

"""simple docstring"""



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

import requests
from bsa import BeautifulSoup

SCREAMING_SNAKE_CASE_					:	Union[str, 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  _snake_case     (    UpperCAmelCase_  :					str = "dhaka"					,   UpperCAmelCase_  :					int = 5			):
     A__										=  min(UpperCAmelCase_					,   50			)  # Prevent abuse!
     A__										=  {
         """q""": query,
         """tbm""": """isch""",
         """hl""": """en""",
         """ijn""": """0""",
     }

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

     A__										=  json.dumps(UpperCAmelCase_			)
     A__										=  json.loads(UpperCAmelCase_			)

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

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

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


if __name__ == "__main__":
 try:
  SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						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"""



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class 					a						(       unittest.TestCase				):



    """simple docstring"""



    @property
    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         torch.manual_seed(0       )
         A__										=  UNetaDModel(
             block_out_channels=(32, 64)    ,						layers_per_block=2    ,						sample_size=32    ,						in_channels=3    ,						out_channels=3    ,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")    ,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")    ,						)
         return model





    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


         A__										=  self.dummy_uncond_unet
         A__										=  PNDMScheduler()

         A__										=  PNDMPipeline(unet=UpperCamelCase    ,						scheduler=UpperCamelCase       )
         pndm.to(UpperCamelCase       )
         pndm.set_progress_bar_config(disable=UpperCamelCase       )

         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						num_inference_steps=20    ,						output_type="""numpy"""       ).images

         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						num_inference_steps=20    ,						output_type="""numpy"""    ,						return_dict=UpperCamelCase       )[0]

         A__										=  image[0, -3:, -3:, -1]
         A__										=  image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A__										=  np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2



@slow
@require_torch
class 					a						(       unittest.TestCase				):



    """simple docstring"""



    def 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


         A__										=  """google/ddpm-cifar10-32"""

         A__										=  UNetaDModel.from_pretrained(UpperCamelCase       )
         A__										=  PNDMScheduler()

         A__										=  PNDMPipeline(unet=UpperCamelCase    ,						scheduler=UpperCamelCase       )
         pndm.to(UpperCamelCase       )
         pndm.set_progress_bar_config(disable=UpperCamelCase       )
         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						output_type="""numpy"""       ).images

         A__										=  image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A__										=  np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2






