






'''simple docstring'''




import copy

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


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

__lowerCamelCase   :					Optional[Any]   							=					{
    '''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}







class    A_	(a_							):
							"""simple docstring"""




							a__   		=	'''deta'''
							a__   		=	{
							    '''hidden_size''': '''d_model''',
							    '''num_attention_heads''': '''encoder_attention_heads''',
							}

							def __init__(		self			:Union[str, Any]       ,							lowerCAmelCase__			:List[Any]=None       ,							lowerCAmelCase__			:Optional[int]=900       ,							lowerCAmelCase__			:Any=2_048       ,							lowerCAmelCase__			:Any=6       ,							lowerCAmelCase__			:Tuple=2_048       ,							lowerCAmelCase__			:Optional[int]=8       ,							lowerCAmelCase__			:List[str]=6       ,							lowerCAmelCase__			:Optional[int]=1_024       ,							lowerCAmelCase__			:Optional[int]=8       ,							lowerCAmelCase__			:int=0.0       ,							lowerCAmelCase__			:str=True       ,							lowerCAmelCase__			:Optional[Any]="relu"       ,							lowerCAmelCase__			:Optional[Any]=256       ,							lowerCAmelCase__			:Tuple=0.1       ,							lowerCAmelCase__			:Union[str, Any]=0.0       ,							lowerCAmelCase__			:List[Any]=0.0       ,							lowerCAmelCase__			:int=0.0_2       ,							lowerCAmelCase__			:str=1.0       ,							lowerCAmelCase__			:Dict=True       ,							lowerCAmelCase__			:Any=False       ,							lowerCAmelCase__			:Dict="sine"       ,							lowerCAmelCase__			:Optional[Any]=5       ,							lowerCAmelCase__			:int=4       ,							lowerCAmelCase__			:List[Any]=4       ,							lowerCAmelCase__			:Optional[int]=True       ,							lowerCAmelCase__			:Union[str, Any]=300       ,							lowerCAmelCase__			:List[Any]=True       ,							lowerCAmelCase__			:Optional[int]=True       ,							lowerCAmelCase__			:int=1       ,							lowerCAmelCase__			:List[Any]=5       ,							lowerCAmelCase__			:Dict=2       ,							lowerCAmelCase__			:int=1       ,							lowerCAmelCase__			:List[Any]=1       ,							lowerCAmelCase__			:Optional[Any]=5       ,							lowerCAmelCase__			:Any=2       ,							lowerCAmelCase__			:Optional[int]=0.1       ,							lowerCAmelCase__			:Any=0.2_5       ,							**lowerCAmelCase__			:str       ,							)				->  Any:


										'''simple docstring'''




										if backbone_config is None:
													logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."				)
													snake_case_				:					Optional[Any]   =       CONFIG_MAPPING['resnet'](out_features=["stage2", "stage3", "stage4"]				)
										else:
													if isinstance(lowerCAmelCase__       ,							lowerCAmelCase__				):
																snake_case_				:					Dict   =       backbone_config.pop("model_type"				)
																snake_case_				:					Union[str, Any]   =       CONFIG_MAPPING[backbone_model_type]
																snake_case_				:					str   =       config_class.from_dict(lowerCAmelCase__				)

										snake_case_				:					int   =       backbone_config
										snake_case_				:					Dict   =       num_queries
										snake_case_				:					List[Any]   =       max_position_embeddings
										snake_case_				:					Tuple   =       d_model
										snake_case_				:					int   =       encoder_ffn_dim
										snake_case_				:					Optional[Any]   =       encoder_layers
										snake_case_				:					str   =       encoder_attention_heads
										snake_case_				:					List[str]   =       decoder_ffn_dim
										snake_case_				:					Optional[int]   =       decoder_layers
										snake_case_				:					List[str]   =       decoder_attention_heads
										snake_case_				:					List[str]   =       dropout
										snake_case_				:					List[Any]   =       attention_dropout
										snake_case_				:					str   =       activation_dropout
										snake_case_				:					Union[str, Any]   =       activation_function
										snake_case_				:					List[str]   =       init_std
										snake_case_				:					Optional[Any]   =       init_xavier_std
										snake_case_				:					Optional[Any]   =       encoder_layerdrop
										snake_case_				:					Union[str, Any]   =       auxiliary_loss
										snake_case_				:					List[str]   =       position_embedding_type
										# deformable attributes
										snake_case_				:					Dict   =       num_feature_levels
										snake_case_				:					Optional[Any]   =       encoder_n_points
										snake_case_				:					Union[str, Any]   =       decoder_n_points
										snake_case_				:					str   =       two_stage
										snake_case_				:					int   =       two_stage_num_proposals
										snake_case_				:					int   =       with_box_refine
										snake_case_				:					Optional[int]   =       assign_first_stage
										if two_stage is True and with_box_refine is False:
													raise ValueError("If two_stage is True, with_box_refine must be True."				)
										# Hungarian matcher
										snake_case_				:					Dict   =       class_cost
										snake_case_				:					Tuple   =       bbox_cost
										snake_case_				:					Any   =       giou_cost
										# Loss coefficients
										snake_case_				:					List[Any]   =       mask_loss_coefficient
										snake_case_				:					Dict   =       dice_loss_coefficient
										snake_case_				:					Tuple   =       bbox_loss_coefficient
										snake_case_				:					str   =       giou_loss_coefficient
										snake_case_				:					str   =       eos_coefficient
										snake_case_				:					str   =       focal_alpha
										super().__init__(is_encoder_decoder=lowerCAmelCase__       ,							**lowerCAmelCase__				)

							@property
							def 							_A	(		self			:Optional[Any]				)				->  Union[str, Any]:


										'''simple docstring'''




										return self.encoder_attention_heads

							@property
							def 							_A	(		self			:Optional[Any]				)				->  List[str]:


										'''simple docstring'''




										return self.d_model






							def 							_A	(		self			:Any				)				->  Any:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       copy.deepcopy(self.__dict__				)
										snake_case_				:					Any   =       self.backbone_config.to_dict()
										snake_case_				:					Union[str, Any]   =       self.__class__.model_type
										return output












'''simple docstring'''




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu







@require_cpu
class    A_	(unittest.TestCase							):
							"""simple docstring"""




							def 							_A	(		self			:str				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       torch.nn.Linear(10       ,							10				)
										snake_case_				:					Dict   =       torch.optim.SGD(model.parameters()       ,							0.1				)
										snake_case_				:					Tuple   =       Accelerator()
										snake_case_				:					Optional[Any]   =       accelerator.prepare(lowerCAmelCase__				)
										try:
													pickle.loads(pickle.dumps(lowerCAmelCase__				)				)
										except Exception as e:
													self.fail(F'''Accelerated optimizer pickling failed with {e}'''				)
										AcceleratorState._reset_state()




