

'''simple docstring'''






def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	str  ,  SCREAMING_SNAKE_CASE_     :	str  ,  SCREAMING_SNAKE_CASE_     :	int  ,  SCREAMING_SNAKE_CASE_     :	int  ,  SCREAMING_SNAKE_CASE_     :	int  ,  SCREAMING_SNAKE_CASE_     :	int  ,  )							->   tuple[list[list[int]], list[list[str]]]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			str     =       list(SCREAMING_SNAKE_CASE_    )
      SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       list(SCREAMING_SNAKE_CASE_    )
      SCREAMING_SNAKE_CASE_      :			Dict     =       len(SCREAMING_SNAKE_CASE_    )
      SCREAMING_SNAKE_CASE_      :			List[Any]     =       len(SCREAMING_SNAKE_CASE_    )

      SCREAMING_SNAKE_CASE_      :			List[Any]     =       [
          [0 for _ in range(len_destination_seq + 1    )] for _ in range(len_source_seq + 1    )
      ]
      SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       [
          ["0" for _ in range(len_destination_seq + 1    )] for _ in range(len_source_seq + 1    )
      ]

      for i in range(1  ,  len_source_seq + 1    ):
            SCREAMING_SNAKE_CASE_      :			str     =       i * delete_cost
            SCREAMING_SNAKE_CASE_      :			Any     =       F"D{source_seq[i - 1]:c}"

      for i in range(1  ,  len_destination_seq + 1    ):
            SCREAMING_SNAKE_CASE_      :			Optional[int]     =       i * insert_cost
            SCREAMING_SNAKE_CASE_      :			Dict     =       F"I{destination_seq[i - 1]:c}"

      for i in range(1  ,  len_source_seq + 1    ):
            for j in range(1  ,  len_destination_seq + 1    ):
                  if source_seq[i - 1] == destination_seq[j - 1]:
                        SCREAMING_SNAKE_CASE_      :			Any     =       costs[i - 1][j - 1] + copy_cost
                        SCREAMING_SNAKE_CASE_      :			Dict     =       F"C{source_seq[i - 1]:c}"
                  else:
                        SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       costs[i - 1][j - 1] + replace_cost
                        SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       F"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]    )

                  if costs[i - 1][j] + delete_cost < costs[i][j]:
                        SCREAMING_SNAKE_CASE_      :			List[str]     =       costs[i - 1][j] + delete_cost
                        SCREAMING_SNAKE_CASE_      :			Any     =       F"D{source_seq[i - 1]:c}"

                  if costs[i][j - 1] + insert_cost < costs[i][j]:
                        SCREAMING_SNAKE_CASE_      :			Tuple     =       costs[i][j - 1] + insert_cost
                        SCREAMING_SNAKE_CASE_      :			Tuple     =       F"I{destination_seq[j - 1]:c}"

      return costs, ops


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	list[list[str]]  ,  SCREAMING_SNAKE_CASE_     :	int  ,  SCREAMING_SNAKE_CASE_     :	int    )							->   list[str]:
      """simple docstring"""

      if i == 0 and j == 0:
            return []
      else:
            if ops[i][j][0] == "C" or ops[i][j][0] == "R":
                  SCREAMING_SNAKE_CASE_      :			Tuple     =       assemble_transformation(SCREAMING_SNAKE_CASE_  ,  i - 1  ,  j - 1    )
                  seq.append(ops[i][j]    )
                  return seq
            elif ops[i][j][0] == "D":
                  SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       assemble_transformation(SCREAMING_SNAKE_CASE_  ,  i - 1  ,  SCREAMING_SNAKE_CASE_    )
                  seq.append(ops[i][j]    )
                  return seq
            else:
                  SCREAMING_SNAKE_CASE_      :			int     =       assemble_transformation(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  j - 1    )
                  seq.append(ops[i][j]    )
                  return seq


if __name__ == "__main__":
      snake_case_  ,	snake_case_       =       compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)

      snake_case_       =       len(operations)
      snake_case_       =       len(operations[0])
      snake_case_       =       assemble_transformation(operations, m - 1, n - 1)

      snake_case_       =       list('Python')
      snake_case_       =       0
      snake_case_       =       0

      with open('min_cost.txt', 'w') as file:
            for op in sequence:
                  print(''.join(string))

                  if op[0] == "C":
                        file.write('%-16s' % 'Copy %c' % op[1])
                        file.write('\t\t\t' + ''.join(string))
                        file.write('\r\n')

                        cost -= 1
                  elif op[0] == "R":
                        snake_case_       =       op[2]

                        file.write('%-16s' % ('Replace %c' % op[1] + ' with ' + str(op[2])))
                        file.write('\t\t' + ''.join(string))
                        file.write('\r\n')

                        cost += 1
                  elif op[0] == "D":
                        string.pop(i)

                        file.write('%-16s' % 'Delete %c' % op[1])
                        file.write('\t\t\t' + ''.join(string))
                        file.write('\r\n')

                        cost += 2
                  else:
                        string.insert(i, op[1])

                        file.write('%-16s' % 'Insert %c' % op[1])
                        file.write('\t\t\t' + ''.join(string))
                        file.write('\r\n')

                        cost += 2

                  i += 1

            print(''.join(string))
            print('Cost: ', cost)

            file.write('\r\nMinimum cost: ' + str(cost))




'''simple docstring'''






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import MobileNetVaImageProcessor
class 	SCREAMING_SNAKE_CASE__			(				unittest.TestCase      ):




    def __init__(					self				,     lowercase__				,     lowercase__=7				,     lowercase__=3				,     lowercase__=18				,     lowercase__=30				,     lowercase__=400				,     lowercase__=True				,     lowercase__=None				,     lowercase__=True				,     lowercase__=None				,     ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			List[str]     =       size if size is not None else {"shortest_edge": 20}
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       crop_size if crop_size is not None else {"height": 18, "width": 18}
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       parent
          SCREAMING_SNAKE_CASE_      :			Tuple     =       batch_size
          SCREAMING_SNAKE_CASE_      :			int     =       num_channels
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       image_size
          SCREAMING_SNAKE_CASE_      :			Dict     =       min_resolution
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       max_resolution
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       do_resize
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       size
          SCREAMING_SNAKE_CASE_      :			Tuple     =       do_center_crop
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       crop_size







    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
          }






@require_torch
@require_vision
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase,unittest.TestCase      ):
    _A			    =							MobileNetVaImageProcessor if is_vision_available() else None




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Any     =       MobileNetVaImageProcessingTester(self			)




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return self.image_processor_tester.prepare_image_processor_dict()




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.image_processing_class(**self.image_processor_dict			)
          self.assertTrue(hasattr(lowercase__				,     "do_resize"			)			)
          self.assertTrue(hasattr(lowercase__				,     "size"			)			)
          self.assertTrue(hasattr(lowercase__				,     "do_center_crop"			)			)
          self.assertTrue(hasattr(lowercase__				,     "center_crop"			)			)




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.image_processing_class.from_dict(self.image_processor_dict			)
          self.assertEqual(image_processor.size				,     {"shortest_edge": 20}			)
          self.assertEqual(image_processor.crop_size				,     {"height": 18, "width": 18}			)

          SCREAMING_SNAKE_CASE_      :			List[str]     =       self.image_processing_class.from_dict(self.image_processor_dict				,     size=42				,     crop_size=84			)
          self.assertEqual(image_processor.size				,     {"shortest_edge": 42}			)
          self.assertEqual(image_processor.crop_size				,     {"height": 84, "width": 84}			)




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          pass




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       self.image_processing_class(**self.image_processor_dict			)
          # create random PIL images
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       prepare_image_inputs(self.image_processor_tester				,     equal_resolution=lowercase__			)
          for image in image_inputs:
                self.assertIsInstance(lowercase__				,     Image.Image			)

          # Test not batched input
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       image_processing(image_inputs[0]				,     return_tensors="pt"			).pixel_values
          self.assertEqual(
              encoded_images.shape				,     (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )				,     )

          # Test batched
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       image_processing(lowercase__				,     return_tensors="pt"			).pixel_values
          self.assertEqual(
              encoded_images.shape				,     (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )				,     )




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.image_processing_class(**self.image_processor_dict			)
          # create random numpy tensors
          SCREAMING_SNAKE_CASE_      :			Dict     =       prepare_image_inputs(self.image_processor_tester				,     equal_resolution=lowercase__				,     numpify=lowercase__			)
          for image in image_inputs:
                self.assertIsInstance(lowercase__				,     np.ndarray			)

          # Test not batched input
          SCREAMING_SNAKE_CASE_      :			Dict     =       image_processing(image_inputs[0]				,     return_tensors="pt"			).pixel_values
          self.assertEqual(
              encoded_images.shape				,     (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )				,     )

          # Test batched
          SCREAMING_SNAKE_CASE_      :			Dict     =       image_processing(lowercase__				,     return_tensors="pt"			).pixel_values
          self.assertEqual(
              encoded_images.shape				,     (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )				,     )







    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			List[str]     =       self.image_processing_class(**self.image_processor_dict			)
          # create random PyTorch tensors
          SCREAMING_SNAKE_CASE_      :			Any     =       prepare_image_inputs(self.image_processor_tester				,     equal_resolution=lowercase__				,     torchify=lowercase__			)
          for image in image_inputs:
                self.assertIsInstance(lowercase__				,     torch.Tensor			)

          # Test not batched input
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       image_processing(image_inputs[0]				,     return_tensors="pt"			).pixel_values
          self.assertEqual(
              encoded_images.shape				,     (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )				,     )

          # Test batched
          SCREAMING_SNAKE_CASE_      :			List[str]     =       image_processing(lowercase__				,     return_tensors="pt"			).pixel_values
          self.assertEqual(
              encoded_images.shape				,     (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )				,     )

