


from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__UpperCamelCase    :						Any     =							200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
__UpperCamelCase    :						Any     =							50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
__UpperCamelCase    :						int     =							0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))





def      _UpperCAmelCase       (     UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			Any      ):
      """simple docstring"""




      __lowerCamelCase     :      Dict       						=						len([g for position, g in enumerate(UpperCAmelCase      ) if g == main_target[position]]      )
      return (item, float(UpperCAmelCase      ))





def      _UpperCAmelCase       (     UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			int      ):
      """simple docstring"""




      __lowerCamelCase     :      int       						=						random.randint(0						,	len(UpperCAmelCase      ) - 1      )
      __lowerCamelCase     :      Tuple       						=						parent_a[:random_slice] + parent_a[random_slice:]
      __lowerCamelCase     :      Dict       						=						parent_a[:random_slice] + parent_a[random_slice:]
      return (child_a, child_a)





def      _UpperCAmelCase       (     UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			Optional[int]      ):
      """simple docstring"""




      __lowerCamelCase     :      str       						=						list(UpperCAmelCase      )
      if random.uniform(0						,	1      ) < MUTATION_PROBABILITY:
            __lowerCamelCase     :      List[Any]       						=						random.choice(UpperCAmelCase      )
      return "".join(UpperCAmelCase      )





def      _UpperCAmelCase       (     UpperCAmelCase      :			Any						,	UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			Tuple						,	):
      """simple docstring"""




      __lowerCamelCase     :      Tuple       						=						[]
      # Generate more children proportionally to the fitness score.
      __lowerCamelCase     :      Optional[Any]       						=						int(parent_a[1] * 100      ) + 1
      __lowerCamelCase     :      Optional[Any]       						=						10 if child_n >= 10 else child_n
      for _ in range(UpperCAmelCase      ):
            __lowerCamelCase     :      Optional[int]       						=						population_score[random.randint(0						,	UpperCAmelCase      )][0]

            __lowerCamelCase     :      Tuple       						=						crossover(parent_a[0]						,	UpperCAmelCase      )
            # Append new string to the population list.
            pop.append(mutate(UpperCAmelCase						,	UpperCAmelCase      )      )
            pop.append(mutate(UpperCAmelCase						,	UpperCAmelCase      )      )
      return pop





def      _UpperCAmelCase       (     UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			Any = True      ):
      """simple docstring"""




      if N_POPULATION < N_SELECTED:
            __lowerCamelCase     :      Dict       						=						f"""{N_POPULATION} must be bigger than {N_SELECTED}"""
            raise ValueError(UpperCAmelCase      )
      # Verify that the target contains no genes besides the ones inside genes variable.
      __lowerCamelCase     :      Any       						=						sorted({c for c in target if c not in genes}      )
      if not_in_genes_list:
            __lowerCamelCase     :      int       						=						f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
            raise ValueError(UpperCAmelCase      )

      # Generate random starting population.
      __lowerCamelCase     :      Dict       						=						[]
      for _ in range(UpperCAmelCase      ):
            population.append("""""".join([random.choice(UpperCAmelCase      ) for i in range(len(UpperCAmelCase      )      )]      )      )

      # Just some logs to know what the algorithms is doing.
      __lowerCamelCase     :      List[Any]       						=						0, 0

      # This loop will end when we find a perfect match for our target.
      while True:
            generation += 1
            total_population += len(UpperCAmelCase      )

            # Random population created. Now it's time to evaluate.

            # Adding a bit of concurrency can make everything faster,
            #
            # import concurrent.futures
            # population_score: list[tuple[str, float]] = []
            # with concurrent.futures.ThreadPoolExecutor(
            #                                   max_workers=NUM_WORKERS) as executor:
            #     futures = {executor.submit(evaluate, item) for item in population}
            #     concurrent.futures.wait(futures)
            #     population_score = [item.result() for item in futures]
            #
            # but with a simple algorithm like this, it will probably be slower.
            # We just need to call evaluate for every item inside the population.
            __lowerCamelCase     :      str       						=						[evaluate(UpperCAmelCase						,	UpperCAmelCase      ) for item in population]

            # Check if there is a matching evolution.
            __lowerCamelCase     :      Union[str, Any]       						=						sorted(UpperCAmelCase						,	key=lambda UpperCAmelCase      : x[1]						,	reverse=UpperCAmelCase      )
            if population_score[0][0] == target:
                  return (generation, total_population, population_score[0][0])

            # Print the best result every 10 generation.
            # Just to know that the algorithm is working.
            if debug and generation % 10 == 0:
                  print(
                      f"""\nGeneration: {generation}"""
                      f"""\nTotal Population:{total_population}"""
                      f"""\nBest score: {population_score[0][1]}"""
                      f"""\nBest string: {population_score[0][0]}"""      )

            # Flush the old population, keeping some of the best evolutions.
            # Keeping this avoid regression of evolution.
            __lowerCamelCase     :      Dict       						=						population[: int(N_POPULATION / 3      )]
            population.clear()
            population.extend(UpperCAmelCase      )
            # Normalize population score to be between 0 and 1.
            __lowerCamelCase     :      str       						=						[
                (item, score / len(UpperCAmelCase      )) for item, score in population_score
            ]

            # This is selection
            for i in range(UpperCAmelCase      ):
                  population.extend(select(population_score[int(UpperCAmelCase      )]						,	UpperCAmelCase						,	UpperCAmelCase      )      )
                  # Check if the population has already reached the maximum value and if so,
                  # break the cycle.  If this check is disabled, the algorithm will take
                  # forever to compute large strings, but will also calculate small strings in
                  # a far fewer generations.
                  if len(UpperCAmelCase      ) > N_POPULATION:
                        break


if __name__ == "__main__":
    __UpperCamelCase    :						str     =							(
        """This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"""
    )
    __UpperCamelCase    :						Any     =							list(
        ' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'
        'nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'
    )
    __UpperCamelCase    :						int     =							basic(target_str, genes_list)
    print(
        F'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
    )







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


__UpperCamelCase    :						Optional[int]     =							logging.get_logger(__name__)

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



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   a_				:							Optional[int]    = "conditional_detr"
   a_				:							Union[str, Any]    = ["past_key_values"]
   a_				:							str    = {
       "hidden_size": "d_model",
       "num_attention_heads": "encoder_attention_heads",
   }

   def __init__(  self  : Dict       ,  _lowerCamelCase  : Optional[int]=True       ,  _lowerCamelCase  : int=None       ,  _lowerCamelCase  : Optional[int]=3       ,  _lowerCamelCase  : Tuple=3_0_0       ,  _lowerCamelCase  : Any=6       ,  _lowerCamelCase  : Union[str, Any]=2_0_4_8       ,  _lowerCamelCase  : List[str]=8       ,  _lowerCamelCase  : Any=6       ,  _lowerCamelCase  : Any=2_0_4_8       ,  _lowerCamelCase  : List[Any]=8       ,  _lowerCamelCase  : Optional[int]=0.0       ,  _lowerCamelCase  : Any=0.0       ,  _lowerCamelCase  : Tuple=True       ,  _lowerCamelCase  : Union[str, Any]="relu"       ,  _lowerCamelCase  : str=2_5_6       ,  _lowerCamelCase  : int=0.1       ,  _lowerCamelCase  : List[str]=0.0       ,  _lowerCamelCase  : Tuple=0.0       ,  _lowerCamelCase  : List[str]=0.02       ,  _lowerCamelCase  : Union[str, Any]=1.0       ,  _lowerCamelCase  : Optional[int]=False       ,  _lowerCamelCase  : List[str]="sine"       ,  _lowerCamelCase  : Optional[Any]="resnet50"       ,  _lowerCamelCase  : List[str]=True       ,  _lowerCamelCase  : Dict=False       ,  _lowerCamelCase  : Optional[int]=2       ,  _lowerCamelCase  : Dict=5       ,  _lowerCamelCase  : List[Any]=2       ,  _lowerCamelCase  : Any=1       ,  _lowerCamelCase  : Any=1       ,  _lowerCamelCase  : Dict=2       ,  _lowerCamelCase  : Dict=5       ,  _lowerCamelCase  : Dict=2       ,  _lowerCamelCase  : Union[str, Any]=0.25       ,  **_lowerCamelCase  : Tuple       ,  ):






         '''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."""							)
                     __lowerCamelCase     :      Any       						=						CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]							)
               elif isinstance(_lowerCamelCase       ,  _lowerCamelCase							):
                     __lowerCamelCase     :      str       						=						backbone_config.get("""model_type"""							)
                     __lowerCamelCase     :      Optional[Any]       						=						CONFIG_MAPPING[backbone_model_type]
                     __lowerCamelCase     :      Optional[Any]       						=						config_class.from_dict(_lowerCamelCase							)

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

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






         '''simple docstring'''



         return self.encoder_attention_heads

   @property
   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         return self.d_model


   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      int       						=						copy.deepcopy(self.__dict__							)
         if self.backbone_config is not None:
               __lowerCamelCase     :      Dict       						=						self.backbone_config.to_dict()
         __lowerCamelCase     :      int       						=						self.__class__.model_type
         return output



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   a_				:							Dict    = version.parse("1.11"						)

   @property
   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



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

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






         '''simple docstring'''



         return 1E-5


   @property
   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         return 1_2



