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 MobileNetVaImageProcessor
  
  class __UpperCamelCase    (							unittest.TestCase		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def __init__(							self							,			lowerCamelCase__							,			lowerCamelCase__=7							,			lowerCamelCase__=3							,			lowerCamelCase__=1_8							,			lowerCamelCase__=3_0							,			lowerCamelCase__=4_0_0							,			lowerCamelCase__=True							,			lowerCamelCase__=None							,			lowerCamelCase__=True							,			lowerCamelCase__=None							,			):
  						UpperCAmelCase__:  Optional[Any]		       =   size if size is not None else {"shortest_edge": 2_0}
  						UpperCAmelCase__:  Tuple		       =   crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
  						UpperCAmelCase__:  Optional[Any]		       =   parent
  						UpperCAmelCase__:  Optional[Any]		       =   batch_size
  						UpperCAmelCase__:  Optional[Any]		       =   num_channels
  						UpperCAmelCase__:  Optional[Any]		       =   image_size
  						UpperCAmelCase__:  Union[str, Any]		       =   min_resolution
  						UpperCAmelCase__:  List[str]		       =   max_resolution
  						UpperCAmelCase__:  Tuple		       =   do_resize
  						UpperCAmelCase__:  Union[str, Any]		       =   size
  						UpperCAmelCase__:  Union[str, Any]		       =   do_center_crop
  						UpperCAmelCase__:  Dict		       =   crop_size
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						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 __UpperCamelCase    (							_a				,unittest.TestCase		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      MobileNetVaImageProcessor if is_vision_available() else None
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Tuple		       =   MobileNetVaImageProcessingTester(self	)
  
  
  
  
  
  					@property
  					def       _UpperCAmelCase						(							self	):
  						return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[str]		       =   self.image_processing_class(**self.image_processor_dict	)
  						self.assertTrue(hasattr(lowerCamelCase__							,			"do_resize"	)	)
  						self.assertTrue(hasattr(lowerCamelCase__							,			"size"	)	)
  						self.assertTrue(hasattr(lowerCamelCase__							,			"do_center_crop"	)	)
  						self.assertTrue(hasattr(lowerCamelCase__							,			"center_crop"	)	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[str]		       =   self.image_processing_class.from_dict(self.image_processor_dict	)
  						self.assertEqual(image_processor.size							,			{"shortest_edge": 2_0}	)
  						self.assertEqual(image_processor.crop_size							,			{"height": 1_8, "width": 1_8}	)
  
  						UpperCAmelCase__:  List[str]		       =   self.image_processing_class.from_dict(self.image_processor_dict							,			size=4_2							,			crop_size=8_4	)
  						self.assertEqual(image_processor.size							,			{"shortest_edge": 4_2}	)
  						self.assertEqual(image_processor.crop_size							,			{"height": 8_4, "width": 8_4}	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						pass
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						# Initialize image_processing
  						UpperCAmelCase__:  Tuple		       =   self.image_processing_class(**self.image_processor_dict	)
  						# create random PIL images
  						UpperCAmelCase__:  Union[str, Any]		       =   prepare_image_inputs(self.image_processor_tester							,			equal_resolution=lowerCamelCase__	)
  						for image in image_inputs:
  							self.assertIsInstance(lowerCamelCase__							,			Image.Image	)
  
  						# Test not batched input
  						UpperCAmelCase__:  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
  						UpperCAmelCase__:  int		       =   image_processing(lowerCamelCase__							,			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       _UpperCAmelCase						(							self	):
  						# Initialize image_processing
  						UpperCAmelCase__:  Any		       =   self.image_processing_class(**self.image_processor_dict	)
  						# create random numpy tensors
  						UpperCAmelCase__:  Optional[Any]		       =   prepare_image_inputs(self.image_processor_tester							,			equal_resolution=lowerCamelCase__							,			numpify=lowerCamelCase__	)
  						for image in image_inputs:
  							self.assertIsInstance(lowerCamelCase__							,			np.ndarray	)
  
  						# Test not batched input
  						UpperCAmelCase__:  Union[str, Any]		       =   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
  						UpperCAmelCase__:  Union[str, Any]		       =   image_processing(lowerCamelCase__							,			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       _UpperCAmelCase						(							self	):
  						# Initialize image_processing
  						UpperCAmelCase__:  Optional[int]		       =   self.image_processing_class(**self.image_processor_dict	)
  						# create random PyTorch tensors
  						UpperCAmelCase__:  List[Any]		       =   prepare_image_inputs(self.image_processor_tester							,			equal_resolution=lowerCamelCase__							,			torchify=lowerCamelCase__	)
  						for image in image_inputs:
  							self.assertIsInstance(lowerCamelCase__							,			torch.Tensor	)
  
  						# Test not batched input
  						UpperCAmelCase__:  Any		       =   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
  						UpperCAmelCase__:  List[Any]		       =   image_processing(lowerCamelCase__							,			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"],
  						    )							,			)
style_context: 
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,):
  	UpperCAmelCase__:  Tuple		       =   list(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  int		       =   list(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  Dict		       =   len(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  int		       =   len(SCREAMING_SNAKE_CASE			)
  
  	UpperCAmelCase__:  Any		       =   [
  	    [0 for _ in range(len_destination_seq + 1			)] for _ in range(len_source_seq + 1			)
  	]
  	UpperCAmelCase__:  List[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			):
  		UpperCAmelCase__:  List[Any]		       =   i * delete_cost
  		UpperCAmelCase__:  str		       =   f"D{source_seq[i - 1]:c}"
  
  	for i in range(1  ,len_destination_seq + 1			):
  		UpperCAmelCase__:  Union[str, Any]		       =   i * insert_cost
  		UpperCAmelCase__:  Optional[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]:
  				UpperCAmelCase__:  Any		       =   costs[i - 1][j - 1] + copy_cost
  				UpperCAmelCase__:  Tuple		       =   f"C{source_seq[i - 1]:c}"
  			else:
  				UpperCAmelCase__:  Union[str, Any]		       =   costs[i - 1][j - 1] + replace_cost
  				UpperCAmelCase__:  Optional[int]		       =   f"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]			)
  
  			if costs[i - 1][j] + delete_cost < costs[i][j]:
  				UpperCAmelCase__:  Optional[int]		       =   costs[i - 1][j] + delete_cost
  				UpperCAmelCase__:  int		       =   f"D{source_seq[i - 1]:c}"
  
  			if costs[i][j - 1] + insert_cost < costs[i][j]:
  				UpperCAmelCase__:  Any		       =   costs[i][j - 1] + insert_cost
  				UpperCAmelCase__:  Tuple		       =   f"I{destination_seq[j - 1]:c}"
  
  	return costs, ops
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	if i == 0 and j == 0:
  		return []
  	else:
  		if ops[i][j][0] == "C" or ops[i][j][0] == "R":
  			UpperCAmelCase__:  Union[str, Any]		       =   assemble_transformation(SCREAMING_SNAKE_CASE  ,i - 1  ,j - 1			)
  			seq.append(ops[i][j]			)
  			return seq
  		elif ops[i][j][0] == "D":
  			UpperCAmelCase__:  Optional[Any]		       =   assemble_transformation(SCREAMING_SNAKE_CASE  ,i - 1  ,SCREAMING_SNAKE_CASE			)
  			seq.append(ops[i][j]			)
  			return seq
  		else:
  			UpperCAmelCase__:  Dict		       =   assemble_transformation(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,j - 1			)
  			seq.append(ops[i][j]			)
  			return seq
  
  
  if __name__ == "__main__":
  					_lowerCAmelCase   ,  _lowerCAmelCase     :			Dict    =compute_transform_tables("""Python""", """Algorithms""", -1, 1, 2, 2)
  
  					_lowerCAmelCase     :			List[Any]    =len(operations)
  					_lowerCAmelCase     :			Optional[int]    =len(operations[0])
  					_lowerCAmelCase     :			Dict    =assemble_transformation(operations, m - 1, n - 1)
  
  					_lowerCAmelCase     :			int    =list("""Python""")
  					_lowerCAmelCase     :			Optional[int]    =0
  					_lowerCAmelCase     :			List[Any]    =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":
  																				_lowerCAmelCase     :			Tuple    =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))