

"""simple docstring"""




def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
					"""simple docstring"""
					if not isinstance(lowercase__    ,							lowercase__   ):
										raise ValueError("double_factorial() only accepts integral values"   )
					if n < 0:
										raise ValueError("double_factorial() not defined for negative values"   )
					return 1 if n <= 1 else n * double_factorial(n - 2   )


if __name__ == "__main__":
	import doctest

	doctest.testmod()








"""simple docstring"""




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     __UpperCamelCase				:


			@staticmethod
			def       SCREAMING_SNAKE_CASE__     (*__SCREAMING_SNAKE_CASE				:	Optional[Any]  ,  **__SCREAMING_SNAKE_CASE				:	Any):
								pass





@is_pipeline_test
@require_torch_or_tf
@require_vision
class     __UpperCamelCase				(     unittest.TestCase		):
		SCREAMING_SNAKE_CASE              =					MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
		SCREAMING_SNAKE_CASE              =					TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


		def       SCREAMING_SNAKE_CASE__     (self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
							A    =      ImageClassificationPipeline(model=__SCREAMING_SNAKE_CASE  ,  image_processor=__SCREAMING_SNAKE_CASE  ,  top_k=2)
							A    =      [
							    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
							    "http://images.cocodataset.org/val2017/000000039769.jpg",
							]
							return image_classifier, examples


		def       SCREAMING_SNAKE_CASE__     (self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	Dict):
							A    =      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

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

							# Accepts URL + PIL.Image + lists
							A    =      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       SCREAMING_SNAKE_CASE__     (self				:	str):
							A    =      "hf-internal-testing/tiny-random-vit"
							A    =      pipeline("image-classification"  ,  model=__SCREAMING_SNAKE_CASE)

							A    =      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}]  ,  )

							A    =      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       SCREAMING_SNAKE_CASE__     (self				:	List[str]):
							A    =      "hf-internal-testing/tiny-random-vit"
							A    =      pipeline("image-classification"  ,  model=__SCREAMING_SNAKE_CASE  ,  framework="tf")

							A    =      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}]  ,  )

							A    =      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       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
							A    =      PreTrainedTokenizer()

							# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
							A    =      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       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
							# 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
							A    =      pipeline("image-classification"  ,  model="deepmind/vision-perceiver-conv")
							A    =      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"},
							    ]  ,  )

							A    =      pipeline("image-classification"  ,  model="deepmind/vision-perceiver-fourier")
							A    =      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"},
							    ]  ,  )

							A    =      pipeline("image-classification"  ,  model="deepmind/vision-perceiver-learned")
							A    =      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"},
							    ]  ,  )





