code: 
  
  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 ViltImageProcessor
  
  
  
  
  
  
  class    __A				(							unittest.TestCase ):
  
  
  
  
  
  
      def __init__(	self			:    Union[str, Any]    ,				UpperCAmelCase_			:    str    ,				UpperCAmelCase_			:    Optional[Any]=7    ,				UpperCAmelCase_			:    Any=3    ,				UpperCAmelCase_			:    int=18    ,				UpperCAmelCase_			:    Union[str, Any]=30    ,				UpperCAmelCase_			:    Union[str, Any]=400    ,				UpperCAmelCase_			:    Optional[int]=True    ,				UpperCAmelCase_			:    Union[str, Any]=None    ,				UpperCAmelCase_			:    Union[str, Any]=2    ,				UpperCAmelCase_			:    Optional[int]=True    ,				UpperCAmelCase_			:    Tuple=[0.5, 0.5, 0.5]    ,				UpperCAmelCase_			:    Any=[0.5, 0.5, 0.5]    ,				):
          lowerCAmelCase				:			Optional[int]    			=      size if size is not None else {'shortest_edge': 30}
          lowerCAmelCase				:			Tuple    			=      parent
          lowerCAmelCase				:			List[str]    			=      batch_size
          lowerCAmelCase				:			int    			=      num_channels
          lowerCAmelCase				:			Dict    			=      image_size
          lowerCAmelCase				:			Dict    			=      min_resolution
          lowerCAmelCase				:			Optional[int]    			=      max_resolution
          lowerCAmelCase				:			Tuple    			=      do_resize
          lowerCAmelCase				:			List[Any]    			=      size
          lowerCAmelCase				:			Any    			=      size_divisor
          lowerCAmelCase				:			Optional[Any]    			=      do_normalize
          lowerCAmelCase				:			Optional[Any]    			=      image_mean
          lowerCAmelCase				:			str    			=      image_std
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[int]    ):
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
              "size_divisor": self.size_divisor,
          }
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Dict    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    List[str]=False    ):
          if not batched:
              lowerCAmelCase				:			List[str]    			=      self.size['shortest_edge']
              lowerCAmelCase				:			str    			=      image_inputs[0]
              if isinstance(UpperCAmelCase_    ,				Image.Image    ):
                  lowerCAmelCase			,  lowerCAmelCase				:			Any    			=      image.size
              else:
                  lowerCAmelCase			,  lowerCAmelCase				:			Optional[int]    			=      image.shape[1], image.shape[2]
              lowerCAmelCase				:			Union[str, Any]    			=      size / min(UpperCAmelCase_    ,				UpperCAmelCase_    )
              if h < w:
                  lowerCAmelCase			,  lowerCAmelCase				:			Any    			=      size, scale * w
              else:
                  lowerCAmelCase			,  lowerCAmelCase				:			int    			=      scale * h, size
  
              lowerCAmelCase				:			Optional[int]    			=      int((1333 / 800) * size    )
              if max(UpperCAmelCase_    ,				UpperCAmelCase_    ) > max_size:
                  lowerCAmelCase				:			List[Any]    			=      max_size / max(UpperCAmelCase_    ,				UpperCAmelCase_    )
                  lowerCAmelCase				:			Any    			=      newh * scale
                  lowerCAmelCase				:			str    			=      neww * scale
  
              lowerCAmelCase			,  lowerCAmelCase				:			Optional[Any]    			=      int(newh + 0.5    ), int(neww + 0.5    )
              lowerCAmelCase			,  lowerCAmelCase				:			Optional[Any]    			=      (
                  newh // self.size_divisor * self.size_divisor,
                  neww // self.size_divisor * self.size_divisor,
              )
  
          else:
              lowerCAmelCase				:			Any    			=      []
              for image in image_inputs:
                  lowerCAmelCase			,  lowerCAmelCase				:			Dict    			=      self.get_expected_values([image]    )
                  expected_values.append((expected_height, expected_width)    )
              lowerCAmelCase				:			str    			=      max(UpperCAmelCase_    ,				key=lambda UpperCAmelCase_    : item[0]    )[0]
              lowerCAmelCase				:			List[Any]    			=      max(UpperCAmelCase_    ,				key=lambda UpperCAmelCase_    : item[1]    )[1]
  
          return expected_height, expected_width
  
  
  
  
  
  
  @require_torch
  @require_vision
  class    __A				(							lowerCAmelCase						,						unittest.TestCase ):
      lowerCAmelCase_							:		Optional[Any]						=							ViltImageProcessor if is_vision_available() else None
  
  
  
  
  
  
      def 	lowercase__ (	self			:    str    ):
          lowerCAmelCase				:			List[str]    			=      ViltImageProcessingTester(self    )
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    Union[str, Any]    ):
          return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ):
          lowerCAmelCase				:			Optional[Any]    			=      self.image_processing_class(**self.image_processor_dict    )
          self.assertTrue(hasattr(UpperCAmelCase_    ,				'image_mean'    )    )
          self.assertTrue(hasattr(UpperCAmelCase_    ,				'image_std'    )    )
          self.assertTrue(hasattr(UpperCAmelCase_    ,				'do_normalize'    )    )
          self.assertTrue(hasattr(UpperCAmelCase_    ,				'do_resize'    )    )
          self.assertTrue(hasattr(UpperCAmelCase_    ,				'size'    )    )
          self.assertTrue(hasattr(UpperCAmelCase_    ,				'size_divisor'    )    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ):
          lowerCAmelCase				:			Union[str, Any]    			=      self.image_processing_class.from_dict(self.image_processor_dict    )
          self.assertEqual(image_processor.size    ,				{'shortest_edge': 30}    )
  
          lowerCAmelCase				:			str    			=      self.image_processing_class.from_dict(self.image_processor_dict    ,				size=42    )
          self.assertEqual(image_processor.size    ,				{'shortest_edge': 42}    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Dict    ):
          pass
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[Any]    ):
          # Initialize image_processing
          lowerCAmelCase				:			List[Any]    			=      self.image_processing_class(**self.image_processor_dict    )
          # create random PIL images
          lowerCAmelCase				:			int    			=      prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=UpperCAmelCase_    )
          for image in image_inputs:
              self.assertIsInstance(UpperCAmelCase_    ,				Image.Image    )
  
          # Test not batched input
          lowerCAmelCase				:			Optional[int]    			=      image_processing(image_inputs[0]    ,				return_tensors='pt'    ).pixel_values
  
          lowerCAmelCase			,  lowerCAmelCase				:			int    			=      self.image_processor_tester.get_expected_values(UpperCAmelCase_    )
          self.assertEqual(
              encoded_images.shape    ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,				)
  
          # Test batched
          lowerCAmelCase				:			str    			=      image_processing(UpperCAmelCase_    ,				return_tensors='pt'    ).pixel_values
  
          lowerCAmelCase			,  lowerCAmelCase				:			Dict    			=      self.image_processor_tester.get_expected_values(UpperCAmelCase_    ,				batched=UpperCAmelCase_    )
          self.assertEqual(
              encoded_images.shape    ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )    ,				)
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[int]    ):
          # Initialize image_processing
          lowerCAmelCase				:			int    			=      self.image_processing_class(**self.image_processor_dict    )
          # create random numpy tensors
          lowerCAmelCase				:			Union[str, Any]    			=      prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=UpperCAmelCase_    ,				numpify=UpperCAmelCase_    )
          for image in image_inputs:
              self.assertIsInstance(UpperCAmelCase_    ,				np.ndarray    )
  
          # Test not batched input
          lowerCAmelCase				:			List[Any]    			=      image_processing(image_inputs[0]    ,				return_tensors='pt'    ).pixel_values
  
          lowerCAmelCase			,  lowerCAmelCase				:			Dict    			=      self.image_processor_tester.get_expected_values(UpperCAmelCase_    )
          self.assertEqual(
              encoded_images.shape    ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,				)
  
          # Test batched
          lowerCAmelCase				:			str    			=      image_processing(UpperCAmelCase_    ,				return_tensors='pt'    ).pixel_values
  
          lowerCAmelCase			,  lowerCAmelCase				:			Dict    			=      self.image_processor_tester.get_expected_values(UpperCAmelCase_    ,				batched=UpperCAmelCase_    )
          self.assertEqual(
              encoded_images.shape    ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )    ,				)
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ):
          # Initialize image_processing
          lowerCAmelCase				:			Optional[int]    			=      self.image_processing_class(**self.image_processor_dict    )
          # create random PyTorch tensors
          lowerCAmelCase				:			Tuple    			=      prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=UpperCAmelCase_    ,				torchify=UpperCAmelCase_    )
          for image in image_inputs:
              self.assertIsInstance(UpperCAmelCase_    ,				torch.Tensor    )
  
          # Test not batched input
          lowerCAmelCase				:			List[str]    			=      image_processing(image_inputs[0]    ,				return_tensors='pt'    ).pixel_values
  
          lowerCAmelCase			,  lowerCAmelCase				:			int    			=      self.image_processor_tester.get_expected_values(UpperCAmelCase_    )
          self.assertEqual(
              encoded_images.shape    ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,				)
  
          # Test batched
          lowerCAmelCase				:			List[Any]    			=      image_processing(UpperCAmelCase_    ,				return_tensors='pt'    ).pixel_values
  
          lowerCAmelCase			,  lowerCAmelCase				:			Optional[int]    			=      self.image_processor_tester.get_expected_values(UpperCAmelCase_    ,				batched=UpperCAmelCase_    )
          self.assertEqual(
              encoded_images.shape    ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )    ,				)
  
  
  
  
  
  
style_context: 
  
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      )					-> tuple[list[list[int]], list[list[str]]]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			List[Any]    			=      list(_UpperCAmelCase				)
      lowerCAmelCase				:			Union[str, Any]    			=      list(_UpperCAmelCase				)
      lowerCAmelCase				:			Optional[int]    			=      len(_UpperCAmelCase				)
      lowerCAmelCase				:			List[Any]    			=      len(_UpperCAmelCase				)
  
      lowerCAmelCase				:			str    			=      [
          [0 for _ in range(len_destination_seq + 1				)] for _ in range(len_source_seq + 1				)
      ]
      lowerCAmelCase				:			str    			=      [
          ['0' for _ in range(len_destination_seq + 1				)] for _ in range(len_source_seq + 1				)
      ]
  
      for i in range(1,      len_source_seq + 1				):
          lowerCAmelCase				:			int    			=      i * delete_cost
          lowerCAmelCase				:			List[str]    			=      f"D{source_seq[i - 1]:c}"
  
      for i in range(1,      len_destination_seq + 1				):
          lowerCAmelCase				:			Tuple    			=      i * insert_cost
          lowerCAmelCase				:			Union[str, Any]    			=      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]:
                  lowerCAmelCase				:			str    			=      costs[i - 1][j - 1] + copy_cost
                  lowerCAmelCase				:			Tuple    			=      f"C{source_seq[i - 1]:c}"
              else:
                  lowerCAmelCase				:			Optional[int]    			=      costs[i - 1][j - 1] + replace_cost
                  lowerCAmelCase				:			Optional[int]    			=      f"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]				)
  
              if costs[i - 1][j] + delete_cost < costs[i][j]:
                  lowerCAmelCase				:			List[str]    			=      costs[i - 1][j] + delete_cost
                  lowerCAmelCase				:			Optional[int]    			=      f"D{source_seq[i - 1]:c}"
  
              if costs[i][j - 1] + insert_cost < costs[i][j]:
                  lowerCAmelCase				:			Union[str, Any]    			=      costs[i][j - 1] + insert_cost
                  lowerCAmelCase				:			str    			=      f"I{destination_seq[j - 1]:c}"
  
      return costs, ops
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> list[str]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      if i == 0 and j == 0:
          return []
      else:
          if ops[i][j][0] == "C" or ops[i][j][0] == "R":
              lowerCAmelCase				:			List[str]    			=      assemble_transformation(_UpperCAmelCase,      i - 1,      j - 1				)
              seq.append(ops[i][j]				)
              return seq
          elif ops[i][j][0] == "D":
              lowerCAmelCase				:			int    			=      assemble_transformation(_UpperCAmelCase,      i - 1,      _UpperCAmelCase				)
              seq.append(ops[i][j]				)
              return seq
          else:
              lowerCAmelCase				:			Tuple    			=      assemble_transformation(_UpperCAmelCase,      _UpperCAmelCase,      j - 1				)
              seq.append(ops[i][j]				)
              return seq
  
  
  if __name__ == "__main__":
   __A     ,      __A   :     List[str]      =			compute_transform_tables('''Python''', '''Algorithms''', -1, 1, 2, 2)
  
   __A   :     Optional[int]      =			len(operations)
   __A   :     Optional[Any]      =			len(operations[0])
   __A   :     Any      =			assemble_transformation(operations, m - 1, n - 1)
  
   __A   :     int      =			list('''Python''')
   __A   :     Tuple      =			0
   __A   :     Tuple      =			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":
      __A   :     Any      =			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))
  
  
  
  
  
  