


import copy

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


SCREAMING_SNAKE_CASE			: Any				   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					{
    "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 _lowerCamelCase(		_a  ):
       lowercase_					:      Dict       					=     """deformable_detr"""
       lowercase_					:      str       					=     {
           """hidden_size""": """d_model""",
           """num_attention_heads""": """encoder_attention_heads""",
       }
       def __init__(   self, lowerCamelCase=True, lowerCamelCase=None, lowerCamelCase=3, lowerCamelCase=3_00, lowerCamelCase=10_24, lowerCamelCase=6, lowerCamelCase=10_24, lowerCamelCase=8, lowerCamelCase=6, lowerCamelCase=10_24, lowerCamelCase=8, lowerCamelCase=0.0, lowerCamelCase=True, lowerCamelCase="relu", lowerCamelCase=2_56, lowerCamelCase=0.1, lowerCamelCase=0.0, lowerCamelCase=0.0, lowerCamelCase=0.0_2, lowerCamelCase=1.0, lowerCamelCase=True, lowerCamelCase=False, lowerCamelCase="sine", lowerCamelCase="resnet50", lowerCamelCase=True, lowerCamelCase=False, lowerCamelCase=4, lowerCamelCase=4, lowerCamelCase=4, lowerCamelCase=False, lowerCamelCase=3_00, lowerCamelCase=False, lowerCamelCase=1, lowerCamelCase=5, lowerCamelCase=2, lowerCamelCase=1, lowerCamelCase=1, lowerCamelCase=5, lowerCamelCase=2, lowerCamelCase=0.1, lowerCamelCase=0.2_5, lowerCamelCase=False, **lowerCamelCase, )			->  Dict:




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




           """simple docstring"""

           _lowercase			:				Tuple 						=						copy.deepcopy(self.__dict__)
           if self.backbone_config is not None:
               _lowercase			:				str 						=						self.backbone_config.to_dict()
           _lowercase			:				List[str] 						=						self.__class__.model_type
           return output










"""simple docstring"""



import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class 			lowerCAmelCase__			(    __magic_name__							,						__magic_name__							,						__magic_name__   ):
   @register_to_config
   def __init__(		self      :	List[Any]							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	float							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	str							,			snake_case__      :	bool = False							,			):


        '''simple docstring'''
        super().__init__()

        UpperCAmelCase__						:    Optional[int]							=							nn.Embedding(snake_case__							,			snake_case__							)

        UpperCAmelCase__						:    List[Any]							=							nn.Embedding(snake_case__							,			snake_case__							)
        UpperCAmelCase__						:    Optional[Any]							=							False

        UpperCAmelCase__						:    Optional[int]							=							nn.Dropout(p=snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							TaConfig(
            vocab_size=snake_case__							,			d_model=snake_case__							,			num_heads=snake_case__							,			d_kv=snake_case__							,			d_ff=snake_case__							,			dropout_rate=snake_case__							,			feed_forward_proj=snake_case__							,			is_decoder=snake_case__							,			is_encoder_decoder=snake_case__							,			)

        UpperCAmelCase__						:    Tuple							=							nn.ModuleList()
        for lyr_num in range(snake_case__							):
             UpperCAmelCase__						:    Tuple							=							TaBlock(snake_case__							)
             self.encoders.append(snake_case__							)

        UpperCAmelCase__						:    str							=							TaLayerNorm(snake_case__							)
        UpperCAmelCase__						:    Tuple							=							nn.Dropout(p=snake_case__							)




   def       __a							(		self      :	int							,			snake_case__      :	List[str]							,			snake_case__      :	List[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							self.token_embedder(snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							encoder_input_tokens.shape[1]
        UpperCAmelCase__						:    List[Any]							=							torch.arange(snake_case__							,			device=encoder_input_tokens.device							)
        x += self.position_encoding(snake_case__							)

        UpperCAmelCase__						:    Union[str, Any]							=							self.dropout_pre(snake_case__							)

        # inverted the attention mask
        UpperCAmelCase__						:    List[Any]							=							encoder_input_tokens.size()
        UpperCAmelCase__						:    Tuple							=							self.get_extended_attention_mask(snake_case__							,			snake_case__							)

        for lyr in self.encoders:
             UpperCAmelCase__						:    Any							=							lyr(snake_case__							,			snake_case__							)[0]
        UpperCAmelCase__						:    Any							=							self.layer_norm(snake_case__							)

        return self.dropout_post(snake_case__							), encoder_inputs_mask


