






import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel

from ...utils import logging


snake_case__   :						Tuple													=   logging.get_logger(__name__)






def      _snake_case     (__lowercase     ,   __lowercase):
   UpperCamelCase_						=  nn.functional.normalize(__lowercase)
   UpperCamelCase_						=  nn.functional.normalize(__lowercase)
   return torch.mm(__lowercase     ,   normalized_text_embeds.t())


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = CLIPConfig

  A_						       = ["""CLIPEncoderLayer"""]




  def __init__(  self		,		_UpperCAmelCase			)      ->			Dict:
     super().__init__(_UpperCAmelCase			)

     UpperCamelCase_						=  CLIPVisionModel(config.vision_config			)
     UpperCamelCase_						=  nn.Linear(config.vision_config.hidden_size		,		config.projection_dim		,		bias=_UpperCAmelCase			)

     UpperCamelCase_						=  nn.Parameter(torch.ones(17		,		config.projection_dim			)		,		requires_grad=_UpperCAmelCase			)
     UpperCamelCase_						=  nn.Parameter(torch.ones(3		,		config.projection_dim			)		,		requires_grad=_UpperCAmelCase			)

     UpperCamelCase_						=  nn.Parameter(torch.ones(17			)		,		requires_grad=_UpperCAmelCase			)
     UpperCamelCase_						=  nn.Parameter(torch.ones(3			)		,		requires_grad=_UpperCAmelCase			)




  @torch.no_grad()
  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Optional[Any]:
     UpperCamelCase_						=  self.vision_model(_UpperCAmelCase			)[1]  # pooled_output
     UpperCamelCase_						=  self.visual_projection(_UpperCAmelCase			)

     # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
     UpperCamelCase_						=  cosine_distance(_UpperCAmelCase		,		self.special_care_embeds			).cpu().float().numpy()
     UpperCamelCase_						=  cosine_distance(_UpperCAmelCase		,		self.concept_embeds			).cpu().float().numpy()

     UpperCamelCase_						=  []
     UpperCamelCase_						=  image_embeds.shape[0]
     for i in range(_UpperCAmelCase			):
        UpperCamelCase_						=  {'special_scores': {}, 'special_care': [], 'concept_scores': {}, 'bad_concepts': []}

        # increase this value to create a stronger `nfsw` filter
        # at the cost of increasing the possibility of filtering benign images
        UpperCamelCase_						=  0.0

        for concept_idx in range(len(special_cos_dist[0]			)			):
           UpperCamelCase_						=  special_cos_dist[i][concept_idx]
           UpperCamelCase_						=  self.special_care_embeds_weights[concept_idx].item()
           UpperCamelCase_						=  round(concept_cos - concept_threshold + adjustment		,		3			)
           if result_img["special_scores"][concept_idx] > 0:
              result_img["special_care"].append({concept_idx, result_img['special_scores'][concept_idx]}			)
              UpperCamelCase_						=  0.0_1

        for concept_idx in range(len(cos_dist[0]			)			):
           UpperCamelCase_						=  cos_dist[i][concept_idx]
           UpperCamelCase_						=  self.concept_embeds_weights[concept_idx].item()
           UpperCamelCase_						=  round(concept_cos - concept_threshold + adjustment		,		3			)
           if result_img["concept_scores"][concept_idx] > 0:
              result_img["bad_concepts"].append(_UpperCAmelCase			)

        result.append(_UpperCAmelCase			)

     UpperCamelCase_						=  [len(res['bad_concepts']			) > 0 for res in result]

     return images, has_nsfw_concepts




  @torch.no_grad()
  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			List[str]:
     UpperCamelCase_						=  self.vision_model(_UpperCAmelCase			)[1]  # pooled_output
     UpperCamelCase_						=  self.visual_projection(_UpperCAmelCase			)

     UpperCamelCase_						=  cosine_distance(_UpperCAmelCase		,		self.special_care_embeds			)
     UpperCamelCase_						=  cosine_distance(_UpperCAmelCase		,		self.concept_embeds			)

     # increase this value to create a stronger `nsfw` filter
     # at the cost of increasing the possibility of filtering benign images
     UpperCamelCase_						=  0.0

     UpperCamelCase_						=  special_cos_dist - self.special_care_embeds_weights + adjustment
     # special_scores = special_scores.round(decimals=3)
     UpperCamelCase_						=  torch.any(special_scores > 0		,		dim=1			)
     UpperCamelCase_						=  special_care * 0.0_1
     UpperCamelCase_						=  special_adjustment.unsqueeze(1			).expand(-1		,		cos_dist.shape[1]			)

     UpperCamelCase_						=  (cos_dist - self.concept_embeds_weights) + special_adjustment
     # concept_scores = concept_scores.round(decimals=3)
     UpperCamelCase_						=  torch.any(concept_scores > 0		,		dim=1			)

     return images, has_nsfw_concepts






'''simple docstring'''




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

import requests
from bsa import BeautifulSoup

__lowercase        =    {
    '''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__				(     _A:   str = "dhaka" ,  _A:   int = 5					)       ->      int:

		'''simple docstring'''


		lowerCAmelCase     =    min(_A ,  50					)  # Prevent abuse!
		lowerCAmelCase     =    {
		    """q""": query,
		    """tbm""": """isch""",
		    """hl""": """en""",
		    """ijn""": """0""",
		}

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

		lowerCAmelCase     =    json.dumps(_A					)
		lowerCAmelCase     =    json.loads(_A					)

		lowerCAmelCase     =    re.findall(
		    r"""\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",""" ,  _A ,  )
		if not matched_google_image_data:
				return 0

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

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


if __name__ == "__main__":
						try:
												__lowercase        =    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




