



'''simple docstring'''





from __future__ import annotations




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





    '''simple docstring'''


    lowercase__		       =						cipher_alphabet or [chr(_snake_case       ) 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": 0.08_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
        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(_snake_case       )       ):
        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(
                    _snake_case       )
                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(_snake_case       )

                    # 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(_snake_case       )

                    # 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(_snake_case  :						int       ) -> tuple[float, str]:
        return chi_squared_statistic_values[key]

    lowercase__		       =						min(
        _snake_case     ,key=_snake_case     ,)

    # 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,
    )









'''simple docstring'''





import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
 pass

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      					=     {
    "kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}


class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Optional[int]						   =					"align_text_model"



       def __init__(				self     ,UpperCAmelCase_=30_522     ,UpperCAmelCase_=768     ,UpperCAmelCase_=12     ,UpperCAmelCase_=12     ,UpperCAmelCase_=3_072     ,UpperCAmelCase_="gelu"     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=512     ,UpperCAmelCase_=2     ,UpperCAmelCase_=0.02     ,UpperCAmelCase_=1E-1_2     ,UpperCAmelCase_=0     ,UpperCAmelCase_="absolute"     ,UpperCAmelCase_=True     ,**UpperCAmelCase_     ,)  ->							List[str]:
           super().__init__(**UpperCAmelCase_      )

           lowercase__		       =						vocab_size
           lowercase__		       =						hidden_size
           lowercase__		       =						num_hidden_layers
           lowercase__		       =						num_attention_heads
           lowercase__		       =						hidden_act
           lowercase__		       =						intermediate_size
           lowercase__		       =						hidden_dropout_prob
           lowercase__		       =						attention_probs_dropout_prob
           lowercase__		       =						max_position_embeddings
           lowercase__		       =						type_vocab_size
           lowercase__		       =						initializer_range
           lowercase__		       =						layer_norm_eps
           lowercase__		       =						position_embedding_type
           lowercase__		       =						use_cache
           lowercase__		       =						pad_token_id







       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							"PretrainedConfig":
           cls._set_token_in_kwargs(UpperCAmelCase_      )

           lowercase__       ,       lowercase__		       =						cls.get_config_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )

           # get the text config dict if we are loading from AlignConfig
           if config_dict.get("model_type"      ) == "align":
               lowercase__		       =						config_dict["text_config"]

           if "model_type" in config_dict and hasattr(cls     ,"model_type"      ) and config_dict["model_type"] != cls.model_type:
               logger.warning(
                   F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
                   F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''      )

           return cls.from_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )





class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Tuple						   =					"align_vision_model"



       def __init__(				self     ,UpperCAmelCase_ = 3     ,UpperCAmelCase_ = 600     ,UpperCAmelCase_ = 2.0     ,UpperCAmelCase_ = 3.1     ,UpperCAmelCase_ = 8     ,UpperCAmelCase_ = [3, 3, 5, 3, 5, 5, 3]     ,UpperCAmelCase_ = [32, 16, 24, 40, 80, 112, 192]     ,UpperCAmelCase_ = [16, 24, 40, 80, 112, 192, 320]     ,UpperCAmelCase_ = []     ,UpperCAmelCase_ = [1, 2, 2, 2, 1, 2, 1]     ,UpperCAmelCase_ = [1, 2, 2, 3, 3, 4, 1]     ,UpperCAmelCase_ = [1, 6, 6, 6, 6, 6, 6]     ,UpperCAmelCase_ = 0.25     ,UpperCAmelCase_ = "swish"     ,UpperCAmelCase_ = 2_560     ,UpperCAmelCase_ = "mean"     ,UpperCAmelCase_ = 0.02     ,UpperCAmelCase_ = 0.0_01     ,UpperCAmelCase_ = 0.99     ,UpperCAmelCase_ = 0.2     ,**UpperCAmelCase_     ,)  ->							Union[str, Any]:
           super().__init__(**UpperCAmelCase_      )

           lowercase__		       =						num_channels
           lowercase__		       =						image_size
           lowercase__		       =						width_coefficient
           lowercase__		       =						depth_coefficient
           lowercase__		       =						depth_divisor
           lowercase__		       =						kernel_sizes
           lowercase__		       =						in_channels
           lowercase__		       =						out_channels
           lowercase__		       =						depthwise_padding
           lowercase__		       =						strides
           lowercase__		       =						num_block_repeats
           lowercase__		       =						expand_ratios
           lowercase__		       =						squeeze_expansion_ratio
           lowercase__		       =						hidden_act
           lowercase__		       =						hidden_dim
           lowercase__		       =						pooling_type
           lowercase__		       =						initializer_range
           lowercase__		       =						batch_norm_eps
           lowercase__		       =						batch_norm_momentum
           lowercase__		       =						drop_connect_rate
           lowercase__		       =						sum(UpperCAmelCase_      ) * 4







       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							"PretrainedConfig":
           cls._set_token_in_kwargs(UpperCAmelCase_      )

           lowercase__       ,       lowercase__		       =						cls.get_config_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )

           # get the vision config dict if we are loading from AlignConfig
           if config_dict.get("model_type"      ) == "align":
               lowercase__		       =						config_dict["vision_config"]

           if "model_type" in config_dict and hasattr(cls     ,"model_type"      ) and config_dict["model_type"] != cls.model_type:
               logger.warning(
                   F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
                   F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''      )

           return cls.from_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )





class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Union[str, Any]						   =					"align"
       lowerCAmelCase__  :Tuple						   =					True



       def __init__(				self     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,UpperCAmelCase_=640     ,UpperCAmelCase_=1.0     ,UpperCAmelCase_=0.02     ,**UpperCAmelCase_     ,)  ->							Union[str, Any]:
           super().__init__(**UpperCAmelCase_      )

           if text_config is None:
               lowercase__		       =						{}
               logger.info("text_config is None. Initializing the AlignTextConfig with default values."      )

           if vision_config is None:
               lowercase__		       =						{}
               logger.info("vision_config is None. Initializing the AlignVisionConfig with default values."      )

           lowercase__		       =						AlignTextConfig(**UpperCAmelCase_      )
           lowercase__		       =						AlignVisionConfig(**UpperCAmelCase_      )

           lowercase__		       =						projection_dim
           lowercase__		       =						temperature_init_value
           lowercase__		       =						initializer_range



       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							List[str]:
           return cls(text_config=text_config.to_dict()     ,vision_config=vision_config.to_dict()     ,**UpperCAmelCase_      )







       def 							_a      (				self      )  ->							List[Any]:
           lowercase__		       =						copy.deepcopy(self.__dict__      )
           lowercase__		       =						self.text_config.to_dict()
           lowercase__		       =						self.vision_config.to_dict()
           lowercase__		       =						self.__class__.model_type
           return output




