






import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a_				=     logging.get_logger(__name__)

a_				=     {
    'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
    # See all DETR models at https://huggingface.co/models?filter=detr
}


class   lowercase__  (      __SCREAMING_SNAKE_CASE						):
	a_       ="""detr"""
	a_       =["""past_key_values"""]
	a_       ={
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	}

	def __init__(				self		,     __UpperCAmelCase=True		,     __UpperCAmelCase=None		,     __UpperCAmelCase=3		,     __UpperCAmelCase=100		,     __UpperCAmelCase=6		,     __UpperCAmelCase=2048		,     __UpperCAmelCase=8		,     __UpperCAmelCase=6		,     __UpperCAmelCase=2048		,     __UpperCAmelCase=8		,     __UpperCAmelCase=0.0		,     __UpperCAmelCase=0.0		,     __UpperCAmelCase=True		,     __UpperCAmelCase="relu"		,     __UpperCAmelCase=256		,     __UpperCAmelCase=0.1		,     __UpperCAmelCase=0.0		,     __UpperCAmelCase=0.0		,     __UpperCAmelCase=0.02		,     __UpperCAmelCase=1.0		,     __UpperCAmelCase=False		,     __UpperCAmelCase="sine"		,     __UpperCAmelCase="resnet50"		,     __UpperCAmelCase=True		,     __UpperCAmelCase=False		,     __UpperCAmelCase=1		,     __UpperCAmelCase=5		,     __UpperCAmelCase=2		,     __UpperCAmelCase=1		,     __UpperCAmelCase=1		,     __UpperCAmelCase=5		,     __UpperCAmelCase=2		,     __UpperCAmelCase=0.1		,     **__UpperCAmelCase		,     )->       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."							)
																			lowerCAmelCase__			   =    CONFIG_MAPPING['resnet'](out_features=["stage4"]							)
													elif isinstance(lowerCAmelCase__		,     lowerCAmelCase__							):
																			lowerCAmelCase__			   =    backbone_config.get("model_type"							)
																			lowerCAmelCase__			   =    CONFIG_MAPPING[backbone_model_type]
																			lowerCAmelCase__			   =    config_class.from_dict(lowerCAmelCase__							)
													# set timm attributes to None
													lowerCAmelCase__			   =    None, None, None

							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__			   =    bbox_loss_coefficient
							lowerCAmelCase__			   =    giou_loss_coefficient
							lowerCAmelCase__			   =    eos_coefficient
							super().__init__(is_encoder_decoder=lowerCAmelCase__		,     **lowerCAmelCase__							)

	@property
	def 		UpperCAmelCase	(				self							)->       List[Any]:







							'''simple docstring'''





							return self.encoder_attention_heads

	@property
	def 		UpperCAmelCase	(				self							)->       Dict:







							'''simple docstring'''





							return self.d_model

	@classmethod
	def 		UpperCAmelCase	(				cls		,     __UpperCAmelCase		,     **__UpperCAmelCase							)->       Optional[int]:







							'''simple docstring'''





							return cls(backbone_config=lowerCAmelCase__		,     **lowerCAmelCase__							)


	def 		UpperCAmelCase	(				self							)->       int:







							'''simple docstring'''





							lowerCAmelCase__			   =    copy.deepcopy(self.__dict__							)
							if output["backbone_config"] is not None:
													lowerCAmelCase__			   =    self.backbone_config.to_dict()
							lowerCAmelCase__			   =    self.__class__.model_type
							return output





class   lowercase__  (      __SCREAMING_SNAKE_CASE						):
	a_       =version.parse("""1.11"""						)

	@property
	def 		UpperCAmelCase	(				self							)->       Any:







							'''simple docstring'''





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

	@property
	def 		UpperCAmelCase	(				self							)->       Optional[int]:







							'''simple docstring'''





							return 1E-5


	@property
	def 		UpperCAmelCase	(				self							)->       int:







							'''simple docstring'''





							return 12










def 						a__			(				A__,      A__				):

			def get_matched_characters(A__,      A__				) -> str:
						SCREAMING_SNAKE_CASE_		:     Dict					=					[]
						SCREAMING_SNAKE_CASE_		:     Any					=					min(len(_stra				),      len(_stra				)				) // 2
						for i, l in enumerate(_stra				):
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					int(max(0,      i - limit				)				)
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					int(min(i + limit + 1,      len(_stra				)				)				)
									if l in _stra[left:right]:
												matched.append(A__				)
												SCREAMING_SNAKE_CASE_		:     List[str]					=					F'''{_stra[0:_stra.index(A__				)]} {_stra[_stra.index(A__				) + 1:]}'''

						return "".join(A__				)

			# matching characters
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					get_matched_characters(A__,      A__				)
			SCREAMING_SNAKE_CASE_		:     int					=					get_matched_characters(A__,      A__				)
			SCREAMING_SNAKE_CASE_		:     Any					=					len(A__				)

			# transposition
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					(
			    len([(ca, ca) for ca, ca in zip(A__,      A__				) if ca != ca]				) // 2
			)

			if not match_count:
						SCREAMING_SNAKE_CASE_		:     Dict					=					0.0
			else:
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					(
						    1
						    / 3
						    * (
						        match_count / len(A__				)
						        + match_count / len(A__				)
						        + (match_count - transpositions) / match_count
						    )
						)

			# common prefix up to 4 characters
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					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'))





