import copy

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


_lowerCAmelCase       =		logging.get_logger(__name__)

_lowerCAmelCase       =		{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     "deta"
							UpperCAmelCase				   =     {
							    "hidden_size": "d_model",
							    "num_attention_heads": "encoder_attention_heads",
							}







							def __init__(   self      :     Optional[int] ,			_A      :     Tuple=None ,			_A      :     Dict=900 ,			_A      :     Union[str, Any]=2048 ,			_A      :     Union[str, Any]=6 ,			_A      :     List[str]=2048 ,			_A      :     str=8 ,			_A      :     Optional[int]=6 ,			_A      :     List[str]=1024 ,			_A      :     Optional[int]=8 ,			_A      :     List[str]=0.0 ,			_A      :     List[str]=True ,			_A      :     Any="relu" ,			_A      :     Any=256 ,			_A      :     Optional[int]=0.1 ,			_A      :     str=0.0 ,			_A      :     Dict=0.0 ,			_A      :     str=0.02 ,			_A      :     Union[str, Any]=1.0 ,			_A      :     Union[str, Any]=True ,			_A      :     Any=False ,			_A      :     Union[str, Any]="sine" ,			_A      :     int=5 ,			_A      :     Optional[Any]=4 ,			_A      :     Any=4 ,			_A      :     Union[str, Any]=True ,			_A      :     Dict=300 ,			_A      :     List[Any]=True ,			_A      :     Any=True ,			_A      :     Tuple=1 ,			_A      :     Optional[int]=5 ,			_A      :     str=2 ,			_A      :     Tuple=1 ,			_A      :     Tuple=1 ,			_A      :     Any=5 ,			_A      :     Tuple=2 ,			_A      :     str=0.1 ,			_A      :     List[str]=0.25 ,			**_A      :     Dict ,			):
												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=['''stage2''', '''stage3''', '''stage4''']		)
												else:
																	if isinstance(_A ,			_A		):
																						_UpperCamelCase			     =     backbone_config.pop('''model_type'''		)
																						_UpperCamelCase			     =     CONFIG_MAPPING[backbone_model_type]
																						_UpperCamelCase			     =     config_class.from_dict(_A		)

												_UpperCamelCase			     =     backbone_config
												_UpperCamelCase			     =     num_queries
												_UpperCamelCase			     =     max_position_embeddings
												_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			     =     auxiliary_loss
												_UpperCamelCase			     =     position_embedding_type
												# deformable attributes
												_UpperCamelCase			     =     num_feature_levels
												_UpperCamelCase			     =     encoder_n_points
												_UpperCamelCase			     =     decoder_n_points
												_UpperCamelCase			     =     two_stage
												_UpperCamelCase			     =     two_stage_num_proposals
												_UpperCamelCase			     =     with_box_refine
												_UpperCamelCase			     =     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
												_UpperCamelCase			     =     class_cost
												_UpperCamelCase			     =     bbox_cost
												_UpperCamelCase			     =     giou_cost
												# Loss coefficients
												_UpperCamelCase			     =     mask_loss_coefficient
												_UpperCamelCase			     =     dice_loss_coefficient
												_UpperCamelCase			     =     bbox_loss_coefficient
												_UpperCamelCase			     =     giou_loss_coefficient
												_UpperCamelCase			     =     eos_coefficient
												_UpperCamelCase			     =     focal_alpha
												super().__init__(is_encoder_decoder=_A ,			**_A		)







							@property
							def  UpperCamelCase_						(   self      :     Optional[int]		):
												return self.encoder_attention_heads







							@property
							def  UpperCamelCase_						(   self      :     int		):
												return self.d_model







							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     copy.deepcopy(self.__dict__		)
												_UpperCamelCase			     =     self.backbone_config.to_dict()
												_UpperCamelCase			     =     self.__class__.model_type
												return output




import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							str	   =       (CMStochasticIterativeScheduler,)
     __lowercase				:							List[str]	   =       10

     def 	SCREAMING_SNAKE_CASE__						(					self:int     ,		**_a:Optional[int]   ):
       snake_case__						= {
           '''num_train_timesteps''': 2_01,
           '''sigma_min''': 0.002,
           '''sigma_max''': 80.0,
       }

       config.update(**_a   )
       return config

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       snake_case__						= 10

       snake_case__						= self.get_scheduler_config()
       snake_case__						= self.scheduler_classes[0](**_a   )

       scheduler.set_timesteps(_a   )

       snake_case__						= scheduler.timesteps[0]
       snake_case__						= scheduler.timesteps[1]

       snake_case__						= self.dummy_sample
       snake_case__						= 0.1 * sample

       snake_case__						= scheduler.step(_a     ,		_a     ,		_a   ).prev_sample
       snake_case__						= scheduler.step(_a     ,		_a     ,		_a   ).prev_sample

       self.assertEqual(output_a.shape     ,		sample.shape   )
       self.assertEqual(output_a.shape     ,		output_a.shape   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       for timesteps in [10, 50, 1_00, 10_00]:
         self.check_over_configs(num_train_timesteps=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       for clip_denoised in [True, False]:
         self.check_over_configs(clip_denoised=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= 1
       scheduler.set_timesteps(_a   )
       snake_case__						= scheduler.timesteps

       snake_case__						= torch.manual_seed(0   )

       snake_case__						= self.dummy_model()
       snake_case__						= self.dummy_sample_deter * scheduler.init_noise_sigma

       for i, t in enumerate(_a   ):
         # 1. scale model input
         snake_case__						= scheduler.scale_model_input(_a     ,		_a   )

         # 2. predict noise residual
         snake_case__						= model(_a     ,		_a   )

         # 3. predict previous sample x_t-1
         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		generator=_a   ).prev_sample

         snake_case__						= pred_prev_sample

       snake_case__						= torch.sum(torch.abs(_a   )   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_sum.item() - 192.7614   ) < 1e-2
       assert abs(result_mean.item() - 0.2510   ) < 1e-3

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [1_06, 0]
       scheduler.set_timesteps(timesteps=_a   )
       snake_case__						= scheduler.timesteps

       snake_case__						= torch.manual_seed(0   )

       snake_case__						= self.dummy_model()
       snake_case__						= self.dummy_sample_deter * scheduler.init_noise_sigma

       for t in timesteps:
         # 1. scale model input
         snake_case__						= scheduler.scale_model_input(_a     ,		_a   )

         # 2. predict noise residual
         snake_case__						= model(_a     ,		_a   )

         # 3. predict previous sample x_t-1
         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		generator=_a   ).prev_sample

         snake_case__						= pred_prev_sample

       snake_case__						= torch.sum(torch.abs(_a   )   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_sum.item() - 347.6357   ) < 1e-2
       assert abs(result_mean.item() - 0.4527   ) < 1e-3

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [39, 30, 12, 15, 0]

       with self.assertRaises(_a     ,		msg='''`timesteps` must be in descending order.'''   ):
         scheduler.set_timesteps(timesteps=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [39, 30, 12, 1, 0]
       snake_case__						= len(_a   )

       with self.assertRaises(_a     ,		msg='''Can only pass one of `num_inference_steps` or `timesteps`.'''   ):
         scheduler.set_timesteps(num_inference_steps=_a     ,		timesteps=_a   )







     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [scheduler.config.num_train_timesteps]

       with self.assertRaises(
           _a     ,		msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''     ,		):
         scheduler.set_timesteps(timesteps=_a   )

