

'''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))




import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image
else:






 class 		__magic_name__ :






      @staticmethod
      def      UpperCAmelCase_							(							*_lowercase						,		**_lowercase     )->			Tuple:
          pass






@is_pipeline_test
@require_torch_or_tf
@require_vision
class 		__magic_name__ (				unittest.TestCase							):
     UpperCamelCase_     :Union[str, Any]									=	MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
     UpperCamelCase_     :Any									=	TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase						,		_lowercase     )->			List[Any]:
         UpperCamelCase_          =		ImageClassificationPipeline(model=_lowercase						,		image_processor=_lowercase						,		top_k=2     )
         UpperCamelCase_          =		[
             Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     ),
             "http://images.cocodataset.org/val2017/000000039769.jpg",
         ]
         return image_classifier, examples






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase     )->			List[str]:
         UpperCamelCase_          =		image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"     )

         self.assertEqual(
             _lowercase						,		[
                 {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
             ]						,		)

         import datasets

         UpperCamelCase_          =		datasets.load_dataset("hf-internal-testing/fixtures_image_utils"						,		"image"						,		split="test"     )

         # Accepts URL + PIL.Image + lists
         UpperCamelCase_          =		image_classifier(
             [
                 Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     ),
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
                 # RGBA
                 dataset[0]["file"],
                 # LA
                 dataset[1]["file"],
                 # L
                 dataset[2]["file"],
             ]     )
         self.assertEqual(
             _lowercase						,		[
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
             ]						,		)






     @require_torch
     def      UpperCAmelCase_							(							self     )->			str:
         UpperCamelCase_          =		"hf-internal-testing/tiny-random-vit"
         UpperCamelCase_          =		pipeline("image-classification"						,		model=_lowercase     )

         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}]						,		)

         UpperCamelCase_          =		image_classifier(
             [
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
             ]						,		top_k=2						,		)
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
                 [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
             ]						,		)






     @require_tf
     def      UpperCAmelCase_							(							self     )->			Tuple:
         UpperCamelCase_          =		"hf-internal-testing/tiny-random-vit"
         UpperCamelCase_          =		pipeline("image-classification"						,		model=_lowercase						,		framework="tf"     )

         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}]						,		)

         UpperCamelCase_          =		image_classifier(
             [
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
             ]						,		top_k=2						,		)
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
                 [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
             ]						,		)






     def      UpperCAmelCase_							(							self     )->			Optional[Any]:
         UpperCamelCase_          =		PreTrainedTokenizer()

         # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
         UpperCamelCase_          =		pipeline(
             "image-classification"						,		model="hf-internal-testing/tiny-random-vit"						,		tokenizer=_lowercase     )

         self.assertIs(image_classifier.tokenizer						,		_lowercase     )






     @slow
     @require_torch
     def      UpperCAmelCase_							(							self     )->			int:
         # Perceiver is not tested by `run_pipeline_test` properly.
         # That is because the type of feature_extractor and model preprocessor need to be kept
         # in sync, which is not the case in the current design
         UpperCamelCase_          =		pipeline("image-classification"						,		model="deepmind/vision-perceiver-conv"     )
         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 {"score": 0.4_385, "label": "tabby, tabby cat"},
                 {"score": 0.321, "label": "tiger cat"},
                 {"score": 0.0_502, "label": "Egyptian cat"},
                 {"score": 0.0_137, "label": "crib, cot"},
                 {"score": 0.007, "label": "radiator"},
             ]						,		)

         UpperCamelCase_          =		pipeline("image-classification"						,		model="deepmind/vision-perceiver-fourier"     )
         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 {"score": 0.5_658, "label": "tabby, tabby cat"},
                 {"score": 0.1_309, "label": "tiger cat"},
                 {"score": 0.0_722, "label": "Egyptian cat"},
                 {"score": 0.0_707, "label": "remote control, remote"},
                 {"score": 0.0_082, "label": "computer keyboard, keypad"},
             ]						,		)

         UpperCamelCase_          =		pipeline("image-classification"						,		model="deepmind/vision-perceiver-learned"     )
         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 {"score": 0.3_022, "label": "tabby, tabby cat"},
                 {"score": 0.2_362, "label": "Egyptian cat"},
                 {"score": 0.1_856, "label": "tiger cat"},
                 {"score": 0.0_324, "label": "remote control, remote"},
                 {"score": 0.0_096, "label": "quilt, comforter, comfort, puff"},
             ]						,		)
