





'''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


__UpperCamelCase					    =	logging.get_logger(__name__)

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




class  _A (					__lowercase		):
		lowercase__:   List[str]				     =   '''conditional_detr'''
		lowercase__:   Optional[int]				     =   ['''past_key_values''']
		lowercase__:   Tuple				     =   {
		    '''hidden_size''': '''d_model''',
		    '''num_attention_heads''': '''encoder_attention_heads''',
		}

		def __init__(   self					:   Tuple  ,      __magic_name__					:   Optional[Any]=True  ,      __magic_name__					:   int=None  ,      __magic_name__					:   str=3  ,      __magic_name__					:   Tuple=3_00  ,      __magic_name__					:   str=6  ,      __magic_name__					:   Any=20_48  ,      __magic_name__					:   Optional[int]=8  ,      __magic_name__					:   Dict=6  ,      __magic_name__					:   Optional[int]=20_48  ,      __magic_name__					:   List[str]=8  ,      __magic_name__					:   Union[str, Any]=0.0  ,      __magic_name__					:   Any=0.0  ,      __magic_name__					:   int=True  ,      __magic_name__					:   List[str]="relu"  ,      __magic_name__					:   Tuple=2_56  ,      __magic_name__					:   Any=0.1  ,      __magic_name__					:   Union[str, Any]=0.0  ,      __magic_name__					:   int=0.0  ,      __magic_name__					:   str=0.02  ,      __magic_name__					:   List[str]=1.0  ,      __magic_name__					:   Union[str, Any]=False  ,      __magic_name__					:   Dict="sine"  ,      __magic_name__					:   Union[str, Any]="resnet50"  ,      __magic_name__					:   Optional[int]=True  ,      __magic_name__					:   Any=False  ,      __magic_name__					:   Dict=2  ,      __magic_name__					:   Any=5  ,      __magic_name__					:   Dict=2  ,      __magic_name__					:   int=1  ,      __magic_name__					:   Optional[Any]=1  ,      __magic_name__					:   Union[str, Any]=2  ,      __magic_name__					:   Optional[int]=5  ,      __magic_name__					:   Dict=2  ,      __magic_name__					:   Any=0.25  ,      **__magic_name__					:   Dict  ,      )					->    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."""			)
											__snake_case					:      Any							      =     CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]			)
								elif isinstance(__magic_name__  ,      __magic_name__			):
											__snake_case					:      Dict							      =     backbone_config.get("""model_type"""			)
											__snake_case					:      Dict							      =     CONFIG_MAPPING[backbone_model_type]
											__snake_case					:      List[Any]							      =     config_class.from_dict(__magic_name__			)

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

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


					"""simple docstring"""

					return self.encoder_attention_heads

		@property
		def 	lowercase__     (   self					:   Any			)					->    int:


					"""simple docstring"""

					return self.d_model

		def 	lowercase__     (   self					:   Optional[Any]			)					->    List[Any]:


					"""simple docstring"""

					__snake_case					:      Optional[int]							      =     copy.deepcopy(self.__dict__			)
					if self.backbone_config is not None:
								__snake_case					:      Union[str, Any]							      =     self.backbone_config.to_dict()
					__snake_case					:      Dict							      =     self.__class__.model_type
					return output





class  _A (					__lowercase		):
		lowercase__:   Dict				     =   version.parse('''1.11'''		)

		@property
		def 	lowercase__     (   self					:   int			)					->    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 	lowercase__     (   self					:   List[Any]			)					->    float:


					"""simple docstring"""

					return 1E-5

		@property
		def 	lowercase__     (   self					:   str			)					->    int:


					"""simple docstring"""

					return 12









'''simple docstring'''






import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


__UpperCamelCase					    =	HUGGINGFACE_HUB_CACHE


__UpperCamelCase					    =	"config.json"
__UpperCamelCase					    =	"diffusion_pytorch_model.bin"
__UpperCamelCase					    =	"diffusion_flax_model.msgpack"
__UpperCamelCase					    =	"model.onnx"
__UpperCamelCase					    =	"diffusion_pytorch_model.safetensors"
__UpperCamelCase					    =	"weights.pb"
__UpperCamelCase					    =	"https://huggingface.co"
__UpperCamelCase					    =	default_cache_path
__UpperCamelCase					    =	"diffusers_modules"
__UpperCamelCase					    =	os.getenv("HF_MODULES_CACHE", os.path.join(hf_cache_home, "modules"))
__UpperCamelCase					    =	["fp16", "non-ema"]
__UpperCamelCase					    =	".self_attn"


