
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


lowerCamelCase__	=							logging.get_logger(__name__)

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







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Optional[int]	=		"conditional_detr"
						UpperCamelCase_							: Optional[int]	=		["past_key_values"]
						UpperCamelCase_							: Optional[Any]	=		{
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						}

						def __init__(			self       ,							a=True       ,							a=None       ,							a=3       ,							a=3_00       ,							a=6       ,							a=20_48       ,							a=8       ,							a=6       ,							a=20_48       ,							a=8       ,							a=0.0       ,							a=0.0       ,							a=True       ,							a="relu"       ,							a=2_56       ,							a=0.1       ,							a=0.0       ,							a=0.0       ,							a=0.02       ,							a=1.0       ,							a=False       ,							a="sine"       ,							a="resnet50"       ,							a=True       ,							a=False       ,							a=2       ,							a=5       ,							a=2       ,							a=1       ,							a=1       ,							a=2       ,							a=5       ,							a=2       ,							a=0.25       ,							**a       ,							)					->      int:


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

								_UpperCamelCase						    =	use_timm_backbone
								_UpperCamelCase						    =	backbone_config
								_UpperCamelCase						    =	num_channels
								_UpperCamelCase						    =	num_queries
								_UpperCamelCase						    =	d_model
								_UpperCamelCase						    =	encoder_ffn_dim
								_UpperCamelCase						    =	encoder_layers
								_UpperCamelCase						    =	encoder_attention_heads
								_UpperCamelCase						    =	decoder_ffn_dim
								_UpperCamelCase						    =	decoder_layers
								_UpperCamelCase						    =	decoder_attention_heads
								_UpperCamelCase						    =	dropout
								_UpperCamelCase						    =	attention_dropout
								_UpperCamelCase						    =	activation_dropout
								_UpperCamelCase						    =	activation_function
								_UpperCamelCase						    =	init_std
								_UpperCamelCase						    =	init_xavier_std
								_UpperCamelCase						    =	encoder_layerdrop
								_UpperCamelCase						    =	decoder_layerdrop
								_UpperCamelCase						    =	encoder_layers
								_UpperCamelCase						    =	auxiliary_loss
								_UpperCamelCase						    =	position_embedding_type
								_UpperCamelCase						    =	backbone
								_UpperCamelCase						    =	use_pretrained_backbone
								_UpperCamelCase						    =	dilation
								# Hungarian matcher
								_UpperCamelCase						    =	class_cost
								_UpperCamelCase						    =	bbox_cost
								_UpperCamelCase						    =	giou_cost
								# Loss coefficients
								_UpperCamelCase						    =	mask_loss_coefficient
								_UpperCamelCase						    =	dice_loss_coefficient
								_UpperCamelCase						    =	cls_loss_coefficient
								_UpperCamelCase						    =	bbox_loss_coefficient
								_UpperCamelCase						    =	giou_loss_coefficient
								_UpperCamelCase						    =	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'''

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







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: str	=		version.parse("1.11"	)

						@property
						def  A_				(			self				)					->      Mapping[str, Mapping[int, str]]:


								'''simple docstring'''

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

						@property
						def  A_				(			self				)					->      float:


								'''simple docstring'''

								return 1e-5







						@property
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								return 12





lowerCamelCase__	=							[
    "DownloadConfig",
    "DownloadManager",
    "DownloadMode",
    "StreamingDownloadManager",
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager



