






from __future__ import annotations

lowerCamelCase			:							int       ='''Alexander Joslin'''







def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			int:

     if number <= 0:
          UpperCamelCase__    :     Dict        =						f'n must be greater than 0. Got n = {number}'
          raise ValueError(__lowerCAmelCase    )

     UpperCamelCase__    :     str        =						[number + 1] * (number + 1)

     # starting position
     UpperCamelCase__    :     int        =						0
     for i in range(1      ,					__lowerCAmelCase    ):
          UpperCamelCase__    :     int        =						min(table[i + 1]      ,					table[i] + 1    )
          # check if out of bounds
          if i * 2 <= number:
               UpperCamelCase__    :     str        =						min(table[i * 2]      ,					table[i] + 1    )
          # check if out of bounds
          if i * 3 <= number:
               UpperCamelCase__    :     List[str]        =						min(table[i * 3]      ,					table[i] + 1    )
     return table[number]


if __name__ == "__main__":
 import doctest

 doctest.testmod()







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"},
          ]							,     )