





from __future__ import annotations



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







      '''simple docstring'''






      __A          =				cipher_alphabet or [chr(lowerCamelCase ) for i in range(97					,				1_23 )]

      # 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)
            __A          =				{
                '''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
            __A          =				frequencies_dict

      if not case_sensitive:
            __A          =				ciphertext.lower()

      # Chi squared statistic values
      __A          =				{}

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

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

            __A          =				0.0

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

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

                              # Complete the chi squared statistic formula
                              __A          =				((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
                              __A          =				decrypted_with_shift.count(lowerCamelCase )

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

                              # Complete the chi squared statistic formula
                              __A          =				((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
            __A          =				(
                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]

      __A          =				min(
          lowerCamelCase					,				key=lowerCamelCase					,				)

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












# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


snake_case__					:							List[str]													=     {
    'configuration_vivit': ['VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VivitConfig'],
}
try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__					:							Tuple													=     ['VivitImageProcessor']


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__					:							Any													=     [
           'VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'VivitModel',
           'VivitPreTrainedModel',
           'VivitForVideoClassification',
       ]


if TYPE_CHECKING:
       from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_vivit import VivitImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vivit import (
                  VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VivitForVideoClassification,
                  VivitModel,
                  VivitPreTrainedModel,
              )


else:
       import sys

       snake_case__					:							List[str]													=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





