



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_:Optional[Any]					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Union[str, Any]					=						{
    """microsoft/conditional-detr-resnet-50""": (
        """https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
    ),
}






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Dict =				"conditional_detr"
    __lowerCamelCase      :			str =				["past_key_values"]
    __lowerCamelCase      :			str =				{
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }






    def __init__(	self,		lowerCamelCase__=True,		lowerCamelCase__=None,		lowerCamelCase__=3,		lowerCamelCase__=300,		lowerCamelCase__=6,		lowerCamelCase__=2048,		lowerCamelCase__=8,		lowerCamelCase__=6,		lowerCamelCase__=2048,		lowerCamelCase__=8,		lowerCamelCase__=0.0,		lowerCamelCase__=0.0,		lowerCamelCase__=True,		lowerCamelCase__="relu",		lowerCamelCase__=256,		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."""		)
                A			:	List[Any]										=       CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]		)
            elif isinstance(lowerCamelCase__,		lowerCamelCase__		):
                A			:	Any										=       backbone_config.get("""model_type"""		)
                A			:	Optional[Any]										=       CONFIG_MAPPING[backbone_model_type]
                A			:	Tuple										=       config_class.from_dict(lowerCamelCase__		)

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






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






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






    def       _lowerCAmelCase (	self		):
        A			:	Dict										=       copy.deepcopy(self.__dict__		)
        if self.backbone_config is not None:
            A			:	List[Any]										=       self.backbone_config.to_dict()
        A			:	List[str]										=       self.__class__.model_type
        return output






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Tuple =				version.parse("1.11"   )






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






    @property
    def       _lowerCAmelCase (	self		):
        return 1e-5






    @property
    def       _lowerCAmelCase (	self		):
        return 12







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_:Optional[Any]					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Union[str, Any]					=						{
    """microsoft/conditional-detr-resnet-50""": (
        """https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
    ),
}






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Dict =				"conditional_detr"
    __lowerCamelCase      :			str =				["past_key_values"]
    __lowerCamelCase      :			str =				{
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }






    def __init__(	self,		lowerCamelCase__=True,		lowerCamelCase__=None,		lowerCamelCase__=3,		lowerCamelCase__=300,		lowerCamelCase__=6,		lowerCamelCase__=2048,		lowerCamelCase__=8,		lowerCamelCase__=6,		lowerCamelCase__=2048,		lowerCamelCase__=8,		lowerCamelCase__=0.0,		lowerCamelCase__=0.0,		lowerCamelCase__=True,		lowerCamelCase__="relu",		lowerCamelCase__=256,		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."""		)
                A			:	List[Any]										=       CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]		)
            elif isinstance(lowerCamelCase__,		lowerCamelCase__		):
                A			:	Any										=       backbone_config.get("""model_type"""		)
                A			:	Optional[Any]										=       CONFIG_MAPPING[backbone_model_type]
                A			:	Tuple										=       config_class.from_dict(lowerCamelCase__		)

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






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






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






    def       _lowerCAmelCase (	self		):
        A			:	Dict										=       copy.deepcopy(self.__dict__		)
        if self.backbone_config is not None:
            A			:	List[Any]										=       self.backbone_config.to_dict()
        A			:	List[str]										=       self.__class__.model_type
        return output






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Tuple =				version.parse("1.11"   )






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






    @property
    def       _lowerCAmelCase (	self		):
        return 1e-5






    @property
    def       _lowerCAmelCase (	self		):
        return 12


