
"""simple docstring"""

from typing import Dict, Optional

import numpy as np

import datasets


UpperCamelCase			:       int									=				"\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n"

UpperCamelCase			:       Union[str, Any]									=				"\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric(\"mean_iou\")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), 'per_category_accuracy': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}\n"

UpperCamelCase			:       List[Any]									=				"\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}"






def 							A		(       snake_case   :Any       ,      snake_case   :Any       ,      snake_case   :List[Any]       ,      snake_case   :bool       ,      snake_case   :Optional[Dict[int, int]] = None       ,      snake_case   :bool = False       ,      )   ->			List[str]:
 if label_map is not None:
  for old_id, new_id in label_map.items():
   __UpperCamelCase       						= new_id

    # turn into Numpy arrays
 __UpperCamelCase       						= np.array(snake_case	)
 __UpperCamelCase       						= np.array(snake_case	)

 if reduce_labels:
  __UpperCamelCase       						= 2_5_5
  __UpperCamelCase       						= label - 1
  __UpperCamelCase       						= 2_5_5

 __UpperCamelCase       						= label != ignore_index
 __UpperCamelCase       						= np.not_equal(snake_case       ,      snake_case	)
 __UpperCamelCase       						= pred_label[mask]
 __UpperCamelCase       						= np.array(snake_case	)[mask]

 __UpperCamelCase       						= pred_label[pred_label == label]

 __UpperCamelCase       						= np.histogram(snake_case       ,      bins=snake_case       ,      range=(0, num_labels - 1)	)[0]
 __UpperCamelCase       						= np.histogram(snake_case       ,      bins=snake_case       ,      range=(0, num_labels - 1)	)[0]
 __UpperCamelCase       						= np.histogram(snake_case       ,      bins=snake_case       ,      range=(0, num_labels - 1)	)[0]

 __UpperCamelCase       						= area_pred_label + area_label - area_intersect

 return area_intersect, area_union, area_pred_label, area_label






def 							A		(       snake_case   :Optional[Any]       ,      snake_case   :List[Any]       ,      snake_case   :int       ,      snake_case   :bool       ,      snake_case   :Optional[Dict[int, int]] = None       ,      snake_case   :bool = False       ,      )   ->			Union[str, Any]:
 __UpperCamelCase       						= np.zeros((num_labels,)       ,      dtype=np.floataa	)
 __UpperCamelCase       						= np.zeros((num_labels,)       ,      dtype=np.floataa	)
 __UpperCamelCase       						= np.zeros((num_labels,)       ,      dtype=np.floataa	)
 __UpperCamelCase       						= np.zeros((num_labels,)       ,      dtype=np.floataa	)
 for result, gt_seg_map in zip(snake_case       ,      snake_case	):
  __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= intersect_and_union(
      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case	)
  total_area_intersect += area_intersect
  total_area_union += area_union
  total_area_pred_label += area_pred_label
  total_area_label += area_label
 return total_area_intersect, total_area_union, total_area_pred_label, total_area_label






def 							A		(       snake_case   :List[Any]       ,      snake_case   :Dict       ,      snake_case   :Any       ,      snake_case   :bool       ,      snake_case   :Optional[int] = None       ,      snake_case   :Optional[Dict[int, int]] = None       ,      snake_case   :bool = False       ,      )   ->			Any:
 __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= total_intersect_and_union(
     snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case	)

 # compute metrics
 __UpperCamelCase       						= {}

 __UpperCamelCase       						= total_area_intersect.sum() / total_area_label.sum()
 __UpperCamelCase       						= total_area_intersect / total_area_union
 __UpperCamelCase       						= total_area_intersect / total_area_label

 __UpperCamelCase       						= np.nanmean(snake_case	)
 __UpperCamelCase       						= np.nanmean(snake_case	)
 __UpperCamelCase       						= all_acc
 __UpperCamelCase       						= iou
 __UpperCamelCase       						= acc

 if nan_to_num is not None:
  __UpperCamelCase       						= {metric: np.nan_to_num(snake_case       ,      nan=snake_case	) for metric, metric_value in metrics.items()}

 return metrics





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,						_KWARGS_DESCRIPTION    )
class __lowerCAmelCase	(   datasets.Metric    ):





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return datasets.MetricInfo(
          description=_DESCRIPTION			,  citation=_CITATION			,  inputs_description=_KWARGS_DESCRIPTION			,  features=datasets.Features(
              # 1st Seq - height dim, 2nd - width dim
              {
                  'predictions': datasets.Sequence(datasets.Sequence(datasets.Value('uint16'   )   )   ),
                  'references': datasets.Sequence(datasets.Sequence(datasets.Value('uint16'   )   )   ),
              }   )			,  reference_urls=[
              'https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'
          ]			,  )







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase = None			,  __UpperCAmelCase = None			,  __UpperCAmelCase = False			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= mean_iou(
          results=__UpperCAmelCase			,  gt_seg_maps=__UpperCAmelCase			,  num_labels=__UpperCAmelCase			,  ignore_index=__UpperCAmelCase			,  nan_to_num=__UpperCAmelCase			,  label_map=__UpperCAmelCase			,  reduce_labels=__UpperCAmelCase			,  )
      return iou_result





"""simple docstring"""

import string

import numpy






def 							A		(       snake_case   :int       ,      snake_case   :int	)   ->			int:
 return b if a == 0 else greatest_common_divisor(b % a       ,      snake_case	)

class __lowerCAmelCase	:
     lowercase		     =		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)
     lowercase		     =		numpy.vectorize(lambda __SCREAMING_SNAKE_CASE    : x % 36    )

     lowercase		     =		numpy.vectorize(__SCREAMING_SNAKE_CASE    )





     def __init__(			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.modulus(__UpperCAmelCase   )  # mod36 calc's on the encrypt key
      self.check_determinant()  # validate the determinant of the encryption key
      __UpperCamelCase       						= encrypt_key.shape[0]





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      return self.key_string.index(__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      return self.key_string[round(__UpperCAmelCase   )]





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= round(numpy.linalg.det(self.encrypt_key   )   )

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

      __UpperCamelCase       						= len(self.key_string   )
      if greatest_common_divisor(__UpperCAmelCase			,  len(self.key_string   )   ) != 1:
       __UpperCamelCase       						= (
           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 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= [char for char in text.upper() if char in self.key_string]

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

      return "".join(__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.process_text(text.upper()   )
      __UpperCamelCase       						= ''

      for i in range(0			,  len(__UpperCAmelCase   ) - self.break_key + 1			,  self.break_key   ):
       __UpperCamelCase       						= text[i : i + self.break_key]
       __UpperCamelCase       						= [self.replace_letters(__UpperCAmelCase   ) for char in batch]
       __UpperCamelCase       						= numpy.array([vec]   ).T
       __UpperCamelCase       						= self.modulus(self.encrypt_key.dot(__UpperCAmelCase   )   ).T.tolist()[
           0
       ]
       __UpperCamelCase       						= ''.join(
           self.replace_digits(__UpperCAmelCase   ) for num in batch_encrypted   )
       encrypted += encrypted_batch

      return encrypted





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= round(numpy.linalg.det(self.encrypt_key   )   )

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

      __UpperCamelCase       						= (
          det_inv
          * numpy.linalg.det(self.encrypt_key   )
          * numpy.linalg.inv(self.encrypt_key   )
      )

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







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.make_decrypt_key()
      __UpperCamelCase       						= self.process_text(text.upper()   )
      __UpperCamelCase       						= ''

      for i in range(0			,  len(__UpperCAmelCase   ) - self.break_key + 1			,  self.break_key   ):
       __UpperCamelCase       						= text[i : i + self.break_key]
       __UpperCamelCase       						= [self.replace_letters(__UpperCAmelCase   ) for char in batch]
       __UpperCamelCase       						= numpy.array([vec]   ).T
       __UpperCamelCase       						= self.modulus(decrypt_key.dot(__UpperCAmelCase   )   ).T.tolist()[0]
       __UpperCamelCase       						= ''.join(
           self.replace_digits(__UpperCAmelCase   ) for num in batch_decrypted   )
       decrypted += decrypted_batch

      return decrypted






def 							A		(       )   ->			None:
 __UpperCamelCase       						= int(input('Enter the order of the encryption key: '	)	)
 __UpperCamelCase       						= []

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

 __UpperCamelCase       						= HillCipher(numpy.array(snake_case	)	)

 print('Would you like to encrypt or decrypt some text? (1 or 2)'	)
 __UpperCamelCase       						= input('\n1. Encrypt\n2. Decrypt\n'	)
 if option == "1":
  __UpperCamelCase       						= input('What text would you like to encrypt?: '	)
  print('Your encrypted text is:'	)
  print(hc.encrypt(snake_case	)	)
 elif option == "2":
  __UpperCamelCase       						= input('What text would you like to decrypt?: '	)
  print('Your decrypted text is:'	)
  print(hc.decrypt(snake_case	)	)


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      main()



