






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

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






class      a						(					__snake_case		):
    SCREAMING_SNAKE_CASE						:     Union[str, Any]						= """conditional_detr"""
    SCREAMING_SNAKE_CASE						:     Union[str, Any]						= ["""past_key_values"""]
    SCREAMING_SNAKE_CASE						:     int						= {
        """hidden_size""": """d_model""",
        """num_attention_heads""": """encoder_attention_heads""",
    }






    def __init__(       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							int=True       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=None       ,						__SCREAMING_SNAKE_CASE     :							int=3       ,						__SCREAMING_SNAKE_CASE     :							List[str]=300       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=6       ,						__SCREAMING_SNAKE_CASE     :							int=2048       ,						__SCREAMING_SNAKE_CASE     :							List[Any]=8       ,						__SCREAMING_SNAKE_CASE     :							Any=6       ,						__SCREAMING_SNAKE_CASE     :							str=2048       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=8       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=0.0       ,						__SCREAMING_SNAKE_CASE     :							Dict=0.0       ,						__SCREAMING_SNAKE_CASE     :							Any=True       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]="relu"       ,						__SCREAMING_SNAKE_CASE     :							Any=256       ,						__SCREAMING_SNAKE_CASE     :							List[Any]=0.1       ,						__SCREAMING_SNAKE_CASE     :							str=0.0       ,						__SCREAMING_SNAKE_CASE     :							List[Any]=0.0       ,						__SCREAMING_SNAKE_CASE     :							Any=0.02       ,						__SCREAMING_SNAKE_CASE     :							List[str]=1.0       ,						__SCREAMING_SNAKE_CASE     :							List[str]=False       ,						__SCREAMING_SNAKE_CASE     :							List[str]="sine"       ,						__SCREAMING_SNAKE_CASE     :							Any="resnet50"       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=True       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=False       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=2       ,						__SCREAMING_SNAKE_CASE     :							Tuple=5       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=2       ,						__SCREAMING_SNAKE_CASE     :							List[Any]=1       ,						__SCREAMING_SNAKE_CASE     :							Any=1       ,						__SCREAMING_SNAKE_CASE     :							int=2       ,						__SCREAMING_SNAKE_CASE     :							List[Any]=5       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=2       ,						__SCREAMING_SNAKE_CASE     :							Dict=0.25       ,						**__SCREAMING_SNAKE_CASE     :							Union[str, Any]       ,						)     ->		Optional[int]:






      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(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					):
          lowerCamelCase_         =						backbone_config.get('model_type'					)
          lowerCamelCase_         =						CONFIG_MAPPING[backbone_model_type]
          lowerCamelCase_         =						config_class.from_dict(__SCREAMING_SNAKE_CASE					)

      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=__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)






    @property
    def    UpperCamelCase       (       self     :							Dict					)     ->		Optional[int]:
      return self.encoder_attention_heads






    @property
    def    UpperCamelCase       (       self     :							Any					)     ->		Union[str, Any]:
      return self.d_model






    def    UpperCamelCase       (       self     :							Any					)     ->		Dict:
      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      a						(					__snake_case		):
    SCREAMING_SNAKE_CASE						:     int						= version.parse("""1.11"""		)






    @property
    def    UpperCamelCase       (       self     :							Tuple					)     ->		Any:
      return OrderedDict(
          [
              ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
              ('pixel_mask', {0: 'batch'}),
          ]					)






    @property
    def    UpperCamelCase       (       self     :							str					)     ->		List[Any]:
      return 1e-5






    @property
    def    UpperCamelCase       (       self     :							Optional[int]					)     ->		List[Any]:
      return 12








"""simple docstring"""






def      lowerCamelCase       (     _UpperCamelCase		:      float       ,			_UpperCamelCase		:      float       ,			_UpperCamelCase		:      int							)		->       float:

							'''simple docstring'''



							if principal <= 0:
														raise Exception("""Principal borrowed must be > 0"""							)
							if rate_per_annum < 0:
														raise Exception("""Rate of interest must be >= 0"""							)
							if years_to_repay <= 0 or not isinstance(_UpperCamelCase       ,			_UpperCamelCase							):
														raise Exception("""Years to repay must be an integer > 0"""							)

							# Yearly rate is divided by 12 to get monthly rate
							__UpperCAmelCase   :						List[Any]      							=					rate_per_annum / 1_2

							# Years to repay is multiplied by 12 to get number of payments as payment is monthly
							__UpperCAmelCase   :						Optional[int]      							=					years_to_repay * 1_2

							return (
							    principal
							    * rate_per_month
							    * (1 + rate_per_month) ** number_of_payments
							    / ((1 + rate_per_month) ** number_of_payments - 1)
							)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
