code: 
  
  
  
  import os
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      )				->			List[str]:
       UpperCamelCase__    :     Any        =						os.path.dirname(os.path.realpath(_UpperCAmelCase    )    )
       UpperCamelCase__    :     Union[str, Any]        =						os.path.join(_UpperCAmelCase      ,					"triangle.txt"    )
  
       with open(_UpperCAmelCase    ) as f:
            UpperCamelCase__    :     Optional[Any]        =						f.readlines()
  
       UpperCamelCase__    :     Optional[int]        =						[[int(_UpperCAmelCase    ) for y in x.rstrip("\r\n"    ).split(" "    )] for x in triangle]
  
       for i in range(1      ,					len(_UpperCAmelCase    )    ):
            for j in range(len(a[i]    )    ):
                 UpperCamelCase__    :     Union[str, Any]        =						a[i - 1][j] if j != len(a[i - 1]    ) else 0
                 UpperCamelCase__    :     Union[str, Any]        =						a[i - 1][j - 1] if j > 0 else 0
                 a[i][j] += max(_UpperCAmelCase      ,					_UpperCAmelCase    )
       return max(a[-1]    )
  
  
  if __name__ == "__main__":
   print(solution())
style_context: 
  
  
  
  
  
  
  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     __a						:
  
  
  
  
  
  
  
    @staticmethod
    def       __lowercase      (	*SCREAMING_SNAKE_CASE      :     Optional[Any]							,     **SCREAMING_SNAKE_CASE      :     Union[str, Any]		):
  
  
         '''simple docstring'''
  
  
  
  
  
  
         pass
  
  
  
  
  
  @is_pipeline_test
  @require_torch_or_tf
  @require_vision
  class     __a						(					unittest.TestCase ):
   _lowerCAmelCase : Tuple 				=				MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
   _lowerCAmelCase : str 				=				TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Union[str, Any]							,     SCREAMING_SNAKE_CASE      :     str							,     SCREAMING_SNAKE_CASE      :     Union[str, Any]							,     SCREAMING_SNAKE_CASE      :     Optional[Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Dict        =						ImageClassificationPipeline(model=SCREAMING_SNAKE_CASE							,     image_processor=SCREAMING_SNAKE_CASE							,     top_k=2		)
        UpperCamelCase__    :     Union[str, Any]        =						[
            Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		),
            "http://images.cocodataset.org/val2017/000000039769.jpg",
        ]
        return image_classifier, examples
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Tuple							,     SCREAMING_SNAKE_CASE      :     Tuple							,     SCREAMING_SNAKE_CASE      :     Tuple		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Optional[Any]        =						image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
  
        self.assertEqual(
            SCREAMING_SNAKE_CASE							,     [
                {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
            ]							,     )
  
        import datasets
  
        UpperCamelCase__    :     int        =						datasets.load_dataset("hf-internal-testing/fixtures_image_utils"							,     "image"							,     split="test"		)
  
        # Accepts URL + PIL.Image + lists
        UpperCamelCase__    :     Tuple        =						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(
            SCREAMING_SNAKE_CASE							,     [
                [
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                ],
                [
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                ],
                [
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                ],
                [
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                ],
                [
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                ],
            ]							,     )
  
  
  
  
  
  
  
   @require_torch
   def       __lowercase      (	self      :     List[str]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     List[Any]        =						"hf-internal-testing/tiny-random-vit"
        UpperCamelCase__    :     List[str]        =						pipeline("image-classification"							,     model=SCREAMING_SNAKE_CASE		)
  
        UpperCamelCase__    :     List[str]        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
        self.assertEqual(
            nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]							,     )
  
        UpperCamelCase__    :     Optional[Any]        =						image_classifier(
            [
                "http://images.cocodataset.org/val2017/000000039769.jpg",
                "http://images.cocodataset.org/val2017/000000039769.jpg",
            ]							,     top_k=2							,     )
        self.assertEqual(
            nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
                [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
                [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
            ]							,     )
  
  
  
  
  
  
  
   @require_tf
   def       __lowercase      (	self      :     int		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     str        =						"hf-internal-testing/tiny-random-vit"
        UpperCamelCase__    :     int        =						pipeline("image-classification"							,     model=SCREAMING_SNAKE_CASE							,     framework="tf"		)
  
        UpperCamelCase__    :     List[Any]        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
        self.assertEqual(
            nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]							,     )
  
        UpperCamelCase__    :     int        =						image_classifier(
            [
                "http://images.cocodataset.org/val2017/000000039769.jpg",
                "http://images.cocodataset.org/val2017/000000039769.jpg",
            ]							,     top_k=2							,     )
        self.assertEqual(
            nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
                [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
                [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
            ]							,     )
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Optional[int]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Any        =						PreTrainedTokenizer()
  
        # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
        UpperCamelCase__    :     str        =						pipeline(
            "image-classification"							,     model="hf-internal-testing/tiny-random-vit"							,     tokenizer=SCREAMING_SNAKE_CASE		)
  
        self.assertIs(image_classifier.tokenizer							,     SCREAMING_SNAKE_CASE		)
  
  
  
  
  
  
  
   @slow
   @require_torch
   def       __lowercase      (	self      :     int		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Optional[Any]        =						pipeline("image-classification"							,     model="deepmind/vision-perceiver-conv"		)
        UpperCamelCase__    :     Any        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
        self.assertEqual(
            nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
                {"score": 0.4_3_8_5, "label": "tabby, tabby cat"},
                {"score": 0.3_2_1, "label": "tiger cat"},
                {"score": 0.0_5_0_2, "label": "Egyptian cat"},
                {"score": 0.0_1_3_7, "label": "crib, cot"},
                {"score": 0.0_0_7, "label": "radiator"},
            ]							,     )
  
        UpperCamelCase__    :     Optional[Any]        =						pipeline("image-classification"							,     model="deepmind/vision-perceiver-fourier"		)
        UpperCamelCase__    :     List[str]        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
        self.assertEqual(
            nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
                {"score": 0.5_6_5_8, "label": "tabby, tabby cat"},
                {"score": 0.1_3_0_9, "label": "tiger cat"},
                {"score": 0.0_7_2_2, "label": "Egyptian cat"},
                {"score": 0.0_7_0_7, "label": "remote control, remote"},
                {"score": 0.0_0_8_2, "label": "computer keyboard, keypad"},
            ]							,     )
  
        UpperCamelCase__    :     List[str]        =						pipeline("image-classification"							,     model="deepmind/vision-perceiver-learned"		)
        UpperCamelCase__    :     Optional[int]        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
        self.assertEqual(
            nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
                {"score": 0.3_0_2_2, "label": "tabby, tabby cat"},
                {"score": 0.2_3_6_2, "label": "Egyptian cat"},
                {"score": 0.1_8_5_6, "label": "tiger cat"},
                {"score": 0.0_3_2_4, "label": "remote control, remote"},
                {"score": 0.0_0_9_6, "label": "quilt, comforter, comfort, puff"},
            ]							,     )