
'''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__":
			snake_case_ :  str     =					'%20'.join(argv[1:]) if len(argv) > 1 else quote(str(input('Search: ')))

			print('Googling.....')

			snake_case_ :  Any     =					F'''https://www.google.com/search?q={query}&num=100'''

			snake_case_ :  int     =					requests.get(
			    url,
			    headers={'User-Agent': str(UserAgent().random)},
			)

			try:
						snake_case_ :  Union[str, Any]     =					(
						    BeautifulSoup(res.text, 'html.parser')
						    .find('div', attrs={'class': 'yuRUbf'})
						    .find('a')
						    .get('href')
						)

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

			webbrowser.open(link)







'''simple docstring'''


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import LevitImageProcessor


class 						lowercase__	(  unittest.TestCase   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__=7 ,   lowerCamelCase__=3 ,   lowerCamelCase__=1_8 ,   lowerCamelCase__=3_0 ,   lowerCamelCase__=4_0_0 ,   lowerCamelCase__=True ,   lowerCamelCase__=None ,   lowerCamelCase__=True ,   lowerCamelCase__=None ,   lowerCamelCase__=True ,   lowerCamelCase__=[0.5, 0.5, 0.5] ,   lowerCamelCase__=[0.5, 0.5, 0.5] ,   ):

      '''simple docstring'''







      UpperCamelCase       =  size if size is not None else {'''shortest_edge''': 1_8}
      UpperCamelCase       =  crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
      UpperCamelCase       =  parent
      UpperCamelCase       =  batch_size
      UpperCamelCase       =  num_channels
      UpperCamelCase       =  image_size
      UpperCamelCase       =  min_resolution
      UpperCamelCase       =  max_resolution
      UpperCamelCase       =  do_resize
      UpperCamelCase       =  size
      UpperCamelCase       =  do_center_crop
      UpperCamelCase       =  crop_size
      UpperCamelCase       =  do_normalize
      UpperCamelCase       =  image_mean
      UpperCamelCase       =  image_std







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "do_center_crop": self.do_center_crop,
          "size": self.size,
          "crop_size": self.crop_size,
      }




@require_torch
@require_vision
class 						lowercase__	(  snake_case_,					unittest.TestCase   ):






   '''simple docstring'''







   _snake_case		   =							LevitImageProcessor if is_vision_available() else None







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  LevitImageProcessingTester(self   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return self.image_processor_tester.prepare_image_processor_dict()







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''image_mean'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''image_std'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''do_normalize'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''do_resize'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''do_center_crop'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''size'''   )   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class.from_dict(self.image_processor_dict   )
      self.assertEqual(image_processor.size ,   {'''shortest_edge''': 1_8}   )
      self.assertEqual(image_processor.crop_size ,   {'''height''': 1_8, '''width''': 1_8}   )

      UpperCamelCase       =  self.image_processing_class.from_dict(self.image_processor_dict ,   size=4_2 ,   crop_size=8_4   )
      self.assertEqual(image_processor.size ,   {'''shortest_edge''': 4_2}   )
      self.assertEqual(image_processor.crop_size ,   {'''height''': 8_4, '''width''': 8_4}   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      pass







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict   )
      # create random PIL images
      UpperCamelCase       =  prepare_image_inputs(self.image_processor_tester ,   equal_resolution=lowerCamelCase__   )
      for image in image_inputs:
         self.assertIsInstance(lowerCamelCase__ ,   Image.Image   )

      # Test not batched input
      UpperCamelCase       =  image_processing(image_inputs[0] ,   return_tensors='''pt'''   ).pixel_values
      self.assertEqual(
          encoded_images.shape ,   (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          ) ,   )

      # Test batched
      UpperCamelCase       =  image_processing(lowerCamelCase__ ,   return_tensors='''pt'''   ).pixel_values
      self.assertEqual(
          encoded_images.shape ,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          ) ,   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict   )
      # create random numpy tensors
      UpperCamelCase       =  prepare_image_inputs(self.image_processor_tester ,   equal_resolution=lowerCamelCase__ ,   numpify=lowerCamelCase__   )
      for image in image_inputs:
         self.assertIsInstance(lowerCamelCase__ ,   np.ndarray   )

      # Test not batched input
      UpperCamelCase       =  image_processing(image_inputs[0] ,   return_tensors='''pt'''   ).pixel_values
      self.assertEqual(
          encoded_images.shape ,   (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          ) ,   )

      # Test batched
      UpperCamelCase       =  image_processing(lowerCamelCase__ ,   return_tensors='''pt'''   ).pixel_values
      self.assertEqual(
          encoded_images.shape ,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          ) ,   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict   )
      # create random PyTorch tensors
      UpperCamelCase       =  prepare_image_inputs(self.image_processor_tester ,   equal_resolution=lowerCamelCase__ ,   torchify=lowerCamelCase__   )
      for image in image_inputs:
         self.assertIsInstance(lowerCamelCase__ ,   torch.Tensor   )

      # Test not batched input
      UpperCamelCase       =  image_processing(image_inputs[0] ,   return_tensors='''pt'''   ).pixel_values
      self.assertEqual(
          encoded_images.shape ,   (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          ) ,   )

      # Test batched
      UpperCamelCase       =  image_processing(lowerCamelCase__ ,   return_tensors='''pt'''   ).pixel_values
      self.assertEqual(
          encoded_images.shape ,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          ) ,   )





