




"""simple docstring"""


import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					["image_processor", "tokenizer"]
 lowercase__ 					=					"LayoutLMv2ImageProcessor"
 lowercase__ 					=					("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")


 def __init__(				self:		Optional[int],      a_:		Union[str, Any]=None,      a_:		Union[str, Any]=None,      **a_:		str		):




   '''simple docstring'''



   if "feature_extractor" in kwargs:
     warnings.warn(
         """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
         """ instead.""",      a_,      )
     _snake_case							:		List[Any]								=  kwargs.pop("""feature_extractor"""		)

   _snake_case							:		Tuple								=  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__(a_,      a_		)


 def __call__(				self:		List[Any],      a_:		Union[str, Any],      a_:		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,      a_:		Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,      a_:		Union[List[List[int]], List[List[List[int]]]] = None,      a_:		Optional[Union[List[int], List[List[int]]]] = None,      a_:		bool = True,      a_:		Union[bool, str, PaddingStrategy] = False,      a_:		Union[bool, str, TruncationStrategy] = None,      a_:		Optional[int] = None,      a_:		int = 0,      a_:		Optional[int] = None,      a_:		Optional[bool] = None,      a_:		Optional[bool] = None,      a_:		bool = False,      a_:		bool = False,      a_:		bool = False,      a_:		bool = False,      a_:		bool = True,      a_:		Optional[Union[str, TensorType]] = None,      **a_:		List[Any],      ):




   '''simple docstring'''



   if self.image_processor.apply_ocr and (boxes is not None):
     raise ValueError(
         """You cannot provide bounding boxes """
         """if you initialized the image processor with apply_ocr set to True."""		)

   if self.image_processor.apply_ocr and (word_labels is not None):
     raise ValueError(
         """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""		)

   if return_overflowing_tokens is True and return_offsets_mapping is False:
     raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""		)

   # first, apply the image processor
   _snake_case							:		List[Any]								=  self.image_processor(images=a_,      return_tensors=a_		)

   # second, apply the tokenizer
   if text is not None and self.image_processor.apply_ocr and text_pair is None:
     if isinstance(a_,      a_		):
       _snake_case							:		Union[str, Any]								=  [text]  # add batch dimension (as the image processor always adds a batch dimension)
     _snake_case							:		Union[str, Any]								=  features["""words"""]

   _snake_case							:		List[Any]								=  self.tokenizer(
       text=text if text is not None else features["""words"""],      text_pair=text_pair if text_pair is not None else None,      boxes=boxes if boxes is not None else features["""boxes"""],      word_labels=a_,      add_special_tokens=a_,      padding=a_,      truncation=a_,      max_length=a_,      stride=a_,      pad_to_multiple_of=a_,      return_token_type_ids=a_,      return_attention_mask=a_,      return_overflowing_tokens=a_,      return_special_tokens_mask=a_,      return_offsets_mapping=a_,      return_length=a_,      verbose=a_,      return_tensors=a_,      **a_,      )

   # add pixel values
   _snake_case							:		int								=  features.pop("""pixel_values"""		)
   if return_overflowing_tokens is True:
     _snake_case							:		int								=  self.get_overflowing_images(a_,      encoded_inputs["""overflow_to_sample_mapping"""]		)
   _snake_case							:		Optional[int]								=  images

   return encoded_inputs


 def  UpperCamelCase_   (				self:		List[Any],      a_:		List[Any],      a_:		Union[str, Any]		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  []
   for sample_idx in overflow_to_sample_mapping:
     images_with_overflow.append(images[sample_idx]		)

   if len(a_		) != len(a_		):
     raise ValueError(
         """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
         f" {len(a_		)} and {len(a_		)}"		)

   return images_with_overflow


 def  UpperCamelCase_   (				self:		Union[str, Any],      *a_:		List[str],      **a_:		Dict		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		List[str],      *a_:		Tuple,      **a_:		Dict		):




   '''simple docstring'''



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


 @property
 def  UpperCamelCase_   (				self:		List[str]		):




   '''simple docstring'''



   return ["input_ids", "bbox", "attention_mask", "image"]


 @property
 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



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


 @property
 def  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



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






from __future__ import annotations
def 				__lowerCamelCase						( UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		list[str] | None = None ,  UpperCAmelCase_						:		dict[str, float] | None = None ,  UpperCAmelCase_						:		bool = False ,  ):




      """simple docstring"""





      a						:str 							=       cipher_alphabet or [chr(UpperCAmelCase_						) 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)
            a						:List[Any] 							=       {
                '''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
            a						:Dict 							=       frequencies_dict

      if not case_sensitive:
            a						:Union[str, Any] 							=       ciphertext.lower()

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

      # cycle through all of the shifts
      for shift in range(len(UpperCAmelCase_						)						):
            a						:int 							=       ''''''

            # decrypt the message with the shift
            for letter in ciphertext:
                  try:
                        # Try to index the letter in the alphabet
                        a						:Dict 							=       (alphabet_letters.index(letter.lower()						) - shift) % len(
                            UpperCAmelCase_						)
                        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						:List[Any] 							=       0.0

            # Loop through each letter in the decoded message with the shift
            for letter in decrypted_with_shift:
                  if case_sensitive:
                        a						:Optional[int] 							=       letter.lower()
                        if letter in frequencies:
                              # Get the amount of times the letter occurs in the message
                              a						:List[Any] 							=       decrypted_with_shift.lower().count(UpperCAmelCase_						)

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

                              # Complete the chi squared statistic formula
                              a						:Any 							=       ((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						:int 							=       decrypted_with_shift.count(UpperCAmelCase_						)

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

                              # Complete the chi squared statistic formula
                              a						:Optional[Any] 							=       ((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						:Optional[Any] 							=       (
                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]

      a						:int 							=       min(
          UpperCAmelCase_ ,  key=UpperCAmelCase_ ,  )

      # Get all the data from the most likely cipher (key, decoded message)
      (
          (
          a
      )							,				(
          a
      )							,				
      )						: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,
      )





