






'''simple docstring'''
from __future__ import annotations





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




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			List[str]													=cipher_alphabet or [chr(snake_case_							) 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)
            SCREAMING_SNAKE_CASE__       :			str													={
                """a""": 0.0_8_4_9_7,
                """b""": 0.0_1_4_9_2,
                """c""": 0.0_2_2_0_2,
                """d""": 0.0_4_2_5_3,
                """e""": 0.1_1_1_6_2,
                """f""": 0.0_2_2_2_8,
                """g""": 0.0_2_0_1_5,
                """h""": 0.0_6_0_9_4,
                """i""": 0.0_7_5_4_6,
                """j""": 0.0_0_1_5_3,
                """k""": 0.0_1_2_9_2,
                """l""": 0.0_4_0_2_5,
                """m""": 0.0_2_4_0_6,
                """n""": 0.0_6_7_4_9,
                """o""": 0.0_7_5_0_7,
                """p""": 0.0_1_9_2_9,
                """q""": 0.0_0_0_9_5,
                """r""": 0.0_7_5_8_7,
                """s""": 0.0_6_3_2_7,
                """t""": 0.0_9_3_5_6,
                """u""": 0.0_2_7_5_8,
                """v""": 0.0_0_9_7_8,
                """w""": 0.0_2_5_6_0,
                """x""": 0.0_0_1_5_0,
                """y""": 0.0_1_9_9_4,
                """z""": 0.0_0_0_7_7,
            }
      else:
            # Custom frequencies dictionary
            SCREAMING_SNAKE_CASE__       :			Dict													=frequencies_dict

      if not case_sensitive:
            SCREAMING_SNAKE_CASE__       :			Tuple													=ciphertext.lower()

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

      # cycle through all of the shifts
      for shift in range(len(snake_case_							)							):
            SCREAMING_SNAKE_CASE__       :			Tuple													=""""""

            # decrypt the message with the shift
            for letter in ciphertext:
                  try:
                        # Try to index the letter in the alphabet
                        SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=(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

            SCREAMING_SNAKE_CASE__       :			Any													=0.0

            # Loop through each letter in the decoded message with the shift
            for letter in decrypted_with_shift:
                  if case_sensitive:
                        SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=letter.lower()
                        if letter in frequencies:
                              # Get the amount of times the letter occurs in the message
                              SCREAMING_SNAKE_CASE__       :			str													=decrypted_with_shift.lower().count(snake_case_							)

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

                              # Complete the chi squared statistic formula
                              SCREAMING_SNAKE_CASE__       :			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
                              SCREAMING_SNAKE_CASE__       :			Any													=decrypted_with_shift.count(snake_case_							)

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

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

      SCREAMING_SNAKE_CASE__       :			int													=min(
          snake_case_,		key=snake_case_,		)

      # Get all the data from the most likely cipher (key, decoded message)
      (
          SCREAMING_SNAKE_CASE__							
      )       :			Optional[int]													=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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_					=	{
    'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
    'tokenization_lxmert': ['LxmertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	['LxmertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	[
      'LxmertEncoder',
      'LxmertForPreTraining',
      'LxmertForQuestionAnswering',
      'LxmertModel',
      'LxmertPreTrainedModel',
      'LxmertVisualFeatureEncoder',
      'LxmertXLayer',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	[
      'TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFLxmertForPreTraining',
      'TFLxmertMainLayer',
      'TFLxmertModel',
      'TFLxmertPreTrainedModel',
      'TFLxmertVisualFeatureEncoder',
  ]


if TYPE_CHECKING:
  from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
  from .tokenization_lxmert import LxmertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_lxmert_fast import LxmertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_lxmert import (
        LxmertEncoder,
        LxmertForPreTraining,
        LxmertForQuestionAnswering,
        LxmertModel,
        LxmertPreTrainedModel,
        LxmertVisualFeatureEncoder,
        LxmertXLayer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_lxmert import (
        TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLxmertForPreTraining,
        TFLxmertMainLayer,
        TFLxmertModel,
        TFLxmertPreTrainedModel,
        TFLxmertVisualFeatureEncoder,
    )

else:
  import sys

  a_					=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)