






"""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 				__SCREAMING_SNAKE_CASE    :


													@staticmethod
													def     _UpperCamelCase							(      *snake_case   :    List[Any]      ,     **snake_case   :    Union[str, Any]				):
																			'''simple docstring'''





																			pass






@is_pipeline_test
@require_torch_or_tf
@require_vision
class 				__SCREAMING_SNAKE_CASE    (					unittest.TestCase		):
							snake_case_  =				MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
							snake_case_  =				TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


							def     _UpperCamelCase							(      self   :    Tuple      ,     snake_case   :    Optional[Any]      ,     snake_case   :    Optional[Any]      ,     snake_case   :    str				):
													'''simple docstring'''





													A__    :	Dict			=	ImageClassificationPipeline(model=snake_case      ,     image_processor=snake_case      ,     top_k=2				)
													A__    :	Optional[Any]			=	[
													    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				),
													    """http://images.cocodataset.org/val2017/000000039769.jpg""",
													]
													return image_classifier, examples


							def     _UpperCamelCase							(      self   :    str      ,     snake_case   :    int      ,     snake_case   :    Optional[Any]				):
													'''simple docstring'''





													A__    :	int			=	image_classifier("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)

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

													import datasets

													A__    :	List[str]			=	datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""      ,     """image"""      ,     split="""test"""				)

													# Accepts URL + PIL.Image + lists
													A__    :	str			=	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(
													    snake_case      ,     [
													        [
													            {"""score""": ANY(snake_case				), """label""": ANY(snake_case				)},
													            {"""score""": ANY(snake_case				), """label""": ANY(snake_case				)},
													        ],
													        [
													            {"""score""": ANY(snake_case				), """label""": ANY(snake_case				)},
													            {"""score""": ANY(snake_case				), """label""": ANY(snake_case				)},
													        ],
													        [
													            {"""score""": ANY(snake_case				), """label""": ANY(snake_case				)},
													            {"""score""": ANY(snake_case				), """label""": ANY(snake_case				)},
													        ],
													        [
													            {"""score""": ANY(snake_case				), """label""": ANY(snake_case				)},
													            {"""score""": ANY(snake_case				), """label""": ANY(snake_case				)},
													        ],
													        [
													            {"""score""": ANY(snake_case				), """label""": ANY(snake_case				)},
													            {"""score""": ANY(snake_case				), """label""": ANY(snake_case				)},
													        ],
													    ]      ,     )


							@require_torch
							def     _UpperCamelCase							(      self   :    Union[str, Any]				):
													'''simple docstring'''





													A__    :	Optional[Any]			=	"""hf-internal-testing/tiny-random-vit"""
													A__    :	Optional[Any]			=	pipeline("""image-classification"""      ,     model=snake_case				)

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

													A__    :	Any			=	image_classifier(
													    [
													        """http://images.cocodataset.org/val2017/000000039769.jpg""",
													        """http://images.cocodataset.org/val2017/000000039769.jpg""",
													    ]      ,     top_k=2      ,     )
													self.assertEqual(
													    nested_simplify(snake_case      ,     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   :    Optional[int]				):
													'''simple docstring'''





													A__    :	Optional[Any]			=	"""hf-internal-testing/tiny-random-vit"""
													A__    :	Tuple			=	pipeline("""image-classification"""      ,     model=snake_case      ,     framework="""tf"""				)

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

													A__    :	List[str]			=	image_classifier(
													    [
													        """http://images.cocodataset.org/val2017/000000039769.jpg""",
													        """http://images.cocodataset.org/val2017/000000039769.jpg""",
													    ]      ,     top_k=2      ,     )
													self.assertEqual(
													    nested_simplify(snake_case      ,     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   :    str				):
													'''simple docstring'''





													A__    :	Dict			=	PreTrainedTokenizer()

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

													self.assertIs(image_classifier.tokenizer      ,     snake_case				)


							@slow
							@require_torch
							def     _UpperCamelCase							(      self   :    Optional[Any]				):
													'''simple docstring'''





													A__    :	Tuple			=	pipeline("""image-classification"""      ,     model="""deepmind/vision-perceiver-conv"""				)
													A__    :	Optional[int]			=	image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""				)
													self.assertEqual(
													    nested_simplify(snake_case      ,     decimals=4				)      ,     [
													        {"""score""": 0.4385, """label""": """tabby, tabby cat"""},
													        {"""score""": 0.321, """label""": """tiger cat"""},
													        {"""score""": 0.0502, """label""": """Egyptian cat"""},
													        {"""score""": 0.0137, """label""": """crib, cot"""},
													        {"""score""": 0.007, """label""": """radiator"""},
													    ]      ,     )

													A__    :	str			=	pipeline("""image-classification"""      ,     model="""deepmind/vision-perceiver-fourier"""				)
													A__    :	List[Any]			=	image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""				)
													self.assertEqual(
													    nested_simplify(snake_case      ,     decimals=4				)      ,     [
													        {"""score""": 0.5658, """label""": """tabby, tabby cat"""},
													        {"""score""": 0.1309, """label""": """tiger cat"""},
													        {"""score""": 0.0722, """label""": """Egyptian cat"""},
													        {"""score""": 0.0707, """label""": """remote control, remote"""},
													        {"""score""": 0.0082, """label""": """computer keyboard, keypad"""},
													    ]      ,     )

													A__    :	Tuple			=	pipeline("""image-classification"""      ,     model="""deepmind/vision-perceiver-learned"""				)
													A__    :	List[Any]			=	image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""				)
													self.assertEqual(
													    nested_simplify(snake_case      ,     decimals=4				)      ,     [
													        {"""score""": 0.3022, """label""": """tabby, tabby cat"""},
													        {"""score""": 0.2362, """label""": """Egyptian cat"""},
													        {"""score""": 0.1856, """label""": """tiger cat"""},
													        {"""score""": 0.0324, """label""": """remote control, remote"""},
													        {"""score""": 0.0096, """label""": """quilt, comforter, comfort, puff"""},
													    ]      ,     )














"""simple docstring"""



def   _lowerCAmelCase		(				UpperCAmelCase__							:       float,    UpperCAmelCase__							:       float,    UpperCAmelCase__							:       float   )		->list[int]:
						if hue < 0 or hue > 3_6_0:
												raise Exception("""hue should be between 0 and 360"""   )

						if saturation < 0 or saturation > 1:
												raise Exception("""saturation should be between 0 and 1"""   )

						if value < 0 or value > 1:
												raise Exception("""value should be between 0 and 1"""   )

						A__    :	int			=	value * saturation
						A__    :	Any			=	hue / 6_0
						A__    :	Any			=	chroma * (1 - abs(hue_section % 2 - 1   ))
						A__    :	Optional[Any]			=	value - chroma

						if hue_section >= 0 and hue_section <= 1:
												A__    :	Dict			=	round(2_5_5 * (chroma + match_value)   )
												A__    :	Optional[Any]			=	round(2_5_5 * (second_largest_component + match_value)   )
												A__    :	Any			=	round(2_5_5 * (match_value)   )
						elif hue_section > 1 and hue_section <= 2:
												A__    :	Optional[int]			=	round(2_5_5 * (second_largest_component + match_value)   )
												A__    :	Union[str, Any]			=	round(2_5_5 * (chroma + match_value)   )
												A__    :	Union[str, Any]			=	round(2_5_5 * (match_value)   )
						elif hue_section > 2 and hue_section <= 3:
												A__    :	Tuple			=	round(2_5_5 * (match_value)   )
												A__    :	List[str]			=	round(2_5_5 * (chroma + match_value)   )
												A__    :	Union[str, Any]			=	round(2_5_5 * (second_largest_component + match_value)   )
						elif hue_section > 3 and hue_section <= 4:
												A__    :	Any			=	round(2_5_5 * (match_value)   )
												A__    :	Dict			=	round(2_5_5 * (second_largest_component + match_value)   )
												A__    :	List[Any]			=	round(2_5_5 * (chroma + match_value)   )
						elif hue_section > 4 and hue_section <= 5:
												A__    :	Any			=	round(2_5_5 * (second_largest_component + match_value)   )
												A__    :	Dict			=	round(2_5_5 * (match_value)   )
												A__    :	Tuple			=	round(2_5_5 * (chroma + match_value)   )
						else:
												A__    :	Tuple			=	round(2_5_5 * (chroma + match_value)   )
												A__    :	Optional[int]			=	round(2_5_5 * (match_value)   )
												A__    :	str			=	round(2_5_5 * (second_largest_component + match_value)   )

						return [red, green, blue]




def   _lowerCAmelCase		(				UpperCAmelCase__							:       int,    UpperCAmelCase__							:       int,    UpperCAmelCase__							:       int   )		->list[float]:
						if red < 0 or red > 2_5_5:
												raise Exception("""red should be between 0 and 255"""   )

						if green < 0 or green > 2_5_5:
												raise Exception("""green should be between 0 and 255"""   )

						if blue < 0 or blue > 2_5_5:
												raise Exception("""blue should be between 0 and 255"""   )

						A__    :	int			=	red / 2_5_5
						A__    :	List[str]			=	green / 2_5_5
						A__    :	Optional[int]			=	blue / 2_5_5
						A__    :	Optional[int]			=	max(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__   )
						A__    :	Tuple			=	value - min(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__   )
						A__    :	Any			=	0 if value == 0 else chroma / value

						if chroma == 0:
												A__    :	Optional[Any]			=	0.0
						elif value == float_red:
												A__    :	Optional[Any]			=	6_0 * (0 + (float_green - float_blue) / chroma)
						elif value == float_green:
												A__    :	Tuple			=	6_0 * (2 + (float_blue - float_red) / chroma)
						else:
												A__    :	int			=	6_0 * (4 + (float_red - float_green) / chroma)

						A__    :	Optional[int]			=	(hue + 3_6_0) % 3_6_0

						return [hue, saturation, value]




def   _lowerCAmelCase		(				UpperCAmelCase__							:       list[float],    UpperCAmelCase__							:       list[float]   )		->bool:
						A__    :	Optional[Any]			=	abs(hsv_a[0] - hsv_a[0]   ) < 0.2
						A__    :	str			=	abs(hsv_a[1] - hsv_a[1]   ) < 0.002
						A__    :	Optional[int]			=	abs(hsv_a[2] - hsv_a[2]   ) < 0.002

						return check_hue and check_saturation and check_value






