


"""simple docstring"""







import string

import numpy


def      lowercase			(				A_  ,   A_       )->      int:
  '''simple docstring'''







  return b if a == 0 else greatest_common_divisor(b % a  ,   A_       )






class 						_A  :







    """simple docstring"""




    UpperCAmelCase       :							List[Any]	=       string.ascii_uppercase + string.digits
    # This cipher takes alphanumerics into account
    # i.e. a total of 36 characters

    # take x and return x % len(key_string)
    UpperCAmelCase       :							Union[str, Any]	=       numpy.vectorize(lambda _a  : x % 3_6  )

    UpperCAmelCase       :							List[Any]	=       numpy.vectorize(_a  )






    def __init__(					self				:    Tuple				, __UpperCAmelCase				:    numpy.ndarray):
      a						:		Any      							=			self.modulus(__UpperCAmelCase)  # mod36 calc's on the encrypt key
      self.check_determinant()  # validate the determinant of the encryption key
      a						:		Union[str, Any]      							=			encrypt_key.shape[0]






    def 	__snake_case							(					self				:    int				, __UpperCAmelCase				:    str):
      return self.key_string.index(__UpperCAmelCase)






    def 	__snake_case							(					self				:    Dict				, __UpperCAmelCase				:    int):
      return self.key_string[round(__UpperCAmelCase)]






    def 	__snake_case							(					self				:    int):
      a						:		List[str]      							=			round(numpy.linalg.det(self.encrypt_key))

      if det < 0:
        a						:		Dict      							=			det % len(self.key_string)

      a						:		List[Any]      							=			len(self.key_string)
      if greatest_common_divisor(__UpperCAmelCase				, len(self.key_string)) != 1:
        a						:		Optional[int]      							=			(
            f'''determinant modular {req_l} of encryption key({det}) '''
            f'''is not co prime w.r.t {req_l}.\nTry another key.'''
        )
        raise ValueError(__UpperCAmelCase)






    def 	__snake_case							(					self				:    Tuple				, __UpperCAmelCase				:    str):
      a						:		Optional[int]      							=			[char for char in text.upper() if char in self.key_string]

      a						:		Any      							=			chars[-1]
      while len(__UpperCAmelCase) % self.break_key != 0:
        chars.append(__UpperCAmelCase)

      return "".join(__UpperCAmelCase)






    def 	__snake_case							(					self				:    str				, __UpperCAmelCase				:    str):
      a						:		str      							=			self.process_text(text.upper())
      a						:		Union[str, Any]      							=			""

      for i in range(0				, len(__UpperCAmelCase) - self.break_key + 1				, self.break_key):
        a						:		List[str]      							=			text[i : i + self.break_key]
        a						:		Dict      							=			[self.replace_letters(__UpperCAmelCase) for char in batch]
        a						:		str      							=			numpy.array([vec]).T
        a						:		int      							=			self.modulus(self.encrypt_key.dot(__UpperCAmelCase)).T.tolist()[
            0
        ]
        a						:		Optional[Any]      							=			"".join(
            self.replace_digits(__UpperCAmelCase) for num in batch_encrypted)
        encrypted += encrypted_batch

      return encrypted






    def 	__snake_case							(					self				:    Any):
      a						:		Dict      							=			round(numpy.linalg.det(self.encrypt_key))

      if det < 0:
        a						:		int      							=			det % len(self.key_string)
      a						:		Any      							=			None
      for i in range(len(self.key_string)):
        if (det * i) % len(self.key_string) == 1:
          a						:		int      							=			i
          break

      a						:		Union[str, Any]      							=			(
          det_inv
          * numpy.linalg.det(self.encrypt_key)
          * numpy.linalg.inv(self.encrypt_key)
      )

      return self.to_int(self.modulus(__UpperCAmelCase))






    def 	__snake_case							(					self				:    Any				, __UpperCAmelCase				:    str):
      a						:		Dict      							=			self.make_decrypt_key()
      a						:		Optional[Any]      							=			self.process_text(text.upper())
      a						:		Optional[Any]      							=			""

      for i in range(0				, len(__UpperCAmelCase) - self.break_key + 1				, self.break_key):
        a						:		Optional[Any]      							=			text[i : i + self.break_key]
        a						:		List[Any]      							=			[self.replace_letters(__UpperCAmelCase) for char in batch]
        a						:		Dict      							=			numpy.array([vec]).T
        a						:		Union[str, Any]      							=			self.modulus(decrypt_key.dot(__UpperCAmelCase)).T.tolist()[0]
        a						:		str      							=			"".join(
            self.replace_digits(__UpperCAmelCase) for num in batch_decrypted)
        decrypted += decrypted_batch

      return decrypted






def      lowercase			(				)->      None:
  '''simple docstring'''







  a						:		List[str]      							=			int(input("Enter the order of the encryption key: "       )       )
  a						:		str      							=			[]

  print("Enter each row of the encryption key with space separated integers"       )
  for _ in range(A_       ):
    a						:		Optional[int]      							=			[int(A_       ) for x in input().split()]
    hill_matrix.append(A_       )

  a						:		List[str]      							=			HillCipher(numpy.array(A_       )       )

  print("Would you like to encrypt or decrypt some text? (1 or 2)"       )
  a						:		List[str]      							=			input("\n1. Encrypt\n2. Decrypt\n"       )
  if option == "1":
    a						:		List[Any]      							=			input("What text would you like to encrypt?: "       )
    print("Your encrypted text is:"       )
    print(hc.encrypt(A_       )       )
  elif option == "2":
    a						:		List[str]      							=			input("What text would you like to decrypt?: "       )
    print("Your decrypted text is:"       )
    print(hc.decrypt(A_       )       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 main()













"""simple docstring"""





from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
  import torch

  from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

__A											=		logging.get_logger(__name__)






@add_end_docstrings(a       )
class 					_lowerCAmelCase   (     a       ):
       """simple docstring"""




       def __init__(    self    , **__UpperCAmelCase      ):



         '''simple docstring'''

         super().__init__(**__UpperCAmelCase      )
         requires_backends(self    , 'vision'      )
         requires_backends(self    , 'torch'      )

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

         self.check_model_type(__UpperCAmelCase      )






       def      snake_case						(    self    , **__UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[str]											=							{}
         lowerCAmelCase__			:Tuple											=							{}
         lowerCAmelCase__			:Any											=							{}
         # preprocess args
         if "points_per_batch" in kwargs:
           lowerCAmelCase__			:Dict											=							kwargs['points_per_batch']
         if "points_per_crop" in kwargs:
           lowerCAmelCase__			:Union[str, Any]											=							kwargs['points_per_crop']
         if "crops_n_layers" in kwargs:
           lowerCAmelCase__			:Any											=							kwargs['crops_n_layers']
         if "crop_overlap_ratio" in kwargs:
           lowerCAmelCase__			:Any											=							kwargs['crop_overlap_ratio']
         if "crop_n_points_downscale_factor" in kwargs:
           lowerCAmelCase__			:Dict											=							kwargs['crop_n_points_downscale_factor']
         # postprocess args
         if "pred_iou_thresh" in kwargs:
           lowerCAmelCase__			:Tuple											=							kwargs['pred_iou_thresh']
         if "stability_score_offset" in kwargs:
           lowerCAmelCase__			:Optional[int]											=							kwargs['stability_score_offset']
         if "mask_threshold" in kwargs:
           lowerCAmelCase__			:List[Any]											=							kwargs['mask_threshold']
         if "stability_score_thresh" in kwargs:
           lowerCAmelCase__			:Optional[Any]											=							kwargs['stability_score_thresh']
         if "crops_nms_thresh" in kwargs:
           lowerCAmelCase__			:int											=							kwargs['crops_nms_thresh']
         if "output_rle_mask" in kwargs:
           lowerCAmelCase__			:Union[str, Any]											=							kwargs['output_rle_mask']
         if "output_bboxes_mask" in kwargs:
           lowerCAmelCase__			:Optional[Any]											=							kwargs['output_bboxes_mask']
         return preprocess_kwargs, forward_params, postprocess_kwargs






       def __call__(    self    , __UpperCAmelCase    , *__UpperCAmelCase    , __UpperCAmelCase=None    , __UpperCAmelCase=None    , **__UpperCAmelCase      ):



         '''simple docstring'''

         return super().__call__(__UpperCAmelCase    , *__UpperCAmelCase    , num_workers=__UpperCAmelCase    , batch_size=__UpperCAmelCase    , **__UpperCAmelCase      )






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase=6_4    , __UpperCAmelCase = 0    , __UpperCAmelCase = 5_1_2 / 1_5_0_0    , __UpperCAmelCase = 3_2    , __UpperCAmelCase = 1    , ):



         '''simple docstring'''

         lowerCAmelCase__			:Union[str, Any]											=							load_image(__UpperCAmelCase      )
         lowerCAmelCase__			:int											=							self.image_processor.size['longest_edge']
         lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:int											=							self.image_processor.generate_crop_boxes(
             __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      )
         lowerCAmelCase__			:Union[str, Any]											=							self.image_processor(images=__UpperCAmelCase    , return_tensors='pt'      )

         with self.device_placement():
           if self.framework == "pt":
             lowerCAmelCase__			:Optional[int]											=							self.get_inference_context()
             with inference_context():
               lowerCAmelCase__			:Any											=							self._ensure_tensor_on_device(__UpperCAmelCase    , device=self.device      )
               lowerCAmelCase__			:Tuple											=							self.model.get_image_embeddings(model_inputs.pop('pixel_values'      )      )
               lowerCAmelCase__			:Optional[int]											=							image_embeddings

         lowerCAmelCase__			:List[Any]											=							grid_points.shape[1]
         lowerCAmelCase__			:Union[str, Any]											=							points_per_batch if points_per_batch is not None else n_points

         if points_per_batch <= 0:
           raise ValueError(
               'Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '
               'To return all points at once, set points_per_batch to None'      )

         for i in range(0    , __UpperCAmelCase    , __UpperCAmelCase      ):
           lowerCAmelCase__			:Optional[Any]											=							grid_points[:, i : i + points_per_batch, :, :]
           lowerCAmelCase__			:List[str]											=							input_labels[:, i : i + points_per_batch]
           lowerCAmelCase__			:List[Any]											=							i == n_points - points_per_batch
           yield {
               "input_points": batched_points,
               "input_labels": labels,
               "input_boxes": crop_boxes,
               "is_last": is_last,
               **model_inputs,
           }






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase=0.88    , __UpperCAmelCase=0.95    , __UpperCAmelCase=0    , __UpperCAmelCase=1    , ):



         '''simple docstring'''

         lowerCAmelCase__			:Any											=							model_inputs.pop('input_boxes'      )
         lowerCAmelCase__			:Optional[int]											=							model_inputs.pop('is_last'      )
         lowerCAmelCase__			:Dict											=							model_inputs.pop('original_sizes'      ).tolist()
         lowerCAmelCase__			:Dict											=							model_inputs.pop('reshaped_input_sizes'      ).tolist()

         lowerCAmelCase__			:Optional[int]											=							self.model(**__UpperCAmelCase      )

         # post processing happens here in order to avoid CPU GPU copies of ALL the masks
         lowerCAmelCase__			:int											=							model_outputs['pred_masks']
         lowerCAmelCase__			:Optional[Any]											=							self.image_processor.post_process_masks(
             __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , binarize=__UpperCAmelCase      )
         lowerCAmelCase__			:Any											=							model_outputs['iou_scores']
         lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Tuple											=							self.image_processor.filter_masks(
             masks[0]    , iou_scores[0]    , original_sizes[0]    , input_boxes[0]    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , )
         return {
             "masks": masks,
             "is_last": is_last,
             "boxes": boxes,
             "iou_scores": iou_scores,
         }






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase=False    , __UpperCAmelCase=False    , __UpperCAmelCase=0.7    , ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							[]
         lowerCAmelCase__			:Optional[Any]											=							[]
         lowerCAmelCase__			:int											=							[]
         for model_output in model_outputs:
           all_scores.append(model_output.pop('iou_scores'      )      )
           all_masks.extend(model_output.pop('masks'      )      )
           all_boxes.append(model_output.pop('boxes'      )      )

         lowerCAmelCase__			:Dict											=							torch.cat(__UpperCAmelCase      )
         lowerCAmelCase__			:Dict											=							torch.cat(__UpperCAmelCase      )
         lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Any											=							self.image_processor.post_process_for_mask_generation(
             __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      )

         lowerCAmelCase__			:Tuple											=							defaultdict(__UpperCAmelCase      )
         for output in model_outputs:
           for k, v in output.items():
             extra[k].append(__UpperCAmelCase      )

         lowerCAmelCase__			:Optional[int]											=							{}
         if output_rle_mask:
           lowerCAmelCase__			:str											=							rle_mask

         if output_bboxes_mask:
           lowerCAmelCase__			:Optional[int]											=							bounding_boxes

         return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
