
"""simple docstring"""





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


__magic_name__       =			logging.get_logger(__name__)

__magic_name__       =			{
    "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 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Optional[Any]						   =		'''detr'''
		__lowercase							:  Any						   =		['''past_key_values''']
		__lowercase							:  Optional[int]						   =		{
		    '''hidden_size''': '''d_model''',
		    '''num_attention_heads''': '''encoder_attention_heads''',
		}




		def __init__(     self							,  lowerCAmelCase__=True							,  lowerCAmelCase__=None							,  lowerCAmelCase__=3							,  lowerCAmelCase__=1_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__=1							,  lowerCAmelCase__=5							,  lowerCAmelCase__=2							,  lowerCAmelCase__=1							,  lowerCAmelCase__=1							,  lowerCAmelCase__=5							,  lowerCAmelCase__=2							,  lowerCAmelCase__=0.1							,  **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__)
						# set timm attributes to None
						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = None, None, None

				__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            = bbox_loss_coefficient
				__SCREAMING_SNAKE_CASE            = giou_loss_coefficient
				__SCREAMING_SNAKE_CASE            = eos_coefficient
				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




		@classmethod
		def 	snake_case_     (     cls							,  lowerCAmelCase__							,  **lowerCAmelCase__):
				return cls(backbone_config=lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = copy.deepcopy(self.__dict__)
				if output["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






"""simple docstring"""





import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


__magic_name__       =			logging.get_logger(__name__)
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		def __init__(     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				warnings.warn(
				    """The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
				    """ use YolosImageProcessor instead."""							,  lowerCAmelCase__							,  )
				super().__init__(*lowerCAmelCase__							,  **lowerCAmelCase__)




