


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 			_lowercase (							unittest.TestCase):


       """simple docstring"""




       A__											=			MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING





       def 						lowerCAmelCase     (   self							:			Union[str, Any]					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			Any					,      __lowerCamelCase							:			Optional[int]       ):


             '''simple docstring'''



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





       def 						lowerCAmelCase     (   self							:			str					,      __lowerCamelCase							:			Union[str, Any]					,      __lowerCamelCase							:			Any       ):


             '''simple docstring'''



             for example in examples:
                   lowerCamelCase__		:				int    =  video_classifier(__lowerCamelCase       )

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





       @require_torch
       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  "hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
             lowerCamelCase__		:				Optional[int]    =  VideoMAEFeatureExtractor(
                 size={"shortest_edge": 10}					,      crop_size={"height": 10, "width": 10}       )
             lowerCamelCase__		:				int    =  pipeline(
                 "video-classification"					,      model=__lowerCamelCase					,      feature_extractor=__lowerCamelCase					,      frame_sampling_rate=4       )

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

             lowerCamelCase__		:				Any    =  video_classifier(
                 [
                     video_file_path,
                     video_file_path,
                 ]					,      top_k=2					,      )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase					,      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 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             pass






class 			_lowercase :  # Public class to implement a graph


       """simple docstring"""




       def __init__(   self							:			Dict					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			list[list[bool]]       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  row
             lowerCamelCase__		:				Optional[Any]    =  col
             lowerCamelCase__		:				Union[str, Any]    =  graph





       def 						lowerCAmelCase     (   self							:			Optional[int]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			list[list[bool]]       ):


             '''simple docstring'''



             return (
                 0 <= i < self.ROW
                 and 0 <= j < self.COL
                 and not visited[i][j]
                 and self.graph[i][j]
             )





       def 						lowerCAmelCase     (   self							:			Dict					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			list[list[bool]]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
             lowerCamelCase__		:				Tuple    =  [-1, 0, 1, -1, 1, -1, 0, 1]
             lowerCamelCase__		:				List[Any]    =  True  # Make those cells visited
             for k in range(8       ):
                   if self.is_safe(i + row_nbr[k]					,      j + col_nbr[k]					,      __lowerCamelCase       ):
                         self.diffs(i + row_nbr[k]					,      j + col_nbr[k]					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			List[Any]       ):  # And finally, count all islands.


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  [[False for j in range(self.COL       )] for i in range(self.ROW       )]
             lowerCamelCase__		:				Optional[Any]    =  0
             for i in range(self.ROW       ):
                   for j in range(self.COL       ):
                         if visited[i][j] is False and self.graph[i][j] == 1:
                               self.diffs(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase       )
                               count += 1
             return count


