





import numpy as np



class 					__lowerCAmelCase       :






			def __init__(self    )     ->   str:


							'''simple docstring'''



							snake_case_						:    Optional[Any]				=  (0, 0)
							snake_case_						:    Any				=  None
							snake_case_						:    List[Any]				=  0
							snake_case_						:    Tuple				=  0
							snake_case_						:    List[str]				=  0






			def __eq__(self					,							__magic_name__    )     ->   Optional[Any]:


							'''simple docstring'''



							return self.position == cell.position






			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							print(self.position    )




class 					__lowerCAmelCase       :






			def __init__(self					,							__magic_name__=(5, 5)    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Tuple				=  np.zeros(__magic_name__    )
							snake_case_						:    Union[str, Any]				=  world_size[0]
							snake_case_						:    str				=  world_size[1]






			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							print(self.w    )






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   Any:


							'''simple docstring'''



							snake_case_						:    List[str]				=  [
							    (-1, -1),
							    (-1, 0),
							    (-1, 1),
							    (0, -1),
							    (0, 1),
							    (1, -1),
							    (1, 0),
							    (1, 1),
							]
							snake_case_						:    Tuple				=  cell.position[0]
							snake_case_						:    Tuple				=  cell.position[1]
							snake_case_						:    Dict				=  []
							for n in neughbour_cord:
											snake_case_						:    List[str]				=  current_x + n[0]
											snake_case_						:    Union[str, Any]				=  current_y + n[1]
											if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
															snake_case_						:    Dict				=  Cell()
															snake_case_						:    Optional[int]				=  (x, y)
															snake_case_						:    Any				=  cell
															neighbours.append(__magic_name__    )
							return neighbours






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						Any:






				"""simple docstring"""


				snake_case_						:    List[Any]				=  []
				snake_case_						:    Optional[int]				=  []
				_open.append(_UpperCamelCase )

				while _open:
								snake_case_						:    Dict				=  np.argmin([n.f for n in _open] )
								snake_case_						:    Optional[Any]				=  _open[min_f]
								_closed.append(_open.pop(_UpperCamelCase ) )
								if current == goal:
												break
								for n in world.get_neigbours(_UpperCamelCase ):
												for c in _closed:
																if c == n:
																				continue
												snake_case_						:    int				=  current.g + 1
												snake_case_			,							snake_case_						:    Tuple				=  n.position
												snake_case_			,							snake_case_						:    str				=  goal.position
												snake_case_						:    Dict				=  (ya - ya) ** 2 + (xa - xa) ** 2
												snake_case_						:    str				=  n.h + n.g

												for c in _open:
																if c == n and c.f < n.f:
																				continue
												_open.append(_UpperCamelCase )
				snake_case_						:    List[str]				=  []
				while current.parent is not None:
								path.append(current.position )
								snake_case_						:    Union[str, Any]				=  current.parent
				path.append(current.position )
				return path[::-1]


if __name__ == "__main__":
	lowerCAmelCase_    =				Gridworld()
	# Start position and goal
	lowerCAmelCase_    =				Cell()
	lowerCAmelCase_    =				(0, 0)
	lowerCAmelCase_    =				Cell()
	lowerCAmelCase_    =				(4, 4)
	print(F'''path from {start.position} to {goal.position}''')
	lowerCAmelCase_    =				astar(world, start, goal)
	#   Just for visual reasons.
	for i in s:
		lowerCAmelCase_    =				1
	print(world.w)







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_     :     Any       			=      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Any				=  hf_hub_download(
							    repo_id='''nateraw/video-demo'''					,							filename='''archery.mp4'''					,							repo_type='''dataset'''    )
							snake_case_						:    List[Any]				=  VideoClassificationPipeline(model=__magic_name__					,							image_processor=__magic_name__					,							top_k=2    )
							snake_case_						:    str				=  [
							    example_video_filepath,
							    '''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
							]
							return video_classifier, examples






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__    )     ->   Any:


							'''simple docstring'''



							for example in examples:
											snake_case_						:    Union[str, Any]				=  video_classifier(__magic_name__    )

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






			@require_torch
			def 			lowerCamelCase  (self    )     ->   str:


							'''simple docstring'''



							snake_case_						:    Any				=  '''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
							snake_case_						:    str				=  VideoMAEFeatureExtractor(
							    size={'''shortest_edge''': 10}					,							crop_size={'''height''': 10, '''width''': 10}    )
							snake_case_						:    int				=  pipeline(
							    '''video-classification'''					,							model=__magic_name__					,							feature_extractor=__magic_name__					,							frame_sampling_rate=4    )

							snake_case_						:    List[str]				=  hf_hub_download(repo_id='''nateraw/video-demo'''					,							filename='''archery.mp4'''					,							repo_type='''dataset'''    )
							snake_case_						:    Union[str, Any]				=  video_classifier(__magic_name__					,							top_k=2    )
							self.assertEqual(
							    nested_simplify(__magic_name__					,							decimals=4    )					,							[{'''score''': 0.5_199, '''label''': '''LABEL_0'''}, {'''score''': 0.4_801, '''label''': '''LABEL_1'''}]					,							)

							snake_case_						:    int				=  video_classifier(
							    [
							        video_file_path,
							        video_file_path,
							    ]					,							top_k=2					,							)
							self.assertEqual(
							    nested_simplify(__magic_name__					,							decimals=4    )					,							[
							        [{'''score''': 0.5_199, '''label''': '''LABEL_0'''}, {'''score''': 0.4_801, '''label''': '''LABEL_1'''}],
							        [{'''score''': 0.5_199, '''label''': '''LABEL_0'''}, {'''score''': 0.4_801, '''label''': '''LABEL_1'''}],
							    ]					,							)






			@require_tf
			def 			lowerCamelCase  (self    )     ->   Optional[int]:


							'''simple docstring'''



							pass
