



'''simple docstring'''


from __future__ import annotations
def 							UpperCAmelCase_  (						__lowerCamelCase : str     ,__lowerCamelCase : list[str] | None = None     ,__lowerCamelCase : dict[str, float] | None = None     ,__lowerCamelCase : bool = False     ,):
   lowercase_      :List[Any]         =			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)
      lowercase_      :int         =			{
          '''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_      :Tuple         =			frequencies_dict

   if not case_sensitive:
      lowercase_      :int         =			ciphertext.lower()

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

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

      # decrypt the message with the shift
      for letter in ciphertext:
         try:
            # Try to index the letter in the alphabet
            lowercase_      :List[Any]         =			(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

      lowercase_      :Dict         =			0.0

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

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

               # Complete the chi squared statistic formula
               lowercase_      :Union[str, 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
               lowercase_      :Dict         =			decrypted_with_shift.count(__lowerCamelCase			)

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

               # Complete the chi squared statistic formula
               lowercase_      :List[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
      lowercase_      :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(__lowerCamelCase : int			) -> tuple[float, str]:
      return chi_squared_statistic_values[key]

   lowercase_      :int         =			min(
       __lowerCamelCase     ,key=__lowerCamelCase     ,)

   # Get all the data from the most likely cipher (key, decoded message)
   (
       lowercase_						
   )      :List[Any]         =			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 Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_torch_available():
 import torch

 from transformers.modeling_outputs import BaseModelOutput

 from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

lowerCAmelCase :					Union[str, Any] 							=logging.get_logger(__name__)
@add_end_docstrings(_lowerCAmelCase							)
class 				a_       (    _lowerCAmelCase							):





     def __init__(			self		: List[Any]     ,				**lowercase		: Optional[int]						):





        """simple docstring"""




        super().__init__(**lowercase						)

        if self.framework == "tf":
           raise ValueError(F'The {self.__class__} is only available in PyTorch.'						)

        requires_backends(self     ,				"vision"						)
        self.check_model_type(lowercase						)





     def __call__(			self		: Tuple     ,				lowercase		: Union[str, "Image.Image", List[Dict[str, Any]]]     ,				lowercase		: Union[str, List[str]] = None     ,				**lowercase		: str     ,				):





        """simple docstring"""




        if "text_queries" in kwargs:
           lowercase_      :List[Any]         =			kwargs.pop("text_queries"						)

        if isinstance(lowercase     ,				(str, Image.Image)						):
           lowercase_      :List[str]         =			{"image": image, "candidate_labels": candidate_labels}
        else:
           lowercase_      :Optional[Any]         =			image
        lowercase_      :str         =			super().__call__(lowercase     ,				**lowercase						)
        return results





     def 							lowercase__				(			self		: Optional[int]     ,				**lowercase		: List[str]						):





        """simple docstring"""




        lowercase_      :Tuple         =			{}
        if "threshold" in kwargs:
           lowercase_      :Dict         =			kwargs["threshold"]
        if "top_k" in kwargs:
           lowercase_      :Optional[Any]         =			kwargs["top_k"]
        return {}, {}, postprocess_params





     def 							lowercase__				(			self		: List[Any]     ,				lowercase		: Dict						):





        """simple docstring"""




        lowercase_      :Any         =			load_image(inputs["image"]						)
        lowercase_      :List[str]         =			inputs["candidate_labels"]
        if isinstance(lowercase     ,				lowercase						):
           lowercase_      :Union[str, Any]         =			candidate_labels.split(","						)

        lowercase_      :Tuple         =			torch.tensor([[image.height, image.width]]     ,				dtype=torch.intaa						)
        for i, candidate_label in enumerate(lowercase						):
           lowercase_      :Union[str, Any]         =			self.tokenizer(lowercase     ,				return_tensors=self.framework						)
           lowercase_      :Tuple         =			self.image_processor(lowercase     ,				return_tensors=self.framework						)
           yield {
               "is_last": i == len(lowercase						) - 1,
               "target_size": target_size,
               "candidate_label": candidate_label,
               **text_inputs,
               **image_features,
           }





     def 							lowercase__				(			self		: List[str]     ,				lowercase		: List[str]						):





        """simple docstring"""




        lowercase_      :Dict         =			model_inputs.pop("target_size"						)
        lowercase_      :str         =			model_inputs.pop("candidate_label"						)
        lowercase_      :List[Any]         =			model_inputs.pop("is_last"						)

        lowercase_      :Optional[Any]         =			self.model(**lowercase						)

        lowercase_      :str         =			{"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
        return model_outputs





     def 							lowercase__				(			self		: Optional[int]     ,				lowercase		: List[str]     ,				lowercase		: List[str]=0.1     ,				lowercase		: Optional[int]=None						):





        """simple docstring"""




        lowercase_      :Dict         =			[]
        for model_output in model_outputs:
           lowercase_      :int         =			model_output["candidate_label"]
           lowercase_      :str         =			BaseModelOutput(lowercase						)
           lowercase_      :List[Any]         =			self.image_processor.post_process_object_detection(
               outputs=lowercase     ,				threshold=lowercase     ,				target_sizes=model_output["target_size"]						)[0]

           for index in outputs["scores"].nonzero():
              lowercase_      :Optional[int]         =			outputs["scores"][index].item()
              lowercase_      :int         =			self._get_bounding_box(outputs["boxes"][index][0]						)

              lowercase_      :int         =			{"score": score, "label": label, "box": box}
              results.append(lowercase						)

        lowercase_      :Dict         =			sorted(lowercase     ,				key=lambda lowercase						: x["score"]     ,				reverse=lowercase						)
        if top_k:
           lowercase_      :List[str]         =			results[:top_k]

        return results







     def 							lowercase__				(			self		: Union[str, Any]     ,				lowercase		: "torch.Tensor"						):





        """simple docstring"""




        if self.framework != "pt":
           raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch."						)
        lowercase_      ,							lowercase_      ,							lowercase_      ,							lowercase_      :List[str]         =			box.int().tolist()
        lowercase_      :List[Any]         =			{
            "xmin": xmin,
            "ymin": ymin,
            "xmax": xmax,
            "ymax": ymax,
        }
        return bbox


