




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

import requests
from bsa import BeautifulSoup

__UpperCAmelCase : 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"
}


def      a	(							SCREAMING_SNAKE_CASE_      :		str = "dhaka"							,			SCREAMING_SNAKE_CASE_      :		int = 5       ):






       """simple docstring"""





       UpperCamelCase    :	List[str]   	=		min(SCREAMING_SNAKE_CASE_							,			5_0       )  # Prevent abuse!
       UpperCamelCase    :	str   	=		{
           '''q''': query,
           '''tbm''': '''isch''',
           '''hl''': '''en''',
           '''ijn''': '''0''',
       }

       UpperCamelCase    :	int   	=		requests.get('''https://www.google.com/search'''							,			params=SCREAMING_SNAKE_CASE_							,			headers=SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	Tuple   	=		BeautifulSoup(html.text							,			'''html.parser'''       )
       UpperCamelCase    :	Union[str, Any]   	=		''''''.join(
           re.findall(R'''AF_initDataCallback\(([^<]+)\);'''							,			str(soup.select('''script'''       )       )       )       )

       UpperCamelCase    :	int   	=		json.dumps(SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	Dict   	=		json.loads(SCREAMING_SNAKE_CASE_       )

       UpperCamelCase    :	Optional[Any]   	=		re.findall(
           R'''\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",'''							,			SCREAMING_SNAKE_CASE_							,			)
       if not matched_google_image_data:
              return 0

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

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


if __name__ == "__main__":
  try:
    __UpperCAmelCase : 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





import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__UpperCAmelCase : Optional[Any] 			=				logging.get_logger(__name__)

__UpperCAmelCase : List[str] 			=				{
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     Optional[int]							=					"conditional_detr"
       __UpperCamelCase      :     Optional[Any]							=					["past_key_values"]
       __UpperCamelCase      :     Union[str, Any]							=					{
           "hidden_size": "d_model",
           "num_attention_heads": "encoder_attention_heads",
       }






       def __init__(     self ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=3 ,					__SCREAMING_SNAKE_CASE=300 ,					__SCREAMING_SNAKE_CASE=6 ,					__SCREAMING_SNAKE_CASE=2_048 ,					__SCREAMING_SNAKE_CASE=8 ,					__SCREAMING_SNAKE_CASE=6 ,					__SCREAMING_SNAKE_CASE=2_048 ,					__SCREAMING_SNAKE_CASE=8 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE="relu" ,					__SCREAMING_SNAKE_CASE=256 ,					__SCREAMING_SNAKE_CASE=0.1 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.02 ,					__SCREAMING_SNAKE_CASE=1.0 ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE="sine" ,					__SCREAMING_SNAKE_CASE="resnet50" ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=5 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=1 ,					__SCREAMING_SNAKE_CASE=1 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=5 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=0.25 ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              if backbone_config is not None and use_timm_backbone:
                     raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''						)

              if not use_timm_backbone:
                     if backbone_config is None:
                            logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''						)
                            UpperCamelCase    :	str   	=		CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']						)
                     elif isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                            UpperCamelCase    :	Tuple   	=		backbone_config.get('''model_type'''						)
                            UpperCamelCase    :	Optional[Any]   	=		CONFIG_MAPPING[backbone_model_type]
                            UpperCamelCase    :	Any   	=		config_class.from_dict(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	List[str]   	=		use_timm_backbone
              UpperCamelCase    :	int   	=		backbone_config
              UpperCamelCase    :	Any   	=		num_channels
              UpperCamelCase    :	Optional[Any]   	=		num_queries
              UpperCamelCase    :	Tuple   	=		d_model
              UpperCamelCase    :	Optional[Any]   	=		encoder_ffn_dim
              UpperCamelCase    :	Optional[int]   	=		encoder_layers
              UpperCamelCase    :	Union[str, Any]   	=		encoder_attention_heads
              UpperCamelCase    :	Optional[Any]   	=		decoder_ffn_dim
              UpperCamelCase    :	Optional[int]   	=		decoder_layers
              UpperCamelCase    :	Optional[Any]   	=		decoder_attention_heads
              UpperCamelCase    :	Any   	=		dropout
              UpperCamelCase    :	List[Any]   	=		attention_dropout
              UpperCamelCase    :	List[Any]   	=		activation_dropout
              UpperCamelCase    :	List[str]   	=		activation_function
              UpperCamelCase    :	Optional[int]   	=		init_std
              UpperCamelCase    :	Optional[Any]   	=		init_xavier_std
              UpperCamelCase    :	Union[str, Any]   	=		encoder_layerdrop
              UpperCamelCase    :	Optional[Any]   	=		decoder_layerdrop
              UpperCamelCase    :	Tuple   	=		encoder_layers
              UpperCamelCase    :	Optional[Any]   	=		auxiliary_loss
              UpperCamelCase    :	Union[str, Any]   	=		position_embedding_type
              UpperCamelCase    :	Optional[int]   	=		backbone
              UpperCamelCase    :	Dict   	=		use_pretrained_backbone
              UpperCamelCase    :	Tuple   	=		dilation
              # Hungarian matcher
              UpperCamelCase    :	Union[str, Any]   	=		class_cost
              UpperCamelCase    :	List[Any]   	=		bbox_cost
              UpperCamelCase    :	Optional[Any]   	=		giou_cost
              # Loss coefficients
              UpperCamelCase    :	Optional[Any]   	=		mask_loss_coefficient
              UpperCamelCase    :	Optional[int]   	=		dice_loss_coefficient
              UpperCamelCase    :	Optional[Any]   	=		cls_loss_coefficient
              UpperCamelCase    :	Optional[int]   	=		bbox_loss_coefficient
              UpperCamelCase    :	Optional[int]   	=		giou_loss_coefficient
              UpperCamelCase    :	Optional[int]   	=		focal_alpha
              super().__init__(is_encoder_decoder=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						)






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return self.encoder_attention_heads






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return self.d_model






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		copy.deepcopy(self.__dict__						)
              if self.backbone_config is not None:
                     UpperCamelCase    :	List[Any]   	=		self.backbone_config.to_dict()
              UpperCamelCase    :	List[Any]   	=		self.__class__.model_type
              return output




class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     Dict							=					version.parse("1.11")






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return OrderedDict(
                  [
                      ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                      ('''pixel_mask''', {0: '''batch'''}),
                  ]						)






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return 1e-5






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return 12

