


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							:    Any      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						hf_hub_download(
                 repo_id="nateraw/video-demo"      ,   filename="archery.mp4"      ,   repo_type="dataset" )
             lowerCamelCase__	:       Union[str, Any]					=						VideoClassificationPipeline(model=__lowerCamelCase      ,   image_processor=__lowerCamelCase      ,   top_k=2 )
             lowerCamelCase__	:       List[Any]					=						[
                 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							:    Optional[Any] ):


             '''simple docstring'''



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

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





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


             '''simple docstring'''



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

             lowerCamelCase__	:       List[Any]					=						hf_hub_download(repo_id="nateraw/video-demo"      ,   filename="archery.mp4"      ,   repo_type="dataset" )
             lowerCamelCase__	:       Union[str, 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






import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
    print("Googling.....")
    A   :					str	=      "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
    A   :					Optional[int]	=      requests.get(url, headers={"UserAgent": UserAgent().random})
    # res.raise_for_status()
    with open("project1a.html", "wb") as out_file:  # only for knowing the class
        for data in res.iter_content(10000):
            out_file.write(data)
    A   :					int	=      BeautifulSoup(res.text, "html.parser")
    A   :					Any	=      list(soup.select(".eZt8xd"))[:5]

    print(len(links))
    for link in links:
        if link.text == "Maps":
            webbrowser.open(link.get("href"))
        else:
            webbrowser.open(f'https://google.com{link.get("href")}')


