




'''simple docstring'''





import copy

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


__a						:      Tuple       =						logging.get_logger(__name__)

__a						:      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        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   str       =						"""deformable_detr"""
     SCREAMING_SNAKE_CASE_							:   Dict       =						{
         """hidden_size""": """d_model""",
         """num_attention_heads""": """encoder_attention_heads""",
     }






     def __init__(			self ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=300 ,_SCREAMING_SNAKE_CASE=1_024 ,_SCREAMING_SNAKE_CASE=6 ,_SCREAMING_SNAKE_CASE=1_024 ,_SCREAMING_SNAKE_CASE=8 ,_SCREAMING_SNAKE_CASE=6 ,_SCREAMING_SNAKE_CASE=1_024 ,_SCREAMING_SNAKE_CASE=8 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE="relu" ,_SCREAMING_SNAKE_CASE=256 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.0_2 ,_SCREAMING_SNAKE_CASE=1.0 ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE="sine" ,_SCREAMING_SNAKE_CASE="resnet50" ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=4 ,_SCREAMING_SNAKE_CASE=4 ,_SCREAMING_SNAKE_CASE=4 ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=300 ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=1 ,_SCREAMING_SNAKE_CASE=5 ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE=1 ,_SCREAMING_SNAKE_CASE=1 ,_SCREAMING_SNAKE_CASE=5 ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.2_5 ,_SCREAMING_SNAKE_CASE=False ,**_SCREAMING_SNAKE_CASE ,)							->      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."   )
                    _snake_case							=				CONFIG_MAPPING["resnet"](out_features=["stage4"]   )
               elif isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ):
                    _snake_case							=				backbone_config.get("model_type"   )
                    _snake_case							=				CONFIG_MAPPING[backbone_model_type]
                    _snake_case							=				config_class.from_dict(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				use_timm_backbone
          _snake_case							=				backbone_config
          _snake_case							=				num_channels
          _snake_case							=				num_queries
          _snake_case							=				max_position_embeddings
          _snake_case							=				d_model
          _snake_case							=				encoder_ffn_dim
          _snake_case							=				encoder_layers
          _snake_case							=				encoder_attention_heads
          _snake_case							=				decoder_ffn_dim
          _snake_case							=				decoder_layers
          _snake_case							=				decoder_attention_heads
          _snake_case							=				dropout
          _snake_case							=				attention_dropout
          _snake_case							=				activation_dropout
          _snake_case							=				activation_function
          _snake_case							=				init_std
          _snake_case							=				init_xavier_std
          _snake_case							=				encoder_layerdrop
          _snake_case							=				auxiliary_loss
          _snake_case							=				position_embedding_type
          _snake_case							=				backbone
          _snake_case							=				use_pretrained_backbone
          _snake_case							=				dilation
          # deformable attributes
          _snake_case							=				num_feature_levels
          _snake_case							=				encoder_n_points
          _snake_case							=				decoder_n_points
          _snake_case							=				two_stage
          _snake_case							=				two_stage_num_proposals
          _snake_case							=				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							=				class_cost
          _snake_case							=				bbox_cost
          _snake_case							=				giou_cost
          # Loss coefficients
          _snake_case							=				mask_loss_coefficient
          _snake_case							=				dice_loss_coefficient
          _snake_case							=				bbox_loss_coefficient
          _snake_case							=				giou_loss_coefficient
          _snake_case							=				eos_coefficient
          _snake_case							=				focal_alpha
          _snake_case							=				disable_custom_kernels
          super().__init__(is_encoder_decoder=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






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






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






     def     _lowercase     (			self   )							->      Optional[int]:
          _snake_case							=				copy.deepcopy(self.__dict__   )
          if self.backbone_config is not None:
               _snake_case							=				self.backbone_config.to_dict()
          _snake_case							=				self.__class__.model_type
          return output








'''simple docstring'''





from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   Any       =						["""image_processor""", """tokenizer"""]
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						"""BridgeTowerImageProcessor"""
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						("""RobertaTokenizer""", """RobertaTokenizerFast""")






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Tuple:
          super().__init__(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






     def __call__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = 0 ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,)							->      BatchEncoding:
          _snake_case							=				self.tokenizer(
              text=_SCREAMING_SNAKE_CASE ,add_special_tokens=_SCREAMING_SNAKE_CASE ,padding=_SCREAMING_SNAKE_CASE ,truncation=_SCREAMING_SNAKE_CASE ,max_length=_SCREAMING_SNAKE_CASE ,stride=_SCREAMING_SNAKE_CASE ,pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,return_token_type_ids=_SCREAMING_SNAKE_CASE ,return_attention_mask=_SCREAMING_SNAKE_CASE ,return_overflowing_tokens=_SCREAMING_SNAKE_CASE ,return_special_tokens_mask=_SCREAMING_SNAKE_CASE ,return_offsets_mapping=_SCREAMING_SNAKE_CASE ,return_length=_SCREAMING_SNAKE_CASE ,verbose=_SCREAMING_SNAKE_CASE ,return_tensors=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ,)
          # add pixel_values + pixel_mask
          _snake_case							=				self.image_processor(
              _SCREAMING_SNAKE_CASE ,return_tensors=_SCREAMING_SNAKE_CASE ,do_normalize=_SCREAMING_SNAKE_CASE ,do_center_crop=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )
          encoding.update(_SCREAMING_SNAKE_CASE   )

          return encoding






     def     _lowercase     (			self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      List[str]:
          return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      List[str]:
          return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






     @property
     def     _lowercase     (			self   )							->      Any:
          _snake_case							=				self.tokenizer.model_input_names
          _snake_case							=				self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )


