






'''simple docstring'''





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

import requests
from bsa import BeautifulSoup

lowerCamelCase__							=     {
    """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   UpperCamelCase  (       snake_case__     :   str = "dhaka"	,snake_case__     :   int = 5  ):




     '''simple docstring'''






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

     __snake_case			:Optional[Any]          =						requests.get("""https://www.google.com/search"""	,params=snake_case__	,headers=snake_case__  )
     __snake_case			:Any          =						BeautifulSoup(html.text	,"""html.parser"""  )
     __snake_case			:List[str]          =						"""""".join(
         re.findall(R"""AF_initDataCallback\(([^<]+)\);"""	,str(soup.select("""script"""  )  )  )  )

     __snake_case			:Union[str, Any]          =						json.dumps(snake_case__  )
     __snake_case			:Union[str, Any]          =						json.loads(snake_case__  )

     __snake_case			:Tuple          =						re.findall(
         R"""\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\","""	,snake_case__	,)
     if not matched_google_image_data:
          return 0

     __snake_case			:Dict          =						re.sub(
         R"""\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]"""	,""""""	,str(snake_case__  )	,)

     __snake_case			:Optional[Any]          =						re.findall(
         R"""(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]"""	,snake_case__	,)
     for index, fixed_full_res_image in enumerate(snake_case__  ):
          if index >= max_images:
               return index
          __snake_case			:List[str]          =						bytes(snake_case__	,"""ascii"""  ).decode(
              """unicode-escape"""  )
          __snake_case			:Any          =						bytes(snake_case__	,"""ascii"""  ).decode(
              """unicode-escape"""  )
          __snake_case			:Tuple          =						urllib.request.build_opener()
          __snake_case			:str          =						[
              (
                  """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(snake_case__  )
          __snake_case			:Optional[Any]          =						f'''query_{query.replace(' '	,'_'  )}'''
          if not os.path.exists(snake_case__  ):
               os.makedirs(snake_case__  )
          urllib.request.urlretrieve(  # noqa: S310
              snake_case__	,f'''{path_name}/original_size_img_{index}.jpg'''  )
     return index


if __name__ == "__main__":
     try:
          lowerCamelCase__							=     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










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 GLPNImageProcessor




class 			snake_case__   (							unittest.TestCase):

       '''simple docstring'''



       def __init__(							self			,						a__			,						a__=7			,						a__=3			,						a__=18			,						a__=30			,						a__=4_00			,						a__=True			,						a__=32			,						a__=True			,						) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:List[Any]          =						parent
            __snake_case			:Dict          =						batch_size
            __snake_case			:Optional[Any]          =						num_channels
            __snake_case			:Dict          =						image_size
            __snake_case			:Dict          =						min_resolution
            __snake_case			:Dict          =						max_resolution
            __snake_case			:List[Any]          =						do_resize
            __snake_case			:Dict          =						size_divisor
            __snake_case			:Union[str, Any]          =						do_rescale



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            return {
                "do_resize": self.do_resize,
                "size_divisor": self.size_divisor,
                "do_rescale": self.do_rescale,
            }




@require_torch
@require_vision
class 			snake_case__   (							lowercase_	,    unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase							:				Tuple 				= GLPNImageProcessor if is_vision_available() else None



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:Dict          =						GLPNImageProcessingTester(self    )



       @property
       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            return self.image_processor_tester.prepare_image_processor_dict()



       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						self.image_processing_class(**self.image_processor_dict    )
            self.assertTrue(hasattr(a__			,						"""do_resize"""    )    )
            self.assertTrue(hasattr(a__			,						"""size_divisor"""    )    )
            self.assertTrue(hasattr(a__			,						"""resample"""    )    )
            self.assertTrue(hasattr(a__			,						"""do_rescale"""    )    )



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            pass



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:int          =						self.image_processing_class(**self.image_processor_dict    )
            # create random PIL images
            __snake_case			:Optional[Any]          =						prepare_image_inputs(self.image_processor_tester			,						equal_resolution=a__    )
            for image in image_inputs:
                 self.assertIsInstance(a__			,						Image.Image    )

            # Test not batched input (GLPNImageProcessor doesn't support batching)
            __snake_case			:Union[str, Any]          =						image_processing(image_inputs[0]			,						return_tensors="""pt"""    ).pixel_values
            self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
            self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:int          =						self.image_processing_class(**self.image_processor_dict    )
            # create random numpy tensors
            __snake_case			:List[Any]          =						prepare_image_inputs(self.image_processor_tester			,						equal_resolution=a__			,						numpify=a__    )
            for image in image_inputs:
                 self.assertIsInstance(a__			,						np.ndarray    )

            # Test not batched input (GLPNImageProcessor doesn't support batching)
            __snake_case			:int          =						image_processing(image_inputs[0]			,						return_tensors="""pt"""    ).pixel_values
            self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
            self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )



       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            __snake_case			:Tuple          =						self.image_processing_class(**self.image_processor_dict    )
            # create random PyTorch tensors
            __snake_case			:Optional[int]          =						prepare_image_inputs(self.image_processor_tester			,						equal_resolution=a__			,						torchify=a__    )
            for image in image_inputs:
                 self.assertIsInstance(a__			,						torch.Tensor    )

            # Test not batched input (GLPNImageProcessor doesn't support batching)
            __snake_case			:List[Any]          =						image_processing(image_inputs[0]			,						return_tensors="""pt"""    ).pixel_values
            self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
            self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )


