

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     a__  (    _UpperCAmelCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				'deformable_detr'
			__lowerCamelCase       				=				{
			    'hidden_size': 'd_model',
			    'num_attention_heads': 'encoder_attention_heads',
			}


			def __init__(			self					,					lowercase=True					,					lowercase=None					,					lowercase=3					,					lowercase=300					,					lowercase=1024					,					lowercase=6					,					lowercase=1024					,					lowercase=8					,					lowercase=6					,					lowercase=1024					,					lowercase=8					,					lowercase=0.0					,					lowercase=True					,					lowercase="relu"					,					lowercase=256					,					lowercase=0.1					,					lowercase=0.0					,					lowercase=0.0					,					lowercase=0.02					,					lowercase=1.0					,					lowercase=True					,					lowercase=False					,					lowercase="sine"					,					lowercase="resnet50"					,					lowercase=True					,					lowercase=False					,					lowercase=4					,					lowercase=4					,					lowercase=4					,					lowercase=False					,					lowercase=300					,					lowercase=False					,					lowercase=1					,					lowercase=5					,					lowercase=2					,					lowercase=1					,					lowercase=1					,					lowercase=5					,					lowercase=2					,					lowercase=0.1					,					lowercase=0.25					,					lowercase=False					,					**lowercase					,					)     ->  int:
							'''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."				)
															A__						=	CONFIG_MAPPING["resnet"](out_features=["stage4"]				)
											elif isinstance(lowercase					,					lowercase				):
															A__						=	backbone_config.get("model_type"				)
															A__						=	CONFIG_MAPPING[backbone_model_type]
															A__						=	config_class.from_dict(lowercase				)
							A__						=	use_timm_backbone
							A__						=	backbone_config
							A__						=	num_channels
							A__						=	num_queries
							A__						=	max_position_embeddings
							A__						=	d_model
							A__						=	encoder_ffn_dim
							A__						=	encoder_layers
							A__						=	encoder_attention_heads
							A__						=	decoder_ffn_dim
							A__						=	decoder_layers
							A__						=	decoder_attention_heads
							A__						=	dropout
							A__						=	attention_dropout
							A__						=	activation_dropout
							A__						=	activation_function
							A__						=	init_std
							A__						=	init_xavier_std
							A__						=	encoder_layerdrop
							A__						=	auxiliary_loss
							A__						=	position_embedding_type
							A__						=	backbone
							A__						=	use_pretrained_backbone
							A__						=	dilation
							# deformable attributes
							A__						=	num_feature_levels
							A__						=	encoder_n_points
							A__						=	decoder_n_points
							A__						=	two_stage
							A__						=	two_stage_num_proposals
							A__						=	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__						=	class_cost
							A__						=	bbox_cost
							A__						=	giou_cost
							# Loss coefficients
							A__						=	mask_loss_coefficient
							A__						=	dice_loss_coefficient
							A__						=	bbox_loss_coefficient
							A__						=	giou_loss_coefficient
							A__						=	eos_coefficient
							A__						=	focal_alpha
							A__						=	disable_custom_kernels
							super().__init__(is_encoder_decoder=lowercase					,					**lowercase				)


			@property
			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							return self.encoder_attention_heads


			@property
			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							return self.d_model


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							A__						=	copy.deepcopy(self.__dict__				)
							if self.backbone_config is not None:
											A__						=	self.backbone_config.to_dict()
							A__						=	self.__class__.model_type
							return output








"""simple docstring"""





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer

@dataclass
class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      42







class 		lowercase					(					_UpperCAmelCase     ,  _UpperCAmelCase					):





      @register_to_config
      def __init__(       self      ,						lowercase = 3      ,						lowercase = 3      ,						lowercase = ("DownEncoderBlock2D",)      ,						lowercase = ("UpDecoderBlock2D",)      ,						lowercase = (64,)      ,						lowercase = 1      ,						lowercase = "silu"      ,						lowercase = 3      ,						lowercase = 32      ,						lowercase = 256      ,						lowercase = 32      ,						lowercase = None      ,						lowercase = 0.18_215      ,						lowercase = "group"      ,						)		->     Union[str, Any]:
            super().__init__()

            # pass init params to Encoder
            lowerCAmelCase  						=       Encoder(
                in_channels=lowercase      ,						out_channels=lowercase      ,						down_block_types=lowercase      ,						block_out_channels=lowercase      ,						layers_per_block=lowercase      ,						act_fn=lowercase      ,						norm_num_groups=lowercase      ,						double_z=lowercase      ,						)

            lowerCAmelCase  						=       vq_embed_dim if vq_embed_dim is not None else latent_channels

            lowerCAmelCase  						=       nn.Convad(lowercase      ,						lowercase      ,						1 )
            lowerCAmelCase  						=       VectorQuantizer(lowercase      ,						lowercase      ,						beta=0.25      ,						remap=lowercase      ,						sane_index_shape=lowercase )
            lowerCAmelCase  						=       nn.Convad(lowercase      ,						lowercase      ,						1 )

            # pass init params to Decoder
            lowerCAmelCase  						=       Decoder(
                in_channels=lowercase      ,						out_channels=lowercase      ,						up_block_types=lowercase      ,						block_out_channels=lowercase      ,						layers_per_block=lowercase      ,						act_fn=lowercase      ,						norm_num_groups=lowercase      ,						norm_type=lowercase      ,						)





      @apply_forward_hook
      def     _snake_case			(       self      ,						lowercase      ,						lowercase = True )		->     VQEncoderOutput:
            lowerCAmelCase  						=       self.encoder(lowercase )
            lowerCAmelCase  						=       self.quant_conv(lowercase )

            if not return_dict:
                  return (h,)

            return VQEncoderOutput(latents=lowercase )





      @apply_forward_hook
      def     _snake_case			(       self      ,						lowercase      ,						lowercase = False      ,						lowercase = True )		->     Union[DecoderOutput, torch.FloatTensor]:
            # also go through quantization layer
            if not force_not_quantize:
                  lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=       self.quantize(lowercase )
            else:
                  lowerCAmelCase  						=       h
            lowerCAmelCase  						=       self.post_quant_conv(lowercase )
            lowerCAmelCase  						=       self.decoder(lowercase      ,						quant if self.config.norm_type == """spatial""" else None )

            if not return_dict:
                  return (dec,)

            return DecoderOutput(sample=lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = True )		->     Union[DecoderOutput, torch.FloatTensor]:
            lowerCAmelCase  						=       sample
            lowerCAmelCase  						=       self.encode(lowercase ).latents
            lowerCAmelCase  						=       self.decode(lowercase ).sample

            if not return_dict:
                  return (dec,)

            return DecoderOutput(sample=lowercase )






