





"""simple docstring"""





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


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					=   {
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      'conditional_detr'
      _SCREAMING_SNAKE_CASE					=      ['past_key_values']
      _SCREAMING_SNAKE_CASE					=      {
          'hidden_size': 'd_model',
          'num_attention_heads': 'encoder_attention_heads',
      }





      def __init__(       self      ,						lowercase=True      ,						lowercase=None      ,						lowercase=3      ,						lowercase=300      ,						lowercase=6      ,						lowercase=2_048      ,						lowercase=8      ,						lowercase=6      ,						lowercase=2_048      ,						lowercase=8      ,						lowercase=0.0      ,						lowercase=0.0      ,						lowercase=True      ,						lowercase="relu"      ,						lowercase=256      ,						lowercase=0.1      ,						lowercase=0.0      ,						lowercase=0.0      ,						lowercase=0.02      ,						lowercase=1.0      ,						lowercase=False      ,						lowercase="sine"      ,						lowercase="resnet50"      ,						lowercase=True      ,						lowercase=False      ,						lowercase=2      ,						lowercase=5      ,						lowercase=2      ,						lowercase=1      ,						lowercase=1      ,						lowercase=2      ,						lowercase=5      ,						lowercase=2      ,						lowercase=0.25      ,						**lowercase      ,						)		->     str:
            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.""" )
                        lowerCAmelCase  						=       CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
                  elif isinstance(lowercase      ,						lowercase ):
                        lowerCAmelCase  						=       backbone_config.get("""model_type""" )
                        lowerCAmelCase  						=       CONFIG_MAPPING[backbone_model_type]
                        lowerCAmelCase  						=       config_class.from_dict(lowercase )

            lowerCAmelCase  						=       use_timm_backbone
            lowerCAmelCase  						=       backbone_config
            lowerCAmelCase  						=       num_channels
            lowerCAmelCase  						=       num_queries
            lowerCAmelCase  						=       d_model
            lowerCAmelCase  						=       encoder_ffn_dim
            lowerCAmelCase  						=       encoder_layers
            lowerCAmelCase  						=       encoder_attention_heads
            lowerCAmelCase  						=       decoder_ffn_dim
            lowerCAmelCase  						=       decoder_layers
            lowerCAmelCase  						=       decoder_attention_heads
            lowerCAmelCase  						=       dropout
            lowerCAmelCase  						=       attention_dropout
            lowerCAmelCase  						=       activation_dropout
            lowerCAmelCase  						=       activation_function
            lowerCAmelCase  						=       init_std
            lowerCAmelCase  						=       init_xavier_std
            lowerCAmelCase  						=       encoder_layerdrop
            lowerCAmelCase  						=       decoder_layerdrop
            lowerCAmelCase  						=       encoder_layers
            lowerCAmelCase  						=       auxiliary_loss
            lowerCAmelCase  						=       position_embedding_type
            lowerCAmelCase  						=       backbone
            lowerCAmelCase  						=       use_pretrained_backbone
            lowerCAmelCase  						=       dilation
            # Hungarian matcher
            lowerCAmelCase  						=       class_cost
            lowerCAmelCase  						=       bbox_cost
            lowerCAmelCase  						=       giou_cost
            # Loss coefficients
            lowerCAmelCase  						=       mask_loss_coefficient
            lowerCAmelCase  						=       dice_loss_coefficient
            lowerCAmelCase  						=       cls_loss_coefficient
            lowerCAmelCase  						=       bbox_loss_coefficient
            lowerCAmelCase  						=       giou_loss_coefficient
            lowerCAmelCase  						=       focal_alpha
            super().__init__(is_encoder_decoder=lowercase      ,						**lowercase )





      @property
      def     _snake_case			(       self )		->     int:
            return self.encoder_attention_heads





      @property
      def     _snake_case			(       self )		->     int:
            return self.d_model





      def     _snake_case			(       self )		->     Optional[Any]:
            lowerCAmelCase  						=       copy.deepcopy(self.__dict__ )
            if self.backbone_config is not None:
                  lowerCAmelCase  						=       self.backbone_config.to_dict()
            lowerCAmelCase  						=       self.__class__.model_type
            return output







class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      version.parse('1.11'					)





      @property
      def     _snake_case			(       self )		->     Mapping[str, Mapping[int, str]]:
            return OrderedDict(
                [
                    ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                    ("""pixel_mask""", {0: """batch"""}),
                ] )





      @property
      def     _snake_case			(       self )		->     float:
            return 1e-5





      @property
      def     _snake_case			(       self )		->     int:
            return 12









def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							global f  # a global dp table for knapsack
							if f[i][j] < 0:
														if j < wt[i - 1]:
																					snake_case_							=    mf_knapsack(i - 1   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )
														else:
																					snake_case_							=    max(
																					    mf_knapsack(i - 1   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )   ,							mf_knapsack(i - 1   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							j - wt[i - 1]      ) + val[i - 1]   ,							)
														snake_case_							=    val
							return f[i][j]





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    [[0] * (w + 1) for _ in range(n + 1      )]

							for i in range(1   ,							n + 1      ):
														for w_ in range(1   ,							w + 1      ):
																					if wt[i - 1] <= w_:
																												snake_case_							=    max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]   ,							dp[i - 1][w_]      )
																					else:
																												snake_case_							=    dp[i - 1][w_]

							return dp[n][w_], dp





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							if not (isinstance(UpperCamelCase__   ,							(list, tuple)      ) and isinstance(UpperCamelCase__   ,							(list, tuple)      )):
														raise ValueError(
														    'Both the weights and values vectors must be either lists or tuples'      )

							snake_case_							=    len(UpperCamelCase__      )
							if num_items != len(UpperCamelCase__      ):
														snake_case_							=    (
														    'The number of weights must be the same as the number of values.\n'
														    F'''But got {num_items} weights and {len(UpperCamelCase__      )} values'''
														)
														raise ValueError(UpperCamelCase__      )
							for i in range(UpperCamelCase__      ):
														if not isinstance(wt[i]   ,							UpperCamelCase__      ):
																					snake_case_							=    (
																					    'All weights must be integers but got weight of '
																					    F'''type {type(wt[i]      )} at index {i}'''
																					)
																					raise TypeError(UpperCamelCase__      )

							snake_case_				,    snake_case_							=    knapsack(UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )
							snake_case_							=    set()
							_construct_solution(UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )

							return optimal_val, example_optional_set





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							if i > 0 and j > 0:
														if dp[i - 1][j] == dp[i][j]:
																					_construct_solution(UpperCamelCase__   ,							UpperCamelCase__   ,							i - 1   ,							UpperCamelCase__   ,							UpperCamelCase__      )
														else:
																					optimal_set.add(UpperCamelCase__      )
																					_construct_solution(UpperCamelCase__   ,							UpperCamelCase__   ,							i - 1   ,							j - wt[i - 1]   ,							UpperCamelCase__      )


if __name__ == "__main__":
					_UpperCAmelCase		:   Tuple										=       [3, 2, 4, 4]
					_UpperCAmelCase		:   Optional[Any]										=       [4, 3, 2, 3]
					_UpperCAmelCase		:   List[str]										=       4
					_UpperCAmelCase		:   str										=       6
					_UpperCAmelCase		:   Tuple										=       [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
					_UpperCAmelCase				,						_UpperCAmelCase		:   List[Any]										=       knapsack(w, wt, val, n)
					print(optimal_solution)
					print(mf_knapsack(n, wt, val, w))  # switched the n and w

					# testing the dynamic programming problem with example
					# the optimal subset for the above example are items 3 and 4
					_UpperCAmelCase				,						_UpperCAmelCase		:   Any										=       knapsack_with_example_solution(w, wt, val)
					assert optimal_solution == 8
					assert optimal_subset == {3, 4}
					print("""optimal_value = """, optimal_solution)
					print("""An optimal subset corresponding to the optimal value""", optimal_subset)




