


'''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  lowerCAmelCase_						(       __lowerCamelCase      ):





  """simple docstring"""



  a_       :Union[str, Any]												='''conditional_detr'''
  a_       :Dict												=['''past_key_values''']
  a_       :List[Any]												={
      '''hidden_size''': '''d_model''',
      '''num_attention_heads''': '''encoder_attention_heads''',
  }

  def __init__(					self     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							List[Any]=True				,							SCREAMING_SNAKE_CASE__     :							Tuple=None				,							SCREAMING_SNAKE_CASE__     :							List[Any]=3				,							SCREAMING_SNAKE_CASE__     :							str=3_0_0				,							SCREAMING_SNAKE_CASE__     :							Tuple=6				,							SCREAMING_SNAKE_CASE__     :							Any=2_0_4_8				,							SCREAMING_SNAKE_CASE__     :							List[str]=8				,							SCREAMING_SNAKE_CASE__     :							str=6				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=2_0_4_8				,							SCREAMING_SNAKE_CASE__     :							str=8				,							SCREAMING_SNAKE_CASE__     :							List[str]=0.0				,							SCREAMING_SNAKE_CASE__     :							Dict=0.0				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=True				,							SCREAMING_SNAKE_CASE__     :							str="relu"				,							SCREAMING_SNAKE_CASE__     :							Dict=2_5_6				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=0.1				,							SCREAMING_SNAKE_CASE__     :							int=0.0				,							SCREAMING_SNAKE_CASE__     :							Dict=0.0				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=0.0_2				,							SCREAMING_SNAKE_CASE__     :							int=1.0				,							SCREAMING_SNAKE_CASE__     :							str=False				,							SCREAMING_SNAKE_CASE__     :							List[Any]="sine"				,							SCREAMING_SNAKE_CASE__     :							List[str]="resnet50"				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=True				,							SCREAMING_SNAKE_CASE__     :							Tuple=False				,							SCREAMING_SNAKE_CASE__     :							List[Any]=2				,							SCREAMING_SNAKE_CASE__     :							Tuple=5				,							SCREAMING_SNAKE_CASE__     :							List[Any]=2				,							SCREAMING_SNAKE_CASE__     :							str=1				,							SCREAMING_SNAKE_CASE__     :							List[str]=1				,							SCREAMING_SNAKE_CASE__     :							int=2				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=5				,							SCREAMING_SNAKE_CASE__     :							int=2				,							SCREAMING_SNAKE_CASE__     :							str=0.2_5				,							**SCREAMING_SNAKE_CASE__     :							List[str]				,							):





       '''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."""						)
                 __a       =   CONFIG_MAPPING["resnet"](out_features=["""stage4"""]						)
            elif isinstance(a_				,							a_						):
                 __a       =   backbone_config.get("""model_type"""						)
                 __a       =   CONFIG_MAPPING[backbone_model_type]
                 __a       =   config_class.from_dict(a_						)

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

  @property
  def __a						(					self     :							int						):





       '''simple docstring'''

       return self.encoder_attention_heads

  @property
  def __a						(					self     :							int						):





       '''simple docstring'''

       return self.d_model


  def __a						(					self     :							str						):





       '''simple docstring'''

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


class  lowerCAmelCase_						(       __lowerCamelCase      ):





  """simple docstring"""



  a_       :List[str]												=version.parse("""1.11"""      )

  @property
  def __a						(					self     :							Any						):





       '''simple docstring'''

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

  @property
  def __a						(					self     :							Any						):





       '''simple docstring'''

       return 1E-5


  @property
  def __a						(					self     :							int						):





       '''simple docstring'''

       return 1_2










'''simple docstring'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					Dict:
     """simple docstring"""




     __a       =   []
     __a       =   set({"""(""", """[""", """{"""}		)
     __a       =   set({""")""", """]""", """}"""}		)
     __a       =   {"""{""": """}""", """[""": """]""", """(""": """)"""}

     for i in range(len(__SCREAMING_SNAKE_CASE		)		):
          if s[i] in open_brackets:
               stack.append(s[i]		)

          elif s[i] in closed_brackets and (
              len(__SCREAMING_SNAKE_CASE		) == 0 or (len(__SCREAMING_SNAKE_CASE		) > 0 and open_to_closed[stack.pop()] != s[i])
          ):
               return False

     return len(__SCREAMING_SNAKE_CASE		) == 0




def        __lowercase						(      )				->					List[str]:
     """simple docstring"""




     __a       =   input("""Enter sequence of brackets: """		)
     if is_balanced(__SCREAMING_SNAKE_CASE		):
          print(__SCREAMING_SNAKE_CASE				,  """is balanced"""		)
     else:
          print(__SCREAMING_SNAKE_CASE				,  """is not balanced"""		)


if __name__ == "__main__":
 main()






