
import copy

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


__lowerCamelCase				=			logging.get_logger(__name__)

__lowerCamelCase				=			{
    """SenseTime/deformable-detr""": """https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json""",
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}






class     UpperCAmelCase						(		A_					):
   A__   :						Dict			=	"deformable_detr"
   A__   :						Dict			=	{
       "hidden_size": "d_model",
       "num_attention_heads": "encoder_attention_heads",
   }



   def __init__(self						:   Any       ,		snake_case__						:   List[str]=True       ,		snake_case__						:   int=None       ,		snake_case__						:   Any=3       ,		snake_case__						:   Dict=3_00       ,		snake_case__						:   Optional[Any]=10_24       ,		snake_case__						:   Optional[Any]=6       ,		snake_case__						:   Union[str, Any]=10_24       ,		snake_case__						:   Optional[int]=8       ,		snake_case__						:   Tuple=6       ,		snake_case__						:   Dict=10_24       ,		snake_case__						:   Optional[Any]=8       ,		snake_case__						:   Any=0.0       ,		snake_case__						:   Tuple=True       ,		snake_case__						:   Any="relu"       ,		snake_case__						:   Tuple=2_56       ,		snake_case__						:   Union[str, Any]=0.1       ,		snake_case__						:   int=0.0       ,		snake_case__						:   Any=0.0       ,		snake_case__						:   Optional[int]=0.02       ,		snake_case__						:   Optional[Any]=1.0       ,		snake_case__						:   Any=True       ,		snake_case__						:   List[str]=False       ,		snake_case__						:   List[str]="sine"       ,		snake_case__						:   Optional[Any]="resnet50"       ,		snake_case__						:   int=True       ,		snake_case__						:   Union[str, Any]=False       ,		snake_case__						:   int=4       ,		snake_case__						:   Any=4       ,		snake_case__						:   Any=4       ,		snake_case__						:   int=False       ,		snake_case__						:   str=3_00       ,		snake_case__						:   Dict=False       ,		snake_case__						:   Optional[Any]=1       ,		snake_case__						:   Tuple=5       ,		snake_case__						:   List[Any]=2       ,		snake_case__						:   Optional[Any]=1       ,		snake_case__						:   Dict=1       ,		snake_case__						:   int=5       ,		snake_case__						:   Optional[Any]=2       ,		snake_case__						:   List[str]=0.1       ,		snake_case__						:   Tuple=0.25       ,		snake_case__						:   List[str]=False       ,		**snake_case__						:   Dict       ,		)						->      str:


          '''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			:  Tuple     							= CONFIG_MAPPING["resnet"](out_features=["stage4"]					)
                 elif isinstance(snake_case__       ,		snake_case__					):
                        snake_case			:  Union[str, Any]     							= backbone_config.get("model_type"					)
                        snake_case			:  Optional[Any]     							= CONFIG_MAPPING[backbone_model_type]
                        snake_case			:  Optional[int]     							= config_class.from_dict(snake_case__					)
          snake_case			:  Union[str, Any]     							= use_timm_backbone
          snake_case			:  Optional[Any]     							= backbone_config
          snake_case			:  Optional[Any]     							= num_channels
          snake_case			:  Optional[Any]     							= num_queries
          snake_case			:  Any     							= max_position_embeddings
          snake_case			:  Dict     							= d_model
          snake_case			:  int     							= encoder_ffn_dim
          snake_case			:  List[Any]     							= encoder_layers
          snake_case			:  Optional[Any]     							= encoder_attention_heads
          snake_case			:  Tuple     							= decoder_ffn_dim
          snake_case			:  str     							= decoder_layers
          snake_case			:  Dict     							= decoder_attention_heads
          snake_case			:  Union[str, Any]     							= dropout
          snake_case			:  Any     							= attention_dropout
          snake_case			:  Optional[Any]     							= activation_dropout
          snake_case			:  Any     							= activation_function
          snake_case			:  List[Any]     							= init_std
          snake_case			:  Dict     							= init_xavier_std
          snake_case			:  int     							= encoder_layerdrop
          snake_case			:  Optional[int]     							= auxiliary_loss
          snake_case			:  Optional[Any]     							= position_embedding_type
          snake_case			:  int     							= backbone
          snake_case			:  Optional[Any]     							= use_pretrained_backbone
          snake_case			:  List[str]     							= dilation
          # deformable attributes
          snake_case			:  Any     							= num_feature_levels
          snake_case			:  int     							= encoder_n_points
          snake_case			:  Any     							= decoder_n_points
          snake_case			:  str     							= two_stage
          snake_case			:  Optional[int]     							= two_stage_num_proposals
          snake_case			:  Optional[Any]     							= with_box_refine
          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			:  str     							= class_cost
          snake_case			:  Any     							= bbox_cost
          snake_case			:  List[str]     							= giou_cost
          # Loss coefficients
          snake_case			:  str     							= mask_loss_coefficient
          snake_case			:  Optional[Any]     							= dice_loss_coefficient
          snake_case			:  int     							= bbox_loss_coefficient
          snake_case			:  int     							= giou_loss_coefficient
          snake_case			:  Any     							= eos_coefficient
          snake_case			:  Any     							= focal_alpha
          snake_case			:  str     							= disable_custom_kernels
          super().__init__(is_encoder_decoder=snake_case__       ,		**snake_case__					)



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


          '''simple docstring'''


          return self.encoder_attention_heads



   @property
   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]					)						->      int:


          '''simple docstring'''


          return self.d_model



   def 							_SCREAMING_SNAKE_CASE  (self						:   str					)						->      List[Any]:


          '''simple docstring'''


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







from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_pt_objects import *  # noqa F403
else:
   from .scheduling_consistency_models import CMStochasticIterativeScheduler
   from .scheduling_ddim import DDIMScheduler
   from .scheduling_ddim_inverse import DDIMInverseScheduler
   from .scheduling_ddim_parallel import DDIMParallelScheduler
   from .scheduling_ddpm import DDPMScheduler
   from .scheduling_ddpm_parallel import DDPMParallelScheduler
   from .scheduling_deis_multistep import DEISMultistepScheduler
   from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
   from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
   from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
   from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
   from .scheduling_euler_discrete import EulerDiscreteScheduler
   from .scheduling_heun_discrete import HeunDiscreteScheduler
   from .scheduling_ipndm import IPNDMScheduler
   from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
   from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
   from .scheduling_karras_ve import KarrasVeScheduler
   from .scheduling_pndm import PNDMScheduler
   from .scheduling_repaint import RePaintScheduler
   from .scheduling_sde_ve import ScoreSdeVeScheduler
   from .scheduling_sde_vp import ScoreSdeVpScheduler
   from .scheduling_unclip import UnCLIPScheduler
   from .scheduling_unipc_multistep import UniPCMultistepScheduler
   from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
   from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_flax_objects import *  # noqa F403
else:
   from .scheduling_ddim_flax import FlaxDDIMScheduler
   from .scheduling_ddpm_flax import FlaxDDPMScheduler
   from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
   from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
   from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
   from .scheduling_pndm_flax import FlaxPNDMScheduler
   from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
   from .scheduling_utils_flax import (
       FlaxKarrasDiffusionSchedulers,
       FlaxSchedulerMixin,
       FlaxSchedulerOutput,
       broadcast_to_shape_from_left,
   )


try:
   if not (is_torch_available() and is_scipy_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
   from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
   if not (is_torch_available() and is_torchsde_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
   from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler





