




'''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							):
			lowerCAmelCase_     :      Tuple						       =    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING




			def    lowerCAmelCase__				(					self				,							a__				,							a__				,							a__							)				->				Tuple:

										'''simple docstring'''




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




			def    lowerCAmelCase__				(					self				,							a__				,							a__							)				->				int:

										'''simple docstring'''




										for example in examples:
																	snake_case_						=      video_classifier(UpperCamelCase__							)

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




			@require_torch
			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




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

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

										snake_case_						=      video_classifier(
										    [
										        video_file_path,
										        video_file_path,
										    ]				,							top_k=2				,							)
										self.assertEqual(
										    nested_simplify(UpperCamelCase__				,							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							)				->				int:

										'''simple docstring'''




										pass










import random

from .binary_exp_mod import bin_exp_mod

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=1000    )	-> List[str]:
       if n < 2:
              return False

       if n % 2 == 0:
              return n == 2

       # this means n is odd
       lowerCamelCase   :      List[Any]			 =					n - 1
       lowerCamelCase   :      Dict			 =					0
       while d % 2 == 0:
              d /= 2
              exp += 1

       # n - 1=d*(2**exp)
       lowerCamelCase   :      Optional[Any]			 =					0
       while count < prec:
              lowerCamelCase   :      str			 =					random.randint(2 ,n - 1    )
              lowerCamelCase   :      Dict			 =					bin_exp_mod(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
              if b != 1:
                     lowerCamelCase   :      str			 =					True
                     for _ in range(_SCREAMING_SNAKE_CASE    ):
                            if b == n - 1:
                                   lowerCamelCase   :      Tuple			 =					False
                                   break
                            lowerCamelCase   :      int			 =					b * b
                            b %= n
                     if flag:
                            return False
                     count += 1
       return True


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Optional[int]  =   abs(int(input('Enter bound : ').strip()))
       print('Here\'s the list of primes:')
       print(', '.join(str(i) for i in range(n + 1) if is_prime_big(i)))






