

"""simple docstring"""


import json
import os
import re
import sys
import urllib.request

import requests
from bsa import BeautifulSoup

a					:Any    = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
    " (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"
}




def  _lowercase					(    __lowerCAmelCase = "dhaka"  ,					__lowerCAmelCase = 5							)       ->				int:

      '''simple docstring'''





      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	min(__lowerCAmelCase  ,					50							)  # Prevent abuse!
      SCREAMING_SNAKE_CASE__   :      Any      		=	{
          """q""": query,
          """tbm""": """isch""",
          """hl""": """en""",
          """ijn""": """0""",
      }

      SCREAMING_SNAKE_CASE__   :      Dict      		=	requests.get("""https://www.google.com/search"""  ,					params=__lowerCAmelCase  ,					headers=__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	BeautifulSoup(html.text  ,					"""html.parser"""							)
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	"""""".join(
          re.findall(r"""AF_initDataCallback\(([^<]+)\);"""  ,					str(soup.select("""script"""							)							)							)							)

      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	json.dumps(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	json.loads(__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	re.findall(
          r"""\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\","""  ,					__lowerCAmelCase  ,					)
      if not matched_google_image_data:
            return 0

      SCREAMING_SNAKE_CASE__   :      Any      		=	re.sub(
          r"""\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]"""  ,					""""""  ,					str(__lowerCAmelCase							)  ,					)

      SCREAMING_SNAKE_CASE__   :      Dict      		=	re.findall(
          r"""(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]"""  ,					__lowerCAmelCase  ,					)
      for index, fixed_full_res_image in enumerate(__lowerCAmelCase							):
            if index >= max_images:
                  return index
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	bytes(__lowerCAmelCase  ,					"""ascii"""							).decode(
                """unicode-escape"""							)
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	bytes(__lowerCAmelCase  ,					"""ascii"""							).decode(
                """unicode-escape"""							)
            SCREAMING_SNAKE_CASE__   :      Dict      		=	urllib.request.build_opener()
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	[
                (
                    """User-Agent""",
                    """Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"""
                    """ (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582""",
                )
            ]
            urllib.request.install_opener(__lowerCAmelCase							)
            SCREAMING_SNAKE_CASE__   :      Dict      		=	F'''query_{query.replace(' '  ,					'_'							)}'''
            if not os.path.exists(__lowerCAmelCase							):
                  os.makedirs(__lowerCAmelCase							)
            urllib.request.urlretrieve(  # noqa: S310
                __lowerCAmelCase  ,					F'''{path_name}/original_size_img_{index}.jpg'''							)
      return index


if __name__ == "__main__":
  try:
    a					:str    = download_images_from_google_query(sys.argv[1])
    print(f'{image_count} images were downloaded to disk.')
  except IndexError:
    print("Please provide a search term.")
    raise







"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import numpy as np
  import tensorflow as tf

  from transformers import TFCamembertModel

@require_tf
@require_sentencepiece
@require_tokenizers
class      __a							(unittest.TestCase):






     '''simple docstring'''

     @slow
     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	tf.convert_to_tensor(
               [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]	,    dtype=tf.intaa	,    )  # J'aime le camembert !"

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	model(_a  )["""last_hidden_state"""]
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	tf.TensorShape((1, 10, 768)  )
           self.assertEqual(output.shape	,    _a  )
           # compare the actual values for a slice.
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	tf.convert_to_tensor(
               [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]	,    dtype=tf.floataa	,    )
           # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
           # camembert.eval()
           # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

           self.assertTrue(np.allclose(output[:, :3, :3].numpy()	,    expected_slice.numpy()	,    atol=1E-4  )  )




