



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 	__snake_case :







								'''simple docstring'''






								@staticmethod
								def _a (							*a_					,					**a_						):
												pass

@is_pipeline_test
@require_torch_or_tf
@require_vision
class 	__snake_case (				unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__			: str             =   MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
							UpperCamelCase__			: Any             =   TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


							def _a (							self					,					a_					,					a_					,					a_						):
											a__			 =						ImageClassificationPipeline(model=a_					,					image_processor=a_					,					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 _a (							self					,					a_					,					a_						):
											a__			 =						image_classifier("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)

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

											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(
											    a_					,					[
											        [
											            {"""score""": ANY(a_						), """label""": ANY(a_						)},
											            {"""score""": ANY(a_						), """label""": ANY(a_						)},
											        ],
											        [
											            {"""score""": ANY(a_						), """label""": ANY(a_						)},
											            {"""score""": ANY(a_						), """label""": ANY(a_						)},
											        ],
											        [
											            {"""score""": ANY(a_						), """label""": ANY(a_						)},
											            {"""score""": ANY(a_						), """label""": ANY(a_						)},
											        ],
											        [
											            {"""score""": ANY(a_						), """label""": ANY(a_						)},
											            {"""score""": ANY(a_						), """label""": ANY(a_						)},
											        ],
											        [
											            {"""score""": ANY(a_						), """label""": ANY(a_						)},
											            {"""score""": ANY(a_						), """label""": ANY(a_						)},
											        ],
											    ]					,					)


							@require_torch
							def _a (							self						):
											a__			 =						"""hf-internal-testing/tiny-random-vit"""
											a__			 =						pipeline("""image-classification"""					,					model=a_						)

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

											a__			 =						image_classifier(
											    [
											        """http://images.cocodataset.org/val2017/000000039769.jpg""",
											        """http://images.cocodataset.org/val2017/000000039769.jpg""",
											    ]					,					top_k=2					,					)
											self.assertEqual(
											    nested_simplify(a_					,					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 _a (							self						):
											a__			 =						"""hf-internal-testing/tiny-random-vit"""
											a__			 =						pipeline("""image-classification"""					,					model=a_					,					framework="""tf"""						)

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

											a__			 =						image_classifier(
											    [
											        """http://images.cocodataset.org/val2017/000000039769.jpg""",
											        """http://images.cocodataset.org/val2017/000000039769.jpg""",
											    ]					,					top_k=2					,					)
											self.assertEqual(
											    nested_simplify(a_					,					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 _a (							self						):
											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=a_						)

											self.assertIs(image_classifier.tokenizer					,					a_						)







							@slow
							@require_torch
							def _a (							self						):
											# 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(a_					,					decimals=4						)					,					[
											        {"""score""": 0.4_385, """label""": """tabby, tabby cat"""},
											        {"""score""": 0.321, """label""": """tiger cat"""},
											        {"""score""": 0.0_502, """label""": """Egyptian cat"""},
											        {"""score""": 0.0_137, """label""": """crib, cot"""},
											        {"""score""": 0.007, """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(a_					,					decimals=4						)					,					[
											        {"""score""": 0.5_658, """label""": """tabby, tabby cat"""},
											        {"""score""": 0.1_309, """label""": """tiger cat"""},
											        {"""score""": 0.0_722, """label""": """Egyptian cat"""},
											        {"""score""": 0.0_707, """label""": """remote control, remote"""},
											        {"""score""": 0.0_082, """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(a_					,					decimals=4						)					,					[
											        {"""score""": 0.3_022, """label""": """tabby, tabby cat"""},
											        {"""score""": 0.2_362, """label""": """Egyptian cat"""},
											        {"""score""": 0.1_856, """label""": """tiger cat"""},
											        {"""score""": 0.0_324, """label""": """remote control, remote"""},
											        {"""score""": 0.0_096, """label""": """quilt, comforter, comfort, puff"""},
											    ]					,					)






from __future__ import annotations

def      A_ (		__a       :			list[int | float]						):

				"""simple docstring"""
				if len(__a						) == 0:
								raise ValueError("""find_max() arg is an empty sequence"""						)
				a__			 =						nums[0]
				for x in nums:
								if x > max_num:
												a__			 =						x
				return max_num


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)

