
"""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 					A_			:



														"""simple docstring"""






														@staticmethod
														def    UpperCAmelCase__							(						*lowerCamelCase_						:Tuple  ,							**lowerCamelCase_						:str ):





																	"""simple docstring"""

																	pass

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



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_			   =     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
							SCREAMING_SNAKE_CASE_			   =     TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING





							def    UpperCAmelCase__							(						self						:List[Any]  ,							lowerCamelCase_						:Dict  ,							lowerCamelCase_						:str  ,							lowerCamelCase_						:List[Any] ):





										"""simple docstring"""

										lowerCamelCase__			:  Any  =ImageClassificationPipeline(model=lowerCamelCase_  ,							image_processor=lowerCamelCase_  ,							top_k=2 )
										lowerCamelCase__			:  int  =[
										    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' ),
										    'http://images.cocodataset.org/val2017/000000039769.jpg',
										]
										return image_classifier, examples





							def    UpperCAmelCase__							(						self						:Any  ,							lowerCamelCase_						:Union[str, Any]  ,							lowerCamelCase_						:Any ):





										"""simple docstring"""

										lowerCamelCase__			:  List[str]  =image_classifier('./tests/fixtures/tests_samples/COCO/000000039769.png' )

										self.assertEqual(
										    lowerCamelCase_  ,							[
										        {'score': ANY(lowerCamelCase_ ), 'label': ANY(lowerCamelCase_ )},
										        {'score': ANY(lowerCamelCase_ ), 'label': ANY(lowerCamelCase_ )},
										    ]  ,							)

										import datasets

										lowerCamelCase__			:  List[Any]  =datasets.load_dataset('hf-internal-testing/fixtures_image_utils'  ,							'image'  ,							split='test' )

										# Accepts URL + PIL.Image + lists
										lowerCamelCase__			:  Any  =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(
										    lowerCamelCase_  ,							[
										        [
										            {'score': ANY(lowerCamelCase_ ), 'label': ANY(lowerCamelCase_ )},
										            {'score': ANY(lowerCamelCase_ ), 'label': ANY(lowerCamelCase_ )},
										        ],
										        [
										            {'score': ANY(lowerCamelCase_ ), 'label': ANY(lowerCamelCase_ )},
										            {'score': ANY(lowerCamelCase_ ), 'label': ANY(lowerCamelCase_ )},
										        ],
										        [
										            {'score': ANY(lowerCamelCase_ ), 'label': ANY(lowerCamelCase_ )},
										            {'score': ANY(lowerCamelCase_ ), 'label': ANY(lowerCamelCase_ )},
										        ],
										        [
										            {'score': ANY(lowerCamelCase_ ), 'label': ANY(lowerCamelCase_ )},
										            {'score': ANY(lowerCamelCase_ ), 'label': ANY(lowerCamelCase_ )},
										        ],
										        [
										            {'score': ANY(lowerCamelCase_ ), 'label': ANY(lowerCamelCase_ )},
										            {'score': ANY(lowerCamelCase_ ), 'label': ANY(lowerCamelCase_ )},
										        ],
										    ]  ,							)





							@require_torch
							def    UpperCAmelCase__							(						self						:Union[str, Any] ):





										"""simple docstring"""

										lowerCamelCase__			:  Optional[int]  ='hf-internal-testing/tiny-random-vit'
										lowerCamelCase__			:  Any  =pipeline('image-classification'  ,							model=lowerCamelCase_ )

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

										lowerCamelCase__			:  Tuple  =image_classifier(
										    [
										        'http://images.cocodataset.org/val2017/000000039769.jpg',
										        'http://images.cocodataset.org/val2017/000000039769.jpg',
										    ]  ,							top_k=2  ,							)
										self.assertEqual(
										    nested_simplify(lowerCamelCase_  ,							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    UpperCAmelCase__							(						self						:int ):





										"""simple docstring"""

										lowerCamelCase__			:  Optional[Any]  ='hf-internal-testing/tiny-random-vit'
										lowerCamelCase__			:  Union[str, Any]  =pipeline('image-classification'  ,							model=lowerCamelCase_  ,							framework='tf' )

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

										lowerCamelCase__			:  Optional[Any]  =image_classifier(
										    [
										        'http://images.cocodataset.org/val2017/000000039769.jpg',
										        'http://images.cocodataset.org/val2017/000000039769.jpg',
										    ]  ,							top_k=2  ,							)
										self.assertEqual(
										    nested_simplify(lowerCamelCase_  ,							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    UpperCAmelCase__							(						self						:Tuple ):





										"""simple docstring"""

										lowerCamelCase__			:  Optional[int]  =PreTrainedTokenizer()

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

										self.assertIs(image_classifier.tokenizer  ,							lowerCamelCase_ )





							@slow
							@require_torch
							def    UpperCAmelCase__							(						self						:Tuple ):





										"""simple docstring"""

										lowerCamelCase__			:  Optional[Any]  =pipeline('image-classification'  ,							model='deepmind/vision-perceiver-conv' )
										lowerCamelCase__			:  Any  =image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg' )
										self.assertEqual(
										    nested_simplify(lowerCamelCase_  ,							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'},
										    ]  ,							)

										lowerCamelCase__			:  Any  =pipeline('image-classification'  ,							model='deepmind/vision-perceiver-fourier' )
										lowerCamelCase__			:  Dict  =image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg' )
										self.assertEqual(
										    nested_simplify(lowerCamelCase_  ,							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'},
										    ]  ,							)

										lowerCamelCase__			:  Optional[int]  =pipeline('image-classification'  ,							model='deepmind/vision-perceiver-learned' )
										lowerCamelCase__			:  Tuple  =image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg' )
										self.assertEqual(
										    nested_simplify(lowerCamelCase_  ,							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'},
										    ]  ,							)








"""simple docstring"""


# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
from queue import Queue






def      lowerCAmelCase_     (	snake_case_	:    List[Any]    )     ->Optional[int]:
			lowerCamelCase__			:  Optional[int]  =Queue()
			lowerCamelCase__			:  Any  =[False] * len(snake_case_    )
			lowerCamelCase__			:  Optional[Any]  =[-1] * len(snake_case_    )

			def bfs():
						while not queue.empty():
									lowerCamelCase__			:  List[Any]  =queue.get()
									lowerCamelCase__			:  int  =True

									for neighbour in graph[u]:
												if neighbour == u:
															return False

												if color[neighbour] == -1:
															lowerCamelCase__			:  Union[str, Any]  =1 - color[u]
															queue.put(snake_case_    )

												elif color[neighbour] == color[u]:
															return False

						return True

			for i in range(len(snake_case_    )    ):
						if not visited[i]:
									queue.put(snake_case_    )
									lowerCamelCase__			:  Tuple  =0
									if bfs() is False:
												return False

			return True


if __name__ == "__main__":
							# Adjacency List of graph
							print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))