


from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    def __init__(			self					,   A_					,   A_    )  ->       str:
         super().__init__()

         # make sure scheduler can always be converted to DDIM
         __UpperCamelCase    							=DDIMScheduler.from_config(scheduler.config    )

         self.register_modules(unet=A_					,   scheduler=A_    )






    @torch.no_grad()
    def __call__(			self					,   A_ = 1					,   A_ = None					,   A_ = 0.0					,   A_ = 50					,   A_ = None					,   A_ = "pil"					,   A_ = True					,   )  ->       Union[ImagePipelineOutput, Tuple]:

         # Sample gaussian noise to begin loop
         if isinstance(self.unet.config.sample_size					,   A_    ):
              __UpperCamelCase    							=(
                  batch_size,
                  self.unet.config.in_channels,
                  self.unet.config.sample_size,
                  self.unet.config.sample_size,
              )
         else:
              __UpperCamelCase    							=(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

         if isinstance(A_					,   A_    ) and len(A_    ) != batch_size:
              raise ValueError(
                  f'You have passed a list of generators of length {len(A_    )}, but requested an effective batch'
                  f' size of {batch_size}. Make sure the batch size matches the length of the generators.'    )

         __UpperCamelCase    							=randn_tensor(A_					,   generator=A_					,   device=self.device					,   dtype=self.unet.dtype    )

         # set step values
         self.scheduler.set_timesteps(A_    )

         for t in self.progress_bar(self.scheduler.timesteps    ):
              # 1. predict noise model_output
              __UpperCamelCase    							=self.unet(A_					,   A_    ).sample

              # 2. predict previous mean of image x_t-1 and add variance depending on eta
              # eta corresponds to η in paper and should be between [0, 1]
              # do x_t -> x_t-1
              __UpperCamelCase    							=self.scheduler.step(
                  A_					,   A_					,   A_					,   eta=A_					,   use_clipped_model_output=A_					,   generator=A_    ).prev_sample

         __UpperCamelCase    							=(image / 2 + 0.5).clamp(0					,   1    )
         __UpperCamelCase    							=image.cpu().permute(0					,   2					,   3					,   1    ).numpy()
         if output_type == "pil":
              __UpperCamelCase    							=self.numpy_to_pil(A_    )

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=A_    )









"""simple docstring"""



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

import requests
from bsa import BeautifulSoup

__snake_case   			=    {
    """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   __lowerCAmelCase						(						lowercase  :  str = "dhaka"						,							lowercase  :  int = 5 )	->						int:


    """simple docstring"""





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

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

    snake_case    :      Optional[Any]      					=    json.dumps(lowercase )
    snake_case    :      str      					=    json.loads(lowercase )

    snake_case    :      List[str]      					=    re.findall(
        R"\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\","						,							lowercase						,							)
    if not matched_google_image_data:
        return 0

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

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


if __name__ == "__main__":
  try:
    __snake_case   			=    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





