


"""simple docstring"""


from __future__ import annotations

import math

UpperCamelCase						:				int             =      "2020.9.26"
UpperCamelCase						:				Union[str, Any]             =      "xcodz-dot, cclaus, dhruvmanila"





def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)      ->		Tuple:







		"""simple docstring"""





		if not all(isinstance(_lowerCamelCase ,			(float, int)	) for val in locals().values()	):
				A					       = f'Input values must either be float or int: {list(locals().values()	)}'
				raise TypeError(_lowerCamelCase	)
		A					       = ((x * distance) / (z + distance)) * scale
		A					       = ((y * distance) / (z + distance)) * scale
		return projected_x, projected_y





def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)      ->		Union[str, Any]:







		"""simple docstring"""





		if not isinstance(_lowerCamelCase ,			_lowerCamelCase	):
				raise TypeError('Axis must be a str'	)
		A					       = locals()
		del input_variables["axis"]
		if not all(isinstance(_lowerCamelCase ,			(float, int)	) for val in input_variables.values()	):
				A					       = (
				    'Input values except axis must either be float or int: '
				    f'{list(input_variables.values()	)}'
				)
				raise TypeError(_lowerCamelCase	)
		A					       = (angle % 360) / 450 * 180 / math.pi
		if axis == "z":
				A					       = x * math.cos(_lowerCamelCase	) - y * math.sin(_lowerCamelCase	)
				A					       = y * math.cos(_lowerCamelCase	) + x * math.sin(_lowerCamelCase	)
				A					       = z
		elif axis == "x":
				A					       = y * math.cos(_lowerCamelCase	) - z * math.sin(_lowerCamelCase	)
				A					       = z * math.cos(_lowerCamelCase	) + y * math.sin(_lowerCamelCase	)
				A					       = x
		elif axis == "y":
				A					       = x * math.cos(_lowerCamelCase	) - z * math.sin(_lowerCamelCase	)
				A					       = z * math.cos(_lowerCamelCase	) + x * math.sin(_lowerCamelCase	)
				A					       = y
		else:
				raise ValueError('not a valid axis, choose one of \'x\', \'y\', \'z\''	)

		return new_x, new_y, new_z


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(F"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
	print(F"""{rotate(1.0, 2.0, 3.0, 'y', 90.0) = }""")









"""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 			lowerCamelCase__						(   unittest.TestCase			):
						lowerCAmelCase       =     MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING







						def __a						(							self   :    Dict			,			_lowercase   :    int			,			_lowercase   :    Any			,			_lowercase   :    int		):
								A					       = hf_hub_download(
								    repo_id='nateraw/video-demo'			,			filename='archery.mp4'			,			repo_type='dataset'		)
								A					       = VideoClassificationPipeline(model=_lowercase			,			image_processor=_lowercase			,			top_k=2		)
								A					       = [
								    example_video_filepath,
								    'https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4',
								]
								return video_classifier, examples







						def __a						(							self   :    Tuple			,			_lowercase   :    Union[str, Any]			,			_lowercase   :    List[Any]		):
								for example in examples:
										A					       = video_classifier(_lowercase		)

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







						@require_torch
						def __a						(							self   :    str		):
								A					       = 'hf-internal-testing/tiny-random-VideoMAEForVideoClassification'
								A					       = VideoMAEFeatureExtractor(
								    size={'shortest_edge': 10}			,			crop_size={'height': 10, 'width': 10}		)
								A					       = pipeline(
								    'video-classification'			,			model=_lowercase			,			feature_extractor=_lowercase			,			frame_sampling_rate=4		)

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

								A					       = video_classifier(
								    [
								        video_file_path,
								        video_file_path,
								    ]			,			top_k=2			,			)
								self.assertEqual(
								    nested_simplify(_lowercase			,			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 __a						(							self   :    Dict		):
								pass





