





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       (					*_A							,      **_A	):






								'''simple docstring'''







								pass

@is_pipeline_test
@require_torch_or_tf
@require_vision
class 			A_ (unittest.TestCase    ):
	UpperCAmelCase__      =    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
	UpperCAmelCase__      =    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
	def   _lowercase       (					self							,      _A							,      _A							,      _A	):






				'''simple docstring'''







				UpperCAmelCase     	=       ImageClassificationPipeline(model=_A							,      image_processor=_A							,      top_k=2	)
				UpperCAmelCase     	=       [
				    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	),
				    '''http://images.cocodataset.org/val2017/000000039769.jpg''',
				]
				return image_classifier, examples
	def   _lowercase       (					self							,      _A							,      _A	):






				'''simple docstring'''







				UpperCAmelCase     	=       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

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

				# Accepts URL + PIL.Image + lists
				UpperCAmelCase     	=       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   _lowercase       (					self	):






				'''simple docstring'''







				UpperCAmelCase     	=       '''hf-internal-testing/tiny-random-vit'''
				UpperCAmelCase     	=       pipeline('''image-classification'''							,      model=_A	)

				UpperCAmelCase     	=       image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''	)
				self.assertEqual(
				    nested_simplify(_A							,      decimals=4	)							,      [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}]							,      )

				UpperCAmelCase     	=       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''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
				        [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
				    ]							,      )
	@require_tf
	def   _lowercase       (					self	):






				'''simple docstring'''







				UpperCAmelCase     	=       '''hf-internal-testing/tiny-random-vit'''
				UpperCAmelCase     	=       pipeline('''image-classification'''							,      model=_A							,      framework='''tf'''	)

				UpperCAmelCase     	=       image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''	)
				self.assertEqual(
				    nested_simplify(_A							,      decimals=4	)							,      [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}]							,      )

				UpperCAmelCase     	=       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''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
				        [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
				    ]							,      )
	def   _lowercase       (					self	):






				'''simple docstring'''







				UpperCAmelCase     	=       PreTrainedTokenizer()

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

				self.assertIs(image_classifier.tokenizer							,      _A	)







	@slow
	@require_torch
	def   _lowercase       (					self	):






				'''simple docstring'''







				UpperCAmelCase     	=       pipeline('''image-classification'''							,      model='''deepmind/vision-perceiver-conv'''	)
				UpperCAmelCase     	=       image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''	)
				self.assertEqual(
				    nested_simplify(_A							,      decimals=4	)							,      [
				        {'''score''': 0.43_85, '''label''': '''tabby, tabby cat'''},
				        {'''score''': 0.3_21, '''label''': '''tiger cat'''},
				        {'''score''': 0.05_02, '''label''': '''Egyptian cat'''},
				        {'''score''': 0.01_37, '''label''': '''crib, cot'''},
				        {'''score''': 0.0_07, '''label''': '''radiator'''},
				    ]							,      )

				UpperCAmelCase     	=       pipeline('''image-classification'''							,      model='''deepmind/vision-perceiver-fourier'''	)
				UpperCAmelCase     	=       image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''	)
				self.assertEqual(
				    nested_simplify(_A							,      decimals=4	)							,      [
				        {'''score''': 0.56_58, '''label''': '''tabby, tabby cat'''},
				        {'''score''': 0.13_09, '''label''': '''tiger cat'''},
				        {'''score''': 0.07_22, '''label''': '''Egyptian cat'''},
				        {'''score''': 0.07_07, '''label''': '''remote control, remote'''},
				        {'''score''': 0.00_82, '''label''': '''computer keyboard, keypad'''},
				    ]							,      )

				UpperCAmelCase     	=       pipeline('''image-classification'''							,      model='''deepmind/vision-perceiver-learned'''	)
				UpperCAmelCase     	=       image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''	)
				self.assertEqual(
				    nested_simplify(_A							,      decimals=4	)							,      [
				        {'''score''': 0.30_22, '''label''': '''tabby, tabby cat'''},
				        {'''score''': 0.23_62, '''label''': '''Egyptian cat'''},
				        {'''score''': 0.18_56, '''label''': '''tiger cat'''},
				        {'''score''': 0.03_24, '''label''': '''remote control, remote'''},
				        {'''score''': 0.00_96, '''label''': '''quilt, comforter, comfort, puff'''},
				    ]							,      )










from __future__ import annotations





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__				)						->       bool:







			'''simple docstring'''

			UpperCAmelCase     	=       42
			UpperCAmelCase     	=       list(UpperCamelCase__				)
			UpperCAmelCase	,    UpperCAmelCase     	=       _vector[:-1], int(_vector[-1]				)
			UpperCAmelCase     	=       [int(UpperCamelCase__				) for digit in __vector]

			vector.reverse()
			for i, digit in enumerate(UpperCamelCase__				):
						if i & 1 == 0:
									UpperCAmelCase     	=       digit * 2
									if doubled > 9:
												doubled -= 9
									check_digit += doubled
						else:
									check_digit += digit

			return check_digit % 10 == 0


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				assert is_luhn("79927398713")
				assert not is_luhn("79927398714")



