




'''simple docstring'''







# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin







class 					_a (a__,		a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Union[str, Any]          =					1







     @register_to_config
     def __init__(				self							,__a=2_000							,__a=0.1							,__a=20							,__a=1E-3							)      ->				Union[str, Any]:
          snake_case			:      Any        =			None
          snake_case			:      Optional[Any]        =			None
          snake_case			:      str        =			None







     def 			snake_case_      (				self							,__a							,__a = None							)      ->				Optional[int]:
          snake_case			:      Union[str, Any]        =			torch.linspace(1							,self.config.sampling_eps							,__a							,device=__a							)







     def 			snake_case_      (				self							,__a							,__a							,__a							,__a=None							)      ->				str:
          if self.timesteps is None:
               raise ValueError(
                   """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""							)

          # TODO(Patrick) better comments + non-PyTorch
          # postprocess model score
          snake_case			:      str        =			(
              -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
          )
          snake_case			:      str        =			torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
          snake_case			:      int        =			std.flatten()
          while len(std.shape							) < len(score.shape							):
               snake_case			:      Tuple        =			std.unsqueeze(-1							)
          snake_case			:      List[str]        =			-score / std

          # compute
          snake_case			:      Tuple        =			-1.0 / len(self.timesteps							)

          snake_case			:      List[Any]        =			self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
          snake_case			:      int        =			beta_t.flatten()
          while len(beta_t.shape							) < len(x.shape							):
               snake_case			:      str        =			beta_t.unsqueeze(-1							)
          snake_case			:      str        =			-0.5 * beta_t * x

          snake_case			:      List[Any]        =			torch.sqrt(__a							)
          snake_case			:      str        =			drift - diffusion**2 * score
          snake_case			:      str        =			x + drift * dt

          # add noise
          snake_case			:      Optional[Any]        =			randn_tensor(x.shape							,layout=x.layout							,generator=__a							,device=x.device							,dtype=x.dtype							)
          snake_case			:      Optional[Any]        =			x_mean + diffusion * math.sqrt(-dt							) * noise

          return x, x_mean







     def __len__(				self							)      ->				Tuple:
          return self.config.num_train_timesteps









'''simple docstring'''







import copy

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


lowercase      :   Tuple      =       logging.get_logger(__name__)

lowercase      :   Dict      =       {
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     str          =					"""deta"""
     lowerCAmelCase_  :     Any          =					{
         """hidden_size""": """d_model""",
         """num_attention_heads""": """encoder_attention_heads""",
     }







     def __init__(				self							,__a=None							,__a=900							,__a=2_048							,__a=6							,__a=2_048							,__a=8							,__a=6							,__a=1_024							,__a=8							,__a=0.0							,__a=True							,__a="relu"							,__a=256							,__a=0.1							,__a=0.0							,__a=0.0							,__a=0.02							,__a=1.0							,__a=True							,__a=False							,__a="sine"							,__a=5							,__a=4							,__a=4							,__a=True							,__a=300							,__a=True							,__a=True							,__a=1							,__a=5							,__a=2							,__a=1							,__a=1							,__a=5							,__a=2							,__a=0.1							,__a=0.25							,**__a							,)      ->				int:
          if backbone_config is None:
               logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""							)
               snake_case			:      int        =			CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]							)
          else:
               if isinstance(__a							,__a							):
                    snake_case			:      str        =			backbone_config.pop("""model_type"""							)
                    snake_case			:      Optional[Any]        =			CONFIG_MAPPING[backbone_model_type]
                    snake_case			:      List[Any]        =			config_class.from_dict(__a							)

          snake_case			:      Any        =			backbone_config
          snake_case			:      Optional[Any]        =			num_queries
          snake_case			:      Dict        =			max_position_embeddings
          snake_case			:      int        =			d_model
          snake_case			:      str        =			encoder_ffn_dim
          snake_case			:      Dict        =			encoder_layers
          snake_case			:      List[Any]        =			encoder_attention_heads
          snake_case			:      int        =			decoder_ffn_dim
          snake_case			:      List[Any]        =			decoder_layers
          snake_case			:      Dict        =			decoder_attention_heads
          snake_case			:      Dict        =			dropout
          snake_case			:      Dict        =			attention_dropout
          snake_case			:      List[Any]        =			activation_dropout
          snake_case			:      Dict        =			activation_function
          snake_case			:      List[Any]        =			init_std
          snake_case			:      Any        =			init_xavier_std
          snake_case			:      List[Any]        =			encoder_layerdrop
          snake_case			:      Optional[Any]        =			auxiliary_loss
          snake_case			:      Optional[int]        =			position_embedding_type
          # deformable attributes
          snake_case			:      Tuple        =			num_feature_levels
          snake_case			:      List[Any]        =			encoder_n_points
          snake_case			:      Dict        =			decoder_n_points
          snake_case			:      int        =			two_stage
          snake_case			:      Any        =			two_stage_num_proposals
          snake_case			:      List[str]        =			with_box_refine
          snake_case			:      Tuple        =			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			:      Optional[int]        =			class_cost
          snake_case			:      int        =			bbox_cost
          snake_case			:      Optional[int]        =			giou_cost
          # Loss coefficients
          snake_case			:      str        =			mask_loss_coefficient
          snake_case			:      Any        =			dice_loss_coefficient
          snake_case			:      List[Any]        =			bbox_loss_coefficient
          snake_case			:      List[Any]        =			giou_loss_coefficient
          snake_case			:      List[str]        =			eos_coefficient
          snake_case			:      Optional[Any]        =			focal_alpha
          super().__init__(is_encoder_decoder=__a							,**__a							)







     @property
     def 			snake_case_      (				self							)      ->				int:
          return self.encoder_attention_heads







     @property
     def 			snake_case_      (				self							)      ->				int:
          return self.d_model







     def 			snake_case_      (				self							)      ->				List[str]:
          snake_case			:      List[str]        =			copy.deepcopy(self.__dict__							)
          snake_case			:      List[str]        =			self.backbone_config.to_dict()
          snake_case			:      List[Any]        =			self.__class__.model_type
          return output



