



from __future__ import annotations






def      lowerCAmelCase__		(   lowerCamelCase_		:		str	,lowerCamelCase_		:		list[str] | None = None	,lowerCamelCase_		:		dict[str, float] | None = None	,lowerCamelCase_		:		bool = False	,):





				'''simple docstring'''

				lowerCAmelCase__					:    str												=					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)
								lowerCAmelCase__					:    str												=					{
								    '''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
								lowerCAmelCase__					:    Any												=					frequencies_dict

				if not case_sensitive:
								lowerCAmelCase__					:    Dict												=					ciphertext.lower()

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

				# cycle through all of the shifts
				for shift in range(len(lowerCamelCase_)):
								lowerCAmelCase__					:    List[Any]												=					''''''

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

								lowerCAmelCase__					:    Union[str, Any]												=					0.0

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

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

																				# Complete the chi squared statistic formula
																				lowerCAmelCase__					:    List[str]												=					((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__					:    int												=					decrypted_with_shift.count(lowerCamelCase_)

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

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

				lowerCAmelCase__					:    int												=					min(
				    lowerCamelCase_	,key=lowerCamelCase_	,)

				# Get all the data from the most likely cipher (key, decoded message)
				(
				    lowerCAmelCase__		
				)					:    List[Any]												=					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 fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save






def      lowerCAmelCase__		(   lowerCamelCase_		:		Dict	,lowerCamelCase_		:		Optional[int]	,lowerCamelCase_		:		List[Any]=1024	,lowerCamelCase_		:		int=1024	,lowerCamelCase_		:		Dict=False	,**lowerCamelCase_		:		Tuple):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[int]												=					AutoTokenizer.from_pretrained(lowerCamelCase_)
				lowerCAmelCase__					:    Optional[Any]												=					SeqaSeqDataset(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,type_path='''train'''	,**lowerCamelCase_)
				lowerCAmelCase__					:    int												=					tok.pad_token_id

				def get_lens(lowerCamelCase_		:		Tuple):
								lowerCAmelCase__					:    Tuple												=					tqdm(
								    DataLoader(lowerCamelCase_	,batch_size=512	,num_workers=8	,shuffle=lowerCamelCase_	,collate_fn=ds.collate_fn)	,desc=str(ds.len_file)	,)
								lowerCAmelCase__					:    Tuple												=					[]
								for batch in dl:
												lowerCAmelCase__					:    Dict												=					batch['''input_ids'''].ne(lowerCamelCase_).sum(1).tolist()
												lowerCAmelCase__					:    Dict												=					batch['''labels'''].ne(lowerCamelCase_).sum(1).tolist()
												if consider_target:
																for src, tgt in zip(lowerCamelCase_	,lowerCamelCase_):
																				max_lens.append(max(lowerCamelCase_	,lowerCamelCase_))
												else:
																max_lens.extend(lowerCamelCase_)
								return max_lens

				lowerCAmelCase__					:    str												=					get_lens(lowerCamelCase_)
				lowerCAmelCase__					:    Tuple												=					SeqaSeqDataset(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,type_path='''val'''	,**lowerCamelCase_)
				lowerCAmelCase__					:    Optional[int]												=					get_lens(lowerCamelCase_)
				pickle_save(lowerCamelCase_	,train_ds.len_file)
				pickle_save(lowerCamelCase_	,val_ds.len_file)


if __name__ == "__main__":
							fire.Fire(save_len_file)

