



'''simple docstring'''

import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY

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


						'''simple docstring'''



						UpperCAmelCase							:   str									=		MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_				)     ->     str:
							lowerCAmelCase   =     hf_hub_download(
							    repo_id="""nateraw/video-demo"""     ,	filename="""archery.mp4"""     ,	repo_type="""dataset"""				)
							lowerCAmelCase   =     VideoClassificationPipeline(model=A_     ,	image_processor=A_     ,	top_k=2				)
							lowerCAmelCase   =     [
							    example_video_filepath,
							    """https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4""",
							]
							return video_classifier, examples


						def     __snake_case   ( self     ,	A_     ,	A_				)     ->     Union[str, Any]:
							for example in examples:
								lowerCAmelCase   =     video_classifier(A_				)

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


						@require_torch
						def     __snake_case   ( self				)     ->     int:
							lowerCAmelCase   =     """hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
							lowerCAmelCase   =     VideoMAEFeatureExtractor(
							    size={"""shortest_edge""": 10}     ,	crop_size={"""height""": 10, """width""": 10}				)
							lowerCAmelCase   =     pipeline(
							    """video-classification"""     ,	model=A_     ,	feature_extractor=A_     ,	frame_sampling_rate=4				)

							lowerCAmelCase   =     hf_hub_download(repo_id="""nateraw/video-demo"""     ,	filename="""archery.mp4"""     ,	repo_type="""dataset"""				)
							lowerCAmelCase   =     video_classifier(A_     ,	top_k=2				)
							self.assertEqual(
							    nested_simplify(A_     ,	decimals=4				)     ,	[{"""score""": 0.5_1_9_9, """label""": """LABEL_0"""}, {"""score""": 0.4_8_0_1, """label""": """LABEL_1"""}]     ,	)

							lowerCAmelCase   =     video_classifier(
							    [
							        video_file_path,
							        video_file_path,
							    ]     ,	top_k=2     ,	)
							self.assertEqual(
							    nested_simplify(A_     ,	decimals=4				)     ,	[
							        [{"""score""": 0.5_1_9_9, """label""": """LABEL_0"""}, {"""score""": 0.4_8_0_1, """label""": """LABEL_1"""}],
							        [{"""score""": 0.5_1_9_9, """label""": """LABEL_0"""}, {"""score""": 0.4_8_0_1, """label""": """LABEL_1"""}],
							    ]     ,	)




						@require_tf
						def     __snake_case   ( self				)     ->     str:
							pass




'''simple docstring'''

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						pass







class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						pass







class __snake_case:


						'''simple docstring'''



						def __init__( self				)     ->     int:
							lowerCAmelCase   =     [
							    [],
							    [],
							    [],
							]


						def     __snake_case   ( self     ,	A_     ,	A_				)     ->     None:
							try:
								if len(self.queues[priority]				) >= 100:
									raise OverflowError("""Maximum queue size is 100"""				)
								self.queues[priority].append(A_				)
							except IndexError:
								raise ValueError("""Valid priorities are 0, 1, and 2"""				)


						def     __snake_case   ( self				)     ->     int:
							for queue in self.queues:
								if queue:
									return queue.pop(0				)
							raise UnderFlowError("""All queues are empty"""				)




						def __str__( self				)     ->     str:
							return "\n".join(f'Priority {i}: {q}' for i, q in enumerate(self.queues				)				)







class __snake_case:


						'''simple docstring'''



						def __init__( self				)     ->     Dict:
							lowerCAmelCase   =     []


						def     __snake_case   ( self     ,	A_				)     ->     None:
							if len(self.queue				) == 100:
								raise OverFlowError("""Maximum queue size is 100"""				)
							self.queue.append(A_				)


						def     __snake_case   ( self				)     ->     int:
							if not self.queue:
								raise UnderFlowError("""The queue is empty"""				)
							else:
								lowerCAmelCase   =     min(self.queue				)
								self.queue.remove(A_				)
								return data




						def __str__( self				)     ->     str:
							return str(self.queue				)







def 		_snake_case		( )      ->						Tuple:







	"""simple docstring"""
	lowerCAmelCase   =     FixedPriorityQueue()
	fpq.enqueue(0							,		10							)
	fpq.enqueue(1							,		70							)
	fpq.enqueue(0							,		100							)
	fpq.enqueue(2							,		1							)
	fpq.enqueue(2							,		5							)
	fpq.enqueue(1							,		7							)
	fpq.enqueue(2							,		4							)
	fpq.enqueue(1							,		64							)
	fpq.enqueue(0							,		128							)
	print(_SCREAMING_SNAKE_CASE							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(_SCREAMING_SNAKE_CASE							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)






def 		_snake_case		( )      ->						Optional[Any]:







	"""simple docstring"""
	lowerCAmelCase   =     ElementPriorityQueue()
	epq.enqueue(10							)
	epq.enqueue(70							)
	epq.enqueue(100							)
	epq.enqueue(1							)
	epq.enqueue(5							)
	epq.enqueue(7							)
	epq.enqueue(4							)
	epq.enqueue(64							)
	epq.enqueue(128							)
	print(_SCREAMING_SNAKE_CASE							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(_SCREAMING_SNAKE_CASE							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)


if __name__ == "__main__":
				fixed_priority_queue()
				element_priority_queue()