




"""simple docstring"""





import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
					print("""Googling.....""")
					A   =       """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
					A   =       requests.get(url, headers={"""UserAgent""": UserAgent().random})
					# res.raise_for_status()
					with open("""project1a.html""", """wb""") as out_file:  # only for knowing the class
										for data in res.iter_content(10_000):
															out_file.write(data)
					A   =       BeautifulSoup(res.text, """html.parser""")
					A   =       list(soup.select(""".eZt8xd"""))[:5]

					print(len(links))
					for link in links:
										if link.text == "Maps":
															webbrowser.open(link.get("""href"""))
										else:
															webbrowser.open(f'''https://google.com{link.get("href")}''')








"""simple docstring"""





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


A   =       logging.get_logger(__name__)

A   =       {
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}



class      a__				(	__magic_name__   ):
			lowercase_    =	"table-transformer"
			lowercase_    =	["past_key_values"]
			lowercase_    =	{
			    "hidden_size": "d_model",
			    "num_attention_heads": "encoder_attention_heads",
			}


			def __init__(      self      :    List[Any]      ,  UpperCamelCase_      :    int=True      ,  UpperCamelCase_      :    Tuple=None      ,  UpperCamelCase_      :    Optional[int]=3      ,  UpperCamelCase_      :    Any=100      ,  UpperCamelCase_      :    Dict=6      ,  UpperCamelCase_      :    List[str]=2048      ,  UpperCamelCase_      :    str=8      ,  UpperCamelCase_      :    Any=6      ,  UpperCamelCase_      :    Dict=2048      ,  UpperCamelCase_      :    str=8      ,  UpperCamelCase_      :    Any=0.0      ,  UpperCamelCase_      :    str=0.0      ,  UpperCamelCase_      :    Union[str, Any]=True      ,  UpperCamelCase_      :    Optional[int]="relu"      ,  UpperCamelCase_      :    str=256      ,  UpperCamelCase_      :    str=0.1      ,  UpperCamelCase_      :    Tuple=0.0      ,  UpperCamelCase_      :    Dict=0.0      ,  UpperCamelCase_      :    Dict=0.02      ,  UpperCamelCase_      :    Optional[Any]=1.0      ,  UpperCamelCase_      :    Optional[int]=False      ,  UpperCamelCase_      :    int="sine"      ,  UpperCamelCase_      :    Optional[Any]="resnet50"      ,  UpperCamelCase_      :    List[str]=True      ,  UpperCamelCase_      :    int=False      ,  UpperCamelCase_      :    Tuple=1      ,  UpperCamelCase_      :    Any=5      ,  UpperCamelCase_      :    Optional[Any]=2      ,  UpperCamelCase_      :    int=1      ,  UpperCamelCase_      :    List[str]=1      ,  UpperCamelCase_      :    List[str]=5      ,  UpperCamelCase_      :    Union[str, Any]=2      ,  UpperCamelCase_      :    Optional[int]=0.1      ,  **UpperCamelCase_      :    int      ,  ):


						"""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	: int		=					CONFIG_MAPPING["resnet"](out_features=["stage4"])
									elif isinstance(UpperCamelCase_      ,  UpperCamelCase_):
												__UpperCAmelCase	: List[Any]		=					backbone_config.get("model_type")
												__UpperCAmelCase	: List[Any]		=					CONFIG_MAPPING[backbone_model_type]
												__UpperCAmelCase	: Optional[int]		=					config_class.from_dict(UpperCamelCase_)
									# set timm attributes to None
									__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	: List[Any]		=					None, None, None

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


			@property
			def       a_     (      self      :    int):


						"""simple docstring"""




						return self.encoder_attention_heads


			@property
			def       a_     (      self      :    int):


						"""simple docstring"""




						return self.d_model






class      a__				(	__magic_name__   ):
			lowercase_    =	version.parse("1.11"   )


			@property
			def       a_     (      self      :    Union[str, Any]):


						"""simple docstring"""




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


			@property
			def       a_     (      self      :    int):


						"""simple docstring"""




						return 1e-5


			@property
			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						return 12


