



"""simple docstring"""





from __future__ import annotations
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__ = None     ,      lowerCAmelCase__ = None     ,      lowerCAmelCase__ = False     ,      ):
      UpperCAmelCase_      			=							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)
            UpperCAmelCase_      			=							{
                "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
            UpperCAmelCase_      			=							frequencies_dict

      if not case_sensitive:
            UpperCAmelCase_      			=							ciphertext.lower()

      # Chi squared statistic values
      UpperCAmelCase_      			=							{}

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

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

            UpperCAmelCase_      			=							0.0

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

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

                              # Complete the chi squared statistic formula
                              UpperCAmelCase_      			=							((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
                              UpperCAmelCase_      			=							decrypted_with_shift.count(lowerCAmelCase__				)

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

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

      UpperCAmelCase_      			=							min(
          lowerCAmelCase__     ,      key=lowerCAmelCase__     ,      )

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

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					['''image_processor''', '''tokenizer''']
       UpperCamelCase						=					'''ViTImageProcessor'''
       UpperCamelCase						=					('''CLIPTokenizer''', '''CLIPTokenizerFast''')



       def __init__(							self      :  List[Any]		,    _UpperCAmelCase      :  str=None		,    _UpperCAmelCase      :  Optional[int]=None		,    **_UpperCAmelCase      :  List[str]   )			->						Any:


             '''simple docstring'''




             UpperCAmelCase_      			=							None
             if "feature_extractor" in kwargs:
                   warnings.warn(
                       "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                       " instead."		,    _UpperCAmelCase		,    )
                   UpperCAmelCase_      			=							kwargs.pop("feature_extractor"   )

             UpperCAmelCase_      			=							image_processor if image_processor is not None else feature_extractor
             if image_processor is None:
                   raise ValueError("You need to specify an `image_processor`."   )
             if tokenizer is None:
                   raise ValueError("You need to specify a `tokenizer`."   )

             super().__init__(_UpperCAmelCase		,    _UpperCAmelCase   )



       def __call__(							self      :  Optional[Any]		,    _UpperCAmelCase      :  Any=None		,    _UpperCAmelCase      :  Optional[int]=None		,    _UpperCAmelCase      :  str=None		,    _UpperCAmelCase      :  List[Any]=None		,    **_UpperCAmelCase      :  str   )			->						Optional[int]:


             '''simple docstring'''




             if text is None and visual_prompt is None and images is None:
                   raise ValueError("You have to specify either text, visual prompt or images."   )

             if text is not None and visual_prompt is not None:
                   raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt."   )

             if text is not None:
                   UpperCAmelCase_      			=							self.tokenizer(_UpperCAmelCase		,    return_tensors=_UpperCAmelCase		,    **_UpperCAmelCase   )

             if visual_prompt is not None:
                   UpperCAmelCase_      			=							self.image_processor(_UpperCAmelCase		,    return_tensors=_UpperCAmelCase		,    **_UpperCAmelCase   )

             if images is not None:
                   UpperCAmelCase_      			=							self.image_processor(_UpperCAmelCase		,    return_tensors=_UpperCAmelCase		,    **_UpperCAmelCase   )

             if visual_prompt is not None and images is not None:
                   UpperCAmelCase_      			=							{
                       "pixel_values": image_features.pixel_values,
                       "conditional_pixel_values": prompt_features.pixel_values,
                   }
                   return encoding
             elif text is not None and images is not None:
                   UpperCAmelCase_      			=							image_features.pixel_values
                   return encoding
             elif text is not None:
                   return encoding
             elif visual_prompt is not None:
                   UpperCAmelCase_      			=							{
                       "conditional_pixel_values": prompt_features.pixel_values,
                   }
                   return encoding
             else:
                   return BatchEncoding(data=dict(**_UpperCAmelCase   )		,    tensor_type=_UpperCAmelCase   )



       def 					lowercase__    (							self      :  List[Any]		,    *_UpperCAmelCase      :  List[str]		,    **_UpperCAmelCase      :  List[Any]   )			->						Any:


             '''simple docstring'''




             return self.tokenizer.batch_decode(*_UpperCAmelCase		,    **_UpperCAmelCase   )



       def 					lowercase__    (							self      :  Dict		,    *_UpperCAmelCase      :  Tuple		,    **_UpperCAmelCase      :  List[Any]   )			->						Union[str, Any]:


             '''simple docstring'''




             return self.tokenizer.decode(*_UpperCAmelCase		,    **_UpperCAmelCase   )



       @property
       def 					lowercase__    (							self      :  int   )			->						Optional[int]:


             '''simple docstring'''




             warnings.warn(
                 "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,    _UpperCAmelCase		,    )
             return self.image_processor_class







       @property
       def 					lowercase__    (							self      :  str   )			->						List[Any]:


             '''simple docstring'''




             warnings.warn(
                 "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,    _UpperCAmelCase		,    )
             return self.image_processor




