
'''simple docstring'''



from __future__ import annotations


def A (__lowerCamelCase :str					,      __lowerCamelCase :list[str] | None = None					,      __lowerCamelCase :dict[str, float] | None = None					,      __lowerCamelCase :bool = False					,      ):
						_lowerCAmelCase    		=					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    		=					{
												    """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
												_lowerCAmelCase    		=					frequencies_dict

						if not case_sensitive:
												_lowerCAmelCase    		=					ciphertext.lower()

						# Chi squared statistic values
						_lowerCAmelCase    		=					{}

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

												# decrypt the message with the shift
												for letter in ciphertext:
																		try:
																								# Try to index the letter in the alphabet
																								_lowerCAmelCase    		=					(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    		=					0.0

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

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

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

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

																														# Complete the chi squared statistic formula
																														_lowerCAmelCase    		=					((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    		=					(
												    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    		=					min(
						    __lowerCamelCase					,      key=__lowerCamelCase					,      )

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


'''simple docstring'''



from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import (
						    ImageTextPipelineOutput,
						    UniDiffuserPipeline,
						)
else:
						from .modeling_text_decoder import UniDiffuserTextDecoder
						from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
						from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
