
"""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


__magic_name__       =			logging.get_logger(__name__)

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




		"""simple docstring"""





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




		def __init__(     self							,  lowerCAmelCase__=True							,  lowerCAmelCase__=None							,  lowerCAmelCase__=3							,  lowerCAmelCase__=3_0_0							,  lowerCAmelCase__=6							,  lowerCAmelCase__=2_0_4_8							,  lowerCAmelCase__=8							,  lowerCAmelCase__=6							,  lowerCAmelCase__=2_0_4_8							,  lowerCAmelCase__=8							,  lowerCAmelCase__=0.0							,  lowerCAmelCase__=0.0							,  lowerCAmelCase__=True							,  lowerCAmelCase__="relu"							,  lowerCAmelCase__=2_5_6							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=0.0							,  lowerCAmelCase__=0.0							,  lowerCAmelCase__=0.02							,  lowerCAmelCase__=1.0							,  lowerCAmelCase__=False							,  lowerCAmelCase__="sine"							,  lowerCAmelCase__="resnet50"							,  lowerCAmelCase__=True							,  lowerCAmelCase__=False							,  lowerCAmelCase__=2							,  lowerCAmelCase__=5							,  lowerCAmelCase__=2							,  lowerCAmelCase__=1							,  lowerCAmelCase__=1							,  lowerCAmelCase__=2							,  lowerCAmelCase__=5							,  lowerCAmelCase__=2							,  lowerCAmelCase__=0.25							,  **lowerCAmelCase__							,  ):
				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.""")
								__SCREAMING_SNAKE_CASE            = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""])
						elif isinstance(lowerCAmelCase__							,  lowerCAmelCase__):
								__SCREAMING_SNAKE_CASE            = backbone_config.get("""model_type""")
								__SCREAMING_SNAKE_CASE            = CONFIG_MAPPING[backbone_model_type]
								__SCREAMING_SNAKE_CASE            = config_class.from_dict(lowerCAmelCase__)

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




		@property
		def 	snake_case_     (     self):
				return self.encoder_attention_heads




		@property
		def 	snake_case_     (     self):
				return self.d_model




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



class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  List[Any]						   =		version.parse('''1.11''' )




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




		@property
		def 	snake_case_     (     self):
				return 1E-5




		@property
		def 	snake_case_     (     self):
				return 1_2





def    lowerCAmelCase_ (     __A,  __A							)     ->				float:







      '''simple docstring'''


      def get_matched_characters(__A,  __A							) -> str:
            UpperCAmelCase__								=  []
            UpperCAmelCase__								=  min(len(_stra							),  len(_stra							)							) // 2
            for i, l in enumerate(_stra							):
                  UpperCAmelCase__								=  int(max(0,  i - limit							)							)
                  UpperCAmelCase__								=  int(min(i + limit + 1,  len(_stra							)							)							)
                  if l in _stra[left:right]:
                        matched.append(__A							)
                        UpperCAmelCase__								=  f"""{_stra[0:_stra.index(__A							)]} {_stra[_stra.index(__A							) + 1:]}"""

            return "".join(__A							)

      # matching characters
      UpperCAmelCase__								=  get_matched_characters(__A,  __A							)
      UpperCAmelCase__								=  get_matched_characters(__A,  __A							)
      UpperCAmelCase__								=  len(__A							)

      # transposition
      UpperCAmelCase__								=  (
          len([(ca, ca) for ca, ca in zip(__A,  __A							) if ca != ca]							) // 2
      )

      if not match_count:
            UpperCAmelCase__								=  0.0
      else:
            UpperCAmelCase__								=  (
                1
                / 3
                * (
                    match_count / len(__A							)
                    + match_count / len(__A							)
                    + (match_count - transpositions) / match_count
                )
            )

      # common prefix up to 4 characters
      UpperCAmelCase__								=  0
      for ca, ca in zip(stra[:4],  stra[:4]							):
            if ca == ca:
                  prefix_len += 1
            else:
                  break

      return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(jaro_winkler('hello', 'world'))



