"""simple docstring"""



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


__lowercase		:		List[Any]														=							{
    "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
}

__lowercase		:		str														=							logging.get_logger(__name__)



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					Tuple							=					'''maskformer'''
    UpperCamelCase_	:					Optional[int]							=					{'''hidden_size''': '''mask_feature_size'''}
    UpperCamelCase_	:					List[str]							=					['''resnet''', '''swin''']
    UpperCamelCase_	:					Dict							=					['''detr''']
    def __init__(		self							:     Optional[Any]	,      A_							:     int = 256	,      A_							:     int = 256	,      A_							:     float = 0.1	,      A_							:     bool = False	,      A_							:     Optional[Dict] = None	,      A_							:     Optional[Dict] = None	,      A_							:     float = 0.02	,      A_							:     float = 1.0	,      A_							:     float = 1.0	,      A_							:     float = 1.0	,      A_							:     float = 20.0	,      A_							:     Optional[bool] = None	,      **A_							:     Optional[Any]	,      )    ->       List[str]:
         if backbone_config is None:
              # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
              __snake_case				=							SwinConfig(
                  image_size=384	,      in_channels=3	,      patch_size=4	,      embed_dim=128	,      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(A_	,      A_    ):
              __snake_case				=							backbone_config.pop('''model_type'''    )
              __snake_case				=							CONFIG_MAPPING[backbone_model_type]
              __snake_case				=							config_class.from_dict(A_    )

         # 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
              __snake_case				=							DetrConfig()
         else:
              # verify that the decoder is supported
              __snake_case				=							(
                  decoder_config.pop('''model_type'''    ) if isinstance(A_	,      A_    ) 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(A_	,      A_    ):
                   __snake_case				=							CONFIG_MAPPING[decoder_type]
                   __snake_case				=							config_class.from_dict(A_    )

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

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







    def  lowercase					(		self							:     Dict    )    ->       Dict[str, any]:
         __snake_case				=							copy.deepcopy(self.__dict__    )
         __snake_case				=							self.backbone_config.to_dict()
         __snake_case				=							self.decoder_config.to_dict()
         __snake_case				=							self.__class__.model_type
         return output
"""simple docstring"""



import math
import random
from typing import Any

from .hill_climbing import SearchProblem





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case = True,      snake_case = math.inf,      snake_case = -math.inf,      snake_case = math.inf,      snake_case = -math.inf,      snake_case = False,      snake_case = 1_00,      snake_case = 0.01,      snake_case = 1,      ):
     __snake_case				=							False
     __snake_case				=							search_prob
     __snake_case				=							start_temperate
     __snake_case				=							[]
     __snake_case				=							0
     __snake_case				=							None

     while not search_end:
          __snake_case				=							current_state.score()
          if best_state is None or current_score > best_state.score():
               __snake_case				=							current_state
          scores.append(snake_case)
          iterations += 1
          __snake_case				=							None
          __snake_case				=							current_state.get_neighbors()
          while (
              next_state is None and neighbors
          ):  # till we do not find a neighbor that we can move to
               __snake_case				=							random.randint(0,      len(snake_case) - 1)  # picking a random neighbor
               __snake_case				=							neighbors.pop(snake_case)
               __snake_case				=							picked_neighbor.score() - current_score

               if (
                   picked_neighbor.x > max_x
                   or picked_neighbor.x < min_x
                   or picked_neighbor.y > max_y
                   or picked_neighbor.y < min_y
               ):
                    continue  # neighbor outside our bounds

               if not find_max:
                    __snake_case				=							change * -1  # in case we are finding minimum
               if change > 0:  # improves the solution
                    __snake_case				=							picked_neighbor
               else:
                    __snake_case				=							(math.e) ** (
                        change / current_temp
                    )  # probability generation function
                    if random.random() < probability:  # random number within probability
                         __snake_case				=							picked_neighbor
          __snake_case				=							current_temp - (current_temp * rate_of_decrease)

          if current_temp < threshold_temp or next_state is None:
               # temperature below threshold, or could not find a suitable neighbor
               __snake_case				=							True
          else:
               __snake_case				=							next_state

     if visualization:
          from matplotlib import pyplot as plt

          plt.plot(range(snake_case),      snake_case)
          plt.xlabel('''Iterations''')
          plt.ylabel('''Function values''')
          plt.show()
     return best_state


if __name__ == "__main__":





       def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
            return (x**2) + (y**2)

       # starting the problem with initial coordinates (12, 47)
       __lowercase		:		Dict														=							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		Union[str, Any]														=							simulated_annealing(
           prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
       )
       print(
           "The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
           F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
       )

       # starting the problem with initial coordinates (12, 47)
       __lowercase		:		Any														=							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		int														=							simulated_annealing(
           prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
       )
       print(
           "The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
           F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
       )





       def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
            return (3 * x**2) - (6 * y)

       __lowercase		:		List[str]														=							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		Dict														=							simulated_annealing(prob, find_max=False, visualization=True)
       print(
           "The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
           F"""{local_min.score()}"""
       )

       __lowercase		:		Any														=							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		Tuple														=							simulated_annealing(prob, find_max=True, visualization=True)




       print(
           "The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
           F"""{local_min.score()}"""
       )