




'''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 					A		( unittest.TestCase		):




				'''simple docstring'''





				A      	=				MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
				def        a_       (self ,  _UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase			)	->       List[Any]:
							__UpperCamelCase      :			Optional[Any]									=   hf_hub_download(
							    repo_id="nateraw/video-demo" ,  filename="archery.mp4" ,  repo_type="dataset"			)
							__UpperCamelCase      :			int									=   VideoClassificationPipeline(model=UpperCamelCase_ ,  image_processor=UpperCamelCase_ ,  top_k=2			)
							__UpperCamelCase      :			int									=   [
							    example_video_filepath,
							    "https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",
							]
							return video_classifier, examples
				def        a_       (self ,  _UpperCAmelCase ,  _UpperCAmelCase			)	->       Tuple:
							for example in examples:
										__UpperCamelCase      :			Any									=   video_classifier(UpperCamelCase_			)

										self.assertEqual(
										    UpperCamelCase_ ,  [
										        {"score": ANY(UpperCamelCase_			), "label": ANY(UpperCamelCase_			)},
										        {"score": ANY(UpperCamelCase_			), "label": ANY(UpperCamelCase_			)},
										    ] ,  )
				@require_torch
				def        a_       (self			)	->       Any:
							__UpperCamelCase      :			int									=   "hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
							__UpperCamelCase      :			Union[str, Any]									=   VideoMAEFeatureExtractor(
							    size={"shortest_edge": 1_0} ,  crop_size={"height": 1_0, "width": 1_0}			)
							__UpperCamelCase      :			Any									=   pipeline(
							    "video-classification" ,  model=UpperCamelCase_ ,  feature_extractor=UpperCamelCase_ ,  frame_sampling_rate=4			)

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

							__UpperCamelCase      :			Any									=   video_classifier(
							    [
							        video_file_path,
							        video_file_path,
							    ] ,  top_k=2 ,  )
							self.assertEqual(
							    nested_simplify(UpperCamelCase_ ,  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        a_       (self			)	->       str:
							pass









from __future__ import annotations




def 					lowerCamelCase__       (						A__       :					list[int]  ,			A__       :					list[int]  ,			A__       :					list[int]  ,			A__       :					list[list[str]]  ,			A__       :					int  ,			):





       '''simple docstring'''



       __lowerCamelCase   =						len(A__	)

       # If row is equal to the size of the board it means there are a queen in each row in
       # the current board (possible_board)
       if row == n:
              # We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
              # this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
              boards.append([""". """ * i + """Q """ + """. """ * (n - 1 - i) for i in possible_board]	)
              return

       # We iterate each column in the row to find all possible results in each row
       for col in range(A__	):
              # We apply that we learned previously. First we check that in the current board
              # (possible_board) there are not other same value because if there is it means
              # that there are a collision in vertical. Then we apply the two formulas we
              # learned before:
              #
              # 45º: y - x = b or 45: row - col = b
              # 135º: y + x = b or row + col = b.
              #
              # And we verify if the results of this two formulas not exist in their variables
              # respectively.  (diagonal_right_collisions, diagonal_left_collisions)
              #
              # If any or these are True it means there is a collision so we continue to the
              # next value in the for loop.
              if (
                  col in possible_board
                  or row - col in diagonal_right_collisions
                  or row + col in diagonal_left_collisions
              ):
                     continue

              # If it is False we call dfs function again and we update the inputs
              depth_first_search(
                  [*possible_board, col]  ,			[*diagonal_right_collisions, row - col]  ,			[*diagonal_left_collisions, row + col]  ,			A__  ,			A__  ,			)




def 					lowerCamelCase__       (						A__       :					int	):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       depth_first_search([]  ,			[]  ,			[]  ,			A__  ,			A__	)

       # Print all the boards
       for board in boards:
              for column in board:
                     print(A__	)
              print(""""""	)

       print(len(A__	)  ,			"""solutions were found."""	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       n_queens_solution(4)

