






'''simple docstring'''







import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


SCREAMING_SNAKE_CASE_      =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_      =			{
    "facebook/detr-resnet-50": "https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json",
    # See all DETR models at https://huggingface.co/models?filter=detr
}


class  lowerCAmelCase_						(       __lowerCAmelCase      ):





  """simple docstring"""



  a_       :int												="""detr"""
  a_       :List[str]												=["""past_key_values"""]
  a_       :str												={
      """hidden_size""": """d_model""",
      """num_attention_heads""": """encoder_attention_heads""",
  }

  def __init__(					self     :							Any				,							SCREAMING_SNAKE_CASE__     :							Dict=True				,							SCREAMING_SNAKE_CASE__     :							List[Any]=None				,							SCREAMING_SNAKE_CASE__     :							Any=3				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=1_0_0				,							SCREAMING_SNAKE_CASE__     :							int=6				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=2_0_4_8				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=8				,							SCREAMING_SNAKE_CASE__     :							str=6				,							SCREAMING_SNAKE_CASE__     :							List[str]=2_0_4_8				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=8				,							SCREAMING_SNAKE_CASE__     :							str=0.0				,							SCREAMING_SNAKE_CASE__     :							List[Any]=0.0				,							SCREAMING_SNAKE_CASE__     :							Any=True				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]="relu"				,							SCREAMING_SNAKE_CASE__     :							Tuple=2_5_6				,							SCREAMING_SNAKE_CASE__     :							Any=0.1				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=0.0				,							SCREAMING_SNAKE_CASE__     :							Dict=0.0				,							SCREAMING_SNAKE_CASE__     :							Dict=0.0_2				,							SCREAMING_SNAKE_CASE__     :							Dict=1.0				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=False				,							SCREAMING_SNAKE_CASE__     :							Dict="sine"				,							SCREAMING_SNAKE_CASE__     :							Dict="resnet50"				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=True				,							SCREAMING_SNAKE_CASE__     :							Tuple=False				,							SCREAMING_SNAKE_CASE__     :							str=1				,							SCREAMING_SNAKE_CASE__     :							int=5				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=2				,							SCREAMING_SNAKE_CASE__     :							List[Any]=1				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=1				,							SCREAMING_SNAKE_CASE__     :							str=5				,							SCREAMING_SNAKE_CASE__     :							List[Any]=2				,							SCREAMING_SNAKE_CASE__     :							List[str]=0.1				,							**SCREAMING_SNAKE_CASE__     :							Any				,							):





       '''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(_UpperCamelCase				,							_UpperCamelCase						):
                 __a       =   backbone_config.get("""model_type"""						)
                 __a       =   CONFIG_MAPPING[backbone_model_type]
                 __a       =   config_class.from_dict(_UpperCamelCase						)
            # set timm attributes to None
            __a       =   None, None, None

       __a       =   use_timm_backbone
       __a       =   backbone_config
       __a       =   num_channels
       __a       =   num_queries
       __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       =   decoder_layerdrop
       __a       =   encoder_layers
       __a       =   auxiliary_loss
       __a       =   position_embedding_type
       __a       =   backbone
       __a       =   use_pretrained_backbone
       __a       =   dilation
       # 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
       super().__init__(is_encoder_decoder=_UpperCamelCase				,							**_UpperCamelCase						)

  @property
  def __a						(					self     :							Union[str, Any]						):





       '''simple docstring'''

       return self.encoder_attention_heads

  @property
  def __a						(					self     :							Dict						):





       '''simple docstring'''

       return self.d_model

  @classmethod
  def __a						(					cls     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							PretrainedConfig				,							**SCREAMING_SNAKE_CASE__     :							List[str]						):





       '''simple docstring'''

       return cls(backbone_config=_UpperCamelCase				,							**_UpperCamelCase						)


  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       __a       =   copy.deepcopy(self.__dict__						)
       if output["backbone_config"] is not None:
            __a       =   self.backbone_config.to_dict()
       __a       =   self.__class__.model_type
       return output


class  lowerCAmelCase_						(       __lowerCAmelCase      ):





  """simple docstring"""



  a_       :Optional[int]												=version.parse("""1.11"""      )

  @property
  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
               ("""pixel_mask""", {0: """batch"""}),
           ]						)

  @property
  def __a						(					self     :							int						):





       '''simple docstring'''

       return 1E-5


  @property
  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       return 1_2










'''simple docstring'''







from ...processing_utils import ProcessorMixin


class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  a_       :Dict												=["""image_processor""", """feature_extractor"""]
  a_       :str												="""TvltImageProcessor"""
  a_       :str												="""TvltFeatureExtractor"""

  def __init__(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]						):





       '''simple docstring'''

       super().__init__(image_processor=SCREAMING_SNAKE_CASE__				,							feature_extractor=SCREAMING_SNAKE_CASE__						)

       __a       =   image_processor
       __a       =   feature_extractor

  def __call__(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							str=None				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=None				,							SCREAMING_SNAKE_CASE__     :							Tuple=None				,							SCREAMING_SNAKE_CASE__     :							List[Any]=None				,							SCREAMING_SNAKE_CASE__     :							Any=False				,							SCREAMING_SNAKE_CASE__     :							List[str]=False				,							*SCREAMING_SNAKE_CASE__     :							str				,							**SCREAMING_SNAKE_CASE__     :							Optional[int]				,							):





       '''simple docstring'''

       if images is None and audio is None:
            raise ValueError("""You need to specify either an `images` or `audio` input to process."""						)

       __a       =   None
       if images is not None:
            __a       =   self.image_processor(SCREAMING_SNAKE_CASE__				,							mask_pixel=SCREAMING_SNAKE_CASE__				,							*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)
       if images_mixed is not None:
            __a       =   self.image_processor(SCREAMING_SNAKE_CASE__				,							is_mixed=SCREAMING_SNAKE_CASE__				,							*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)
       if audio is not None:
            __a       =   self.feature_extractor(
                SCREAMING_SNAKE_CASE__				,							*SCREAMING_SNAKE_CASE__				,							sampling_rate=SCREAMING_SNAKE_CASE__				,							mask_audio=SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)

       __a       =   {}
       if audio is not None:
            output_dict.update(SCREAMING_SNAKE_CASE__						)
       if images is not None:
            output_dict.update(SCREAMING_SNAKE_CASE__						)
       if images_mixed_dict is not None:
            output_dict.update(SCREAMING_SNAKE_CASE__						)
       return output_dict


  @property
  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       __a       =   self.image_processor.model_input_names
       __a       =   self.feature_extractor.model_input_names
       return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names						)						)






