


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


snake_case							: List[str]      =   {
    '''facebook/maskformer-swin-base-ade''': (
        '''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

snake_case							: List[str]      =   logging.get_logger(__name__)







class  snake_case_     (lowerCamelCase_   ):
					UpperCAmelCase__      :					Dict					   =    '''maskformer'''
					UpperCAmelCase__      :					Any					   =    {'''hidden_size''': '''mask_feature_size'''}
					UpperCAmelCase__      :					Union[str, Any]					   =    ['''resnet''', '''swin''']
					UpperCAmelCase__      :					Any					   =    ['''detr''']
					def __init__(   self     :List[str]    ,__snake_case     :int = 2_56    ,__snake_case     :int = 2_56    ,__snake_case     :float = 0.1    ,__snake_case     :bool = False    ,__snake_case     :Optional[Dict] = None    ,__snake_case     :Optional[Dict] = None    ,__snake_case     :float = 0.02    ,__snake_case     :float = 1.0    ,__snake_case     :float = 1.0    ,__snake_case     :float = 1.0    ,__snake_case     :float = 20.0    ,__snake_case     :Optional[bool] = None    ,**__snake_case     :List[Any]    ,)      ->					Dict:
												if backbone_config is None:
																			# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
																			a__    =  SwinConfig(
																			    image_size=3_84    ,in_channels=3    ,patch_size=4    ,embed_dim=1_28    ,depths=[2, 2, 18, 2]    ,num_heads=[4, 8, 16, 32]    ,window_size=12    ,drop_path_rate=0.3    ,out_features=['stage1', 'stage2', 'stage3', 'stage4']    ,)

												if isinstance(__snake_case    ,__snake_case   ):
																			a__    =  backbone_config.pop('model_type'   )
																			a__    =  CONFIG_MAPPING[backbone_model_type]
																			a__    =  config_class.from_dict(__snake_case   )

												# verify that the backbone is supported
												if backbone_config.model_type not in self.backbones_supported:
																			logger.warning_once(
																			    F'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '
																			    F'Supported model types: {",".join(self.backbones_supported   )}'   )

												if decoder_config is None:
																			# fall back to https://huggingface.co/facebook/detr-resnet-50
																			a__    =  DetrConfig()
												else:
																			# verify that the decoder is supported
																			a__    =  (
																			    decoder_config.pop('model_type'   ) if isinstance(__snake_case    ,__snake_case   ) else decoder_config.model_type
																			)
																			if decoder_type not in self.decoders_supported:
																										raise ValueError(
																										    F'Transformer Decoder {decoder_type} not supported, please use one of'
																										    F' {",".join(self.decoders_supported   )}'   )
																			if isinstance(__snake_case    ,__snake_case   ):
																										a__    =  CONFIG_MAPPING[decoder_type]
																										a__    =  config_class.from_dict(__snake_case   )

												a__    =  backbone_config
												a__    =  decoder_config
												# main feature dimension for the model
												a__    =  fpn_feature_size
												a__    =  mask_feature_size
												# initializer
												a__    =  init_std
												a__    =  init_xavier_std
												# Hungarian matcher && loss
												a__    =  cross_entropy_weight
												a__    =  dice_weight
												a__    =  mask_weight
												a__    =  use_auxiliary_loss
												a__    =  no_object_weight
												a__    =  output_auxiliary_logits

												a__    =  self.decoder_config.encoder_attention_heads
												a__    =  self.decoder_config.num_hidden_layers
												super().__init__(**__snake_case   )
					@classmethod
					def 				lowerCamelCase__(   cls     :Union[str, Any]    ,__snake_case     :PretrainedConfig    ,__snake_case     :PretrainedConfig    ,**__snake_case     :Any   )      ->					str:
												return cls(
												    backbone_config=__snake_case    ,decoder_config=__snake_case    ,**__snake_case    ,)



					def 				lowerCamelCase__(   self     :Dict   )      ->					Dict[str, any]:
												a__    =  copy.deepcopy(self.__dict__   )
												a__    =  self.backbone_config.to_dict()
												a__    =  self.decoder_config.to_dict()
												a__    =  self.__class__.model_type
												return output








def 							__lowercase     (				__lowerCAmelCase							:       list      ,					__lowerCAmelCase							:       list      ,					__lowerCAmelCase							:       int     ):
							if len(__lowerCAmelCase     ) != len(__lowerCAmelCase     ):
														raise ValueError('The length of profit and weight must be same.'     )
							if max_weight <= 0:
														raise ValueError('max_weight must greater than zero.'     )
							if any(p < 0 for p in profit     ):
														raise ValueError('Profit can not be negative.'     )
							if any(w < 0 for w in weight     ):
														raise ValueError('Weight can not be negative.'     )

							# List created to store profit gained for the 1kg in case of each weight
							# respectively.  Calculate and append profit/weight for each element.
							a__    =  [p / w for p, w in zip(__lowerCAmelCase      ,					__lowerCAmelCase     )]

							# Creating a copy of the list and sorting profit/weight in ascending order
							a__    =  sorted(__lowerCAmelCase     )

							# declaring useful variables
							a__    =  len(__lowerCAmelCase     )
							a__    =  0
							a__    =  0
							a__    =  0

							# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
							while limit <= max_weight and i < length:
														# flag value for encountered greatest element in sorted_profit_by_weight
														a__    =  sorted_profit_by_weight[length - i - 1]
														a__    =  profit_by_weight.index(__lowerCAmelCase     )
														a__    =  -1

														# check if the weight encountered is less than the total weight
														# encountered before.
														if max_weight - limit >= weight[index]:
																					limit += weight[index]
																					# Adding profit gained for the given weight 1 ===
																					# weight[index]/weight[index]
																					gain += 1 * profit[index]
														else:
																					# Since the weight encountered is greater than limit, therefore take the
																					# required number of remaining kgs and calculate profit for it.
																					# weight remaining / weight[index]
																					gain += (max_weight - limit) / weight[index] * profit[index]
																					break
														i += 1
							return gain


if __name__ == "__main__":
						print(
						    '''Input profits, weights, and then max_weight (all positive ints) separated by '''
						    '''spaces.'''
						)

						snake_case							: Tuple      =   [int(x) for x in input('''Input profits separated by spaces: ''').split()]
						snake_case							: Optional[int]      =   [int(x) for x in input('''Input weights separated by spaces: ''').split()]
						snake_case							: List[str]      =   int(input('''Max weight allowed: '''))

						# Function Call
						calc_profit(profit, weight, max_weight)




