import copy

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


_lowercase							:      Union[str, Any]      							=logging.get_logger(__name__)

_lowercase							:      Optional[int]      							={
    '''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  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Tuple            =				"deformable_detr"
  lowercase		:     Optional[int]            =				{
      "hidden_size": "d_model",
      "num_attention_heads": "encoder_attention_heads",
  }







  def __init__(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=True      ,	SCREAMING_SNAKE_CASE__    :					str=None      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=3      ,	SCREAMING_SNAKE_CASE__    :					int=3_00      ,	SCREAMING_SNAKE_CASE__    :					int=10_24      ,	SCREAMING_SNAKE_CASE__    :					List[str]=6      ,	SCREAMING_SNAKE_CASE__    :					Tuple=10_24      ,	SCREAMING_SNAKE_CASE__    :					Any=8      ,	SCREAMING_SNAKE_CASE__    :					Any=6      ,	SCREAMING_SNAKE_CASE__    :					int=10_24      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=8      ,	SCREAMING_SNAKE_CASE__    :					int=0.0      ,	SCREAMING_SNAKE_CASE__    :					str=True      ,	SCREAMING_SNAKE_CASE__    :					Dict="relu"      ,	SCREAMING_SNAKE_CASE__    :					Any=2_56      ,	SCREAMING_SNAKE_CASE__    :					List[str]=0.1      ,	SCREAMING_SNAKE_CASE__    :					str=0.0      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]=0.0      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					Tuple=1.0      ,	SCREAMING_SNAKE_CASE__    :					Tuple=True      ,	SCREAMING_SNAKE_CASE__    :					int=False      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]="sine"      ,	SCREAMING_SNAKE_CASE__    :					Tuple="resnet50"      ,	SCREAMING_SNAKE_CASE__    :					str=True      ,	SCREAMING_SNAKE_CASE__    :					Any=False      ,	SCREAMING_SNAKE_CASE__    :					int=4      ,	SCREAMING_SNAKE_CASE__    :					Any=4      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=4      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]=False      ,	SCREAMING_SNAKE_CASE__    :					int=3_00      ,	SCREAMING_SNAKE_CASE__    :					Dict=False      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=1      ,	SCREAMING_SNAKE_CASE__    :					List[str]=5      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=2      ,	SCREAMING_SNAKE_CASE__    :					str=1      ,	SCREAMING_SNAKE_CASE__    :					Dict=1      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=5      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=2      ,	SCREAMING_SNAKE_CASE__    :					List[str]=0.1      ,	SCREAMING_SNAKE_CASE__    :					str=0.2_5      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=False      ,	**SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	)    ->				Optional[Any]:
         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.'     )
                       A						:     Optional[int]         =CONFIG_MAPPING['resnet'](out_features=['stage4']     )
                elif isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ):
                       A						:     int         =backbone_config.get('model_type'     )
                       A						:     Optional[Any]         =CONFIG_MAPPING[backbone_model_type]
                       A						:     Dict         =config_class.from_dict(SCREAMING_SNAKE_CASE__     )
         A						:     str         =use_timm_backbone
         A						:     Union[str, Any]         =backbone_config
         A						:     Dict         =num_channels
         A						:     Dict         =num_queries
         A						:     List[Any]         =max_position_embeddings
         A						:     Optional[int]         =d_model
         A						:     Optional[Any]         =encoder_ffn_dim
         A						:     Tuple         =encoder_layers
         A						:     Union[str, Any]         =encoder_attention_heads
         A						:     Optional[int]         =decoder_ffn_dim
         A						:     Union[str, Any]         =decoder_layers
         A						:     Tuple         =decoder_attention_heads
         A						:     Optional[Any]         =dropout
         A						:     Dict         =attention_dropout
         A						:     Dict         =activation_dropout
         A						:     Any         =activation_function
         A						:     str         =init_std
         A						:     Any         =init_xavier_std
         A						:     Optional[Any]         =encoder_layerdrop
         A						:     Union[str, Any]         =auxiliary_loss
         A						:     List[str]         =position_embedding_type
         A						:     List[str]         =backbone
         A						:     int         =use_pretrained_backbone
         A						:     int         =dilation
         # deformable attributes
         A						:     List[Any]         =num_feature_levels
         A						:     Optional[Any]         =encoder_n_points
         A						:     List[str]         =decoder_n_points
         A						:     Optional[Any]         =two_stage
         A						:     List[str]         =two_stage_num_proposals
         A						:     Optional[int]         =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
         A						:     Optional[Any]         =class_cost
         A						:     Tuple         =bbox_cost
         A						:     Optional[Any]         =giou_cost
         # Loss coefficients
         A						:     Union[str, Any]         =mask_loss_coefficient
         A						:     Dict         =dice_loss_coefficient
         A						:     List[str]         =bbox_loss_coefficient
         A						:     Optional[int]         =giou_loss_coefficient
         A						:     Any         =eos_coefficient
         A						:     Any         =focal_alpha
         A						:     Tuple         =disable_custom_kernels
         super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )







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







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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Optional[int]:
         A						:     List[Any]         =copy.deepcopy(self.__dict__     )
         if self.backbone_config is not None:
                A						:     str         =self.backbone_config.to_dict()
         A						:     Dict         =self.__class__.model_type
         return output







import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_lowercase							:      Any      							=logging.getLogger(__name__)



@dataclass
class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Optional[float]            =				field(
      default=0.0   ,						metadata={"help": "The label smoothing epsilon to apply (if not zero)."}			)
  lowercase		:     bool            =				field(default=lowerCAmelCase_   ,						metadata={"help": "Whether to SortishSamler or not."}			)
  lowercase		:     bool            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}			)
  lowercase		:     bool            =				field(default=lowerCAmelCase_   ,						metadata={"help": "whether to use adafactor"}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Encoder layer dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Decoder layer dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(default=lowerCAmelCase_   ,						metadata={"help": "Dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Attention dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[str]            =				field(
      default="linear"   ,						metadata={"help": f'Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()			)}'}   ,						)






