






'''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


lowerCAmelCase__          =						logging.get_logger(__name__)

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







class       __lowercase					(__lowerCamelCase    ):
	_lowerCamelCase              =  '''table-transformer'''
	_lowerCamelCase              =  ['''past_key_values''']
	_lowerCamelCase              =  {
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''encoder_attention_heads''',
	}

	def __init__(      self   :						int  ,					UpperCAmelCase_   :						Dict=True  ,					UpperCAmelCase_   :						Optional[int]=None  ,					UpperCAmelCase_   :						Any=3  ,					UpperCAmelCase_   :						List[str]=100  ,					UpperCAmelCase_   :						Tuple=6  ,					UpperCAmelCase_   :						List[str]=2_048  ,					UpperCAmelCase_   :						str=8  ,					UpperCAmelCase_   :						Optional[int]=6  ,					UpperCAmelCase_   :						Tuple=2_048  ,					UpperCAmelCase_   :						Optional[int]=8  ,					UpperCAmelCase_   :						Tuple=0.0  ,					UpperCAmelCase_   :						Optional[int]=0.0  ,					UpperCAmelCase_   :						str=True  ,					UpperCAmelCase_   :						str="relu"  ,					UpperCAmelCase_   :						Dict=256  ,					UpperCAmelCase_   :						Union[str, Any]=0.1  ,					UpperCAmelCase_   :						List[str]=0.0  ,					UpperCAmelCase_   :						Tuple=0.0  ,					UpperCAmelCase_   :						Tuple=0.02  ,					UpperCAmelCase_   :						Dict=1.0  ,					UpperCAmelCase_   :						Dict=False  ,					UpperCAmelCase_   :						int="sine"  ,					UpperCAmelCase_   :						Dict="resnet50"  ,					UpperCAmelCase_   :						Dict=True  ,					UpperCAmelCase_   :						int=False  ,					UpperCAmelCase_   :						str=1  ,					UpperCAmelCase_   :						Optional[int]=5  ,					UpperCAmelCase_   :						List[str]=2  ,					UpperCAmelCase_   :						Optional[Any]=1  ,					UpperCAmelCase_   :						str=1  ,					UpperCAmelCase_   :						str=5  ,					UpperCAmelCase_   :						Dict=2  ,					UpperCAmelCase_   :						Tuple=0.1  ,					**UpperCAmelCase_   :						Tuple  ,					):
			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__				:					Tuple					=					CONFIG_MAPPING['resnet'](out_features=['stage4'])
					elif isinstance(UpperCAmelCase_  ,					UpperCAmelCase_):
							UpperCamelCase__				:					Optional[Any]					=					backbone_config.get('model_type')
							UpperCamelCase__				:					Union[str, Any]					=					CONFIG_MAPPING[backbone_model_type]
							UpperCamelCase__				:					Union[str, Any]					=					config_class.from_dict(UpperCAmelCase_)
					# set timm attributes to None
					UpperCamelCase__				:					Union[str, Any]					=					None, None, None

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

	@property
	def      __UpperCamelCase							(      self   :						Any):
			return self.encoder_attention_heads





	@property
	def      __UpperCamelCase							(      self   :						List[Any]):
			return self.d_model







class       __lowercase					(__lowerCamelCase    ):
	_lowerCamelCase              =  version.parse('''1.11'''    )

	@property
	def      __UpperCamelCase							(      self   :						List[Any]):
			return OrderedDict(
			    [
			        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
			        ('pixel_mask', {0: 'batch'}),
			    ])

	@property
	def      __UpperCamelCase							(      self   :						Dict):
			return 1e-5





	@property
	def      __UpperCamelCase							(      self   :						List[Any]):
			return 12












'''simple docstring'''




import requests
from bsa import BeautifulSoup







def       __UpperCAmelCase						(  lowerCamelCase_ = "AAPL")							->     str:
		UpperCamelCase__				:					str					=					f'https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'
		UpperCamelCase__				:					Optional[Any]					=					BeautifulSoup(requests.get(lowerCamelCase_).text     ,      'html.parser')
		UpperCamelCase__				:					Union[str, Any]					=					'My(6px) Pos(r) smartphone_Mt(6px)'
		return soup.find('div'     ,      class_=class_).find('span').text


if __name__ == "__main__":
		for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
				print(f'''Current {symbol:<4} stock price is {stock_price(symbol):>8}''')




