'''simple docstring'''

import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
 _A			:      int					      =				"""%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input('''Search: ''')))

 print('''Googling.....''')

 _A			:      Union[str, Any]					      =				f'https://www.google.com/search?q={query}&num=100'

 _A			:      List[Any]					      =				requests.get(
     url,
     headers={'''User-Agent''': str(UserAgent().random)},
 )

 try:
  _A			:      int					      =				(
      BeautifulSoup(res.text, '''html.parser''')
      .find('''div''', attrs={'''class''': '''yuRUbf'''})
      .find('''a''')
      .get('''href''')
  )

 except AttributeError:
  _A			:      Optional[int]					      =				parse_qs(
      BeautifulSoup(res.text, '''html.parser''')
      .find('''div''', attrs={'''class''': '''kCrYT'''})
      .find('''a''')
      .get('''href''')
  )["""url"""][0]

 webbrowser.open(link)








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     ):
    _a		:						Optional[int]=				MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Any       	=      hf_hub_download(
               repo_id="""nateraw/video-demo"""		,filename="""archery.mp4"""		,repo_type="""dataset"""      )
           lowercase							:     List[str]       	=      VideoClassificationPipeline(model=snake_case		,image_processor=snake_case		,top_k=2      )
           lowercase							:     Dict       	=      [
               example_video_filepath,
               """https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4""",
           ]
           return video_classifier, examples
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           for example in examples:
                  lowercase							:     int       	=      video_classifier(snake_case      )

                  self.assertEqual(
                      snake_case		,[
                          {"""score""": ANY(snake_case      ), """label""": ANY(snake_case      )},
                          {"""score""": ANY(snake_case      ), """label""": ANY(snake_case      )},
                      ]		,)
    @require_torch
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      """hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
           lowercase							:     str       	=      VideoMAEFeatureExtractor(
               size={"""shortest_edge""": 10}		,crop_size={"""height""": 10, """width""": 10}      )
           lowercase							:     List[Any]       	=      pipeline(
               """video-classification"""		,model=snake_case		,feature_extractor=snake_case		,frame_sampling_rate=4      )

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

           lowercase							:     str       	=      video_classifier(
               [
                   video_file_path,
                   video_file_path,
               ]		,top_k=2		,)
           self.assertEqual(
               nested_simplify(snake_case		,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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           pass
