_type_ids': tf.intaa,
											        },
											        tf.intaa,
											    )     ,       (
											        {
											            'example_id': tf.TensorShape([]		),
											            'input_ids': tf.TensorShape([None, None]		),
											            'attention_mask': tf.TensorShape([None, None]		),
											            'token_type_ids': tf.TensorShape([None, None]		),
											        },
											        tf.TensorShape([]		),
											    )     ,       )
				def       UpperCAmelCase__							(     self		)					->			Dict:




											"""simple docstring"""





											return self.dataset
				def __len__(     self		)					->			List[Any]:




											"""simple docstring"""





											return len(self.features		)
				def __getitem__(     self     ,       _SCREAMING_SNAKE_CASE		)					->			InputFeatures:




											"""simple docstring"""





											return self.features[i]






				def       UpperCAmelCase__							(     self		)					->			List[Any]:




											"""simple docstring"""





											return self.label_list

class 						__a (							UpperCAmelCase     ):
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			List[Any]:




									"""simple docstring"""





									return self._create_examples(self._read_tsv(os.path.join(_SCREAMING_SNAKE_CASE     ,       'heuristics_train_set.txt'		)		)     ,       'train'		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			Union[str, Any]:




									"""simple docstring"""





									return self._create_examples(self._read_tsv(os.path.join(_SCREAMING_SNAKE_CASE     ,       'heuristics_evaluation_set.txt'		)		)     ,       'dev'		)
		def       UpperCAmelCase__							(     self		)					->			List[str]:




									"""simple docstring"""





									return ["contradiction", "entailment", "neutral"]






		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     []
									for i, line in enumerate(_SCREAMING_SNAKE_CASE		):
																if i == 0:
																							continue
																_UpperCAmelCase											=     '%s-%s' % (set_type, line[0])
																_UpperCAmelCase											=     line[5]
																_UpperCAmelCase											=     line[6]
																_UpperCAmelCase											=     line[7][2:] if line[7].startswith('ex'		) else line[7]
																_UpperCAmelCase											=     line[0]
																examples.append(InputExample(guid=_SCREAMING_SNAKE_CASE     ,       text_a=_SCREAMING_SNAKE_CASE     ,       text_b=_SCREAMING_SNAKE_CASE     ,       label=_SCREAMING_SNAKE_CASE     ,       pairID=_SCREAMING_SNAKE_CASE		)		)
									return examples



def 			lowerCAmelCase__					(     a__:						List[InputExample]     ,    a__:						List[str]     ,    a__:						int     ,    a__:						PreTrainedTokenizer     ,    )    ->						str:


							'''simple docstring'''





							_UpperCAmelCase											=     {label: i for i, label in enumerate(a__			)}

							_UpperCAmelCase											=     []
							for ex_index, example in tqdm.tqdm(enumerate(a__			)     ,    desc='convert examples to features'			):
														if ex_index % 1_0_0_0_0 == 0:
																					logger.info('Writing example %d' % (ex_index)			)

														_UpperCAmelCase											=     tokenizer(
														    example.text_a     ,    example.text_b     ,    add_special_tokens=a__     ,    max_length=a__     ,    padding='max_length'     ,    truncation=a__     ,    return_overflowing_tokens=a__     ,    )

														_UpperCAmelCase											=     label_map[example.label] if example.label in label_map else 0

														_UpperCAmelCase											=     int(example.pairID			)

														features.append(InputFeatures(**a__     ,    label=a__     ,    pairID=a__			)			)

							for i, example in enumerate(examples[:5]			):
														logger.info('*** Example ***'			)
														logger.info(F'''guid: {example}'''			)
														logger.info(F'''features: {features[i]}'''			)

							return features


lowerCAmelCase__    :Optional[Any]       =				{
    '''hans''': 3,
}







lowerCAmelCase__    :Optional[int]       =				{
    '''hans''': HansProcessor,
}
