





"""simple docstring"""




import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
		from PIL import Image
else:


		class        __UpperCamelCase				:






									@staticmethod
									def     __a   (   *lowerCAmelCase__      ,					**lowerCAmelCase__      )	-> Optional[Any]:
										pass


@is_pipeline_test
@require_vision
class        __UpperCamelCase				(						unittest.TestCase						):






							@require_torch
							def     __a   (   self      )	-> Dict:
								a      :		List[Any]	=				pipeline(
								    model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification"      ,					)
								a      :		Dict	=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
								a      :		List[str]	=				image_classifier(lowerCAmelCase__      ,					candidate_labels=["a", "b", "c"]      )

								# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
								# python and torch versions.
								self.assertIn(
								    nested_simplify(lowerCAmelCase__      )      ,					[
								        [{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}],
								        [{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "c"}, {"score": 0.333, "label": "b"}],
								    ]      ,					)

								a      :		Optional[int]	=				image_classifier([image] * 5      ,					candidate_labels=["A", "B", "C"]      ,					batch_size=2      )
								self.assertEqual(
								    nested_simplify(lowerCAmelCase__      )      ,					[
								        [
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								        ],
								        [
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								        ],
								        [
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								        ],
								        [
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								        ],
								        [
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								        ],
								    ]      ,					)






							@require_tf
							def     __a   (   self      )	-> int:
								a      :		Tuple	=				pipeline(
								    model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification"      ,					framework="tf"      )
								a      :		List[Any]	=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
								a      :		int	=				image_classifier(lowerCAmelCase__      ,					candidate_labels=["a", "b", "c"]      )

								self.assertEqual(
								    nested_simplify(lowerCAmelCase__      )      ,					[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}]      ,					)

								a      :		List[str]	=				image_classifier([image] * 5      ,					candidate_labels=["A", "B", "C"]      ,					batch_size=2      )
								self.assertEqual(
								    nested_simplify(lowerCAmelCase__      )      ,					[
								        [
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								        ],
								        [
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								        ],
								        [
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								        ],
								        [
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								        ],
								        [
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								            {"score": 0.333, "label": ANY(lowerCAmelCase__      )},
								        ],
								    ]      ,					)






							@slow
							@require_torch
							def     __a   (   self      )	-> Union[str, Any]:
								a      :		Optional[int]	=				pipeline(
								    task="zero-shot-image-classification"      ,					model="openai/clip-vit-base-patch32"      ,					)
								# This is an image of 2 cats with remotes and no planes
								a      :		List[str]	=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
								a      :		str	=				image_classifier(lowerCAmelCase__      ,					candidate_labels=["cat", "plane", "remote"]      )

								self.assertEqual(
								    nested_simplify(lowerCAmelCase__      )      ,					[
								        {"score": 0.511, "label": "remote"},
								        {"score": 0.485, "label": "cat"},
								        {"score": 0.004, "label": "plane"},
								    ]      ,					)

								a      :		List[str]	=				image_classifier([image] * 5      ,					candidate_labels=["cat", "plane", "remote"]      ,					batch_size=2      )
								self.assertEqual(
								    nested_simplify(lowerCAmelCase__      )      ,					[
								        [
								            {"score": 0.511, "label": "remote"},
								            {"score": 0.485, "label": "cat"},
								            {"score": 0.004, "label": "plane"},
								        ],
								    ]
								    * 5      ,					)






							@slow
							@require_tf
							def     __a   (   self      )	-> Optional[Any]:
								a      :		List[str]	=				pipeline(
								    task="zero-shot-image-classification"      ,					model="openai/clip-vit-base-patch32"      ,					framework="tf"      )
								# This is an image of 2 cats with remotes and no planes
								a      :		Dict	=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
								a      :		Optional[Any]	=				image_classifier(lowerCAmelCase__      ,					candidate_labels=["cat", "plane", "remote"]      )
								self.assertEqual(
								    nested_simplify(lowerCAmelCase__      )      ,					[
								        {"score": 0.511, "label": "remote"},
								        {"score": 0.485, "label": "cat"},
								        {"score": 0.004, "label": "plane"},
								    ]      ,					)

								a      :		Optional[int]	=				image_classifier([image] * 5      ,					candidate_labels=["cat", "plane", "remote"]      ,					batch_size=2      )
								self.assertEqual(
								    nested_simplify(lowerCAmelCase__      )      ,					[
								        [
								            {"score": 0.511, "label": "remote"},
								            {"score": 0.485, "label": "cat"},
								            {"score": 0.004, "label": "plane"},
								        ],
								    ]
								    * 5      ,					)









"""simple docstring"""




def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list					) ->int:






	'''simple docstring'''






	if not grid or not grid[0]:
		raise TypeError("The grid does not contain the appropriate information"					)

	for cell_n in range(1    ,       len(grid[0]					)					):
		grid[0][cell_n] += grid[0][cell_n - 1]
	a      :		Union[str, Any]	=				grid[0]

	for row_n in range(1    ,       len(_lowercase					)					):
		a      :		Optional[Any]	=				grid[row_n]
		a      :		Tuple	=				fill_row(_lowercase    ,       _lowercase					)
		a      :		List[Any]	=				grid[row_n]

	return grid[-1][-1]






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list    ,       _lowercase						:    list					) ->list:






	'''simple docstring'''






	current_row[0] += row_above[0]
	for cell_n in range(1    ,       len(_lowercase					)					):
		current_row[cell_n] += min(current_row[cell_n - 1]    ,       row_above[cell_n]					)

	return current_row


if __name__ == "__main__":
		import doctest

		doctest.testmod()


