





'''simple docstring'''




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__    :      Tuple						=    {
    '''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}


class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       '''deta'''
     lowerCAmelCase					=       {
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
     }


     def __init__(						self						,	_UpperCAmelCase=None						,	_UpperCAmelCase=900						,	_UpperCAmelCase=2048						,	_UpperCAmelCase=6						,	_UpperCAmelCase=2048						,	_UpperCAmelCase=8						,	_UpperCAmelCase=6						,	_UpperCAmelCase=1024						,	_UpperCAmelCase=8						,	_UpperCAmelCase=0.0						,	_UpperCAmelCase=True						,	_UpperCAmelCase="relu"						,	_UpperCAmelCase=256						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=0.0						,	_UpperCAmelCase=0.0						,	_UpperCAmelCase=0.02						,	_UpperCAmelCase=1.0						,	_UpperCAmelCase=True						,	_UpperCAmelCase=False						,	_UpperCAmelCase="sine"						,	_UpperCAmelCase=5						,	_UpperCAmelCase=4						,	_UpperCAmelCase=4						,	_UpperCAmelCase=True						,	_UpperCAmelCase=300						,	_UpperCAmelCase=True						,	_UpperCAmelCase=True						,	_UpperCAmelCase=1						,	_UpperCAmelCase=5						,	_UpperCAmelCase=2						,	_UpperCAmelCase=1						,	_UpperCAmelCase=1						,	_UpperCAmelCase=5						,	_UpperCAmelCase=2						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=0.25						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          if backbone_config is None:
               logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.')
               __A						: Dict      =       CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4'])
          else:
               if isinstance(_UpperCAmelCase						,	_UpperCAmelCase):
                    __A						: List[str]      =       backbone_config.pop('model_type')
                    __A						: str      =       CONFIG_MAPPING[backbone_model_type]
                    __A						: Any      =       config_class.from_dict(_UpperCAmelCase)

          __A						: int      =       backbone_config
          __A						: List[str]      =       num_queries
          __A						: str      =       max_position_embeddings
          __A						: Optional[int]      =       d_model
          __A						: Any      =       encoder_ffn_dim
          __A						: int      =       encoder_layers
          __A						: str      =       encoder_attention_heads
          __A						: Dict      =       decoder_ffn_dim
          __A						: Optional[Any]      =       decoder_layers
          __A						: List[str]      =       decoder_attention_heads
          __A						: int      =       dropout
          __A						: int      =       attention_dropout
          __A						: int      =       activation_dropout
          __A						: int      =       activation_function
          __A						: Any      =       init_std
          __A						: List[Any]      =       init_xavier_std
          __A						: List[str]      =       encoder_layerdrop
          __A						: Optional[Any]      =       auxiliary_loss
          __A						: str      =       position_embedding_type
          # deformable attributes
          __A						: Optional[Any]      =       num_feature_levels
          __A						: Optional[int]      =       encoder_n_points
          __A						: int      =       decoder_n_points
          __A						: int      =       two_stage
          __A						: Union[str, Any]      =       two_stage_num_proposals
          __A						: Union[str, Any]      =       with_box_refine
          __A						: Dict      =       assign_first_stage
          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						: int      =       class_cost
          __A						: str      =       bbox_cost
          __A						: str      =       giou_cost
          # Loss coefficients
          __A						: Tuple      =       mask_loss_coefficient
          __A						: Optional[Any]      =       dice_loss_coefficient
          __A						: int      =       bbox_loss_coefficient
          __A						: str      =       giou_loss_coefficient
          __A						: List[str]      =       eos_coefficient
          __A						: Union[str, Any]      =       focal_alpha
          super().__init__(is_encoder_decoder=_UpperCAmelCase						,	**_UpperCAmelCase)


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return self.encoder_attention_heads


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return self.d_model


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[Any]      =       copy.deepcopy(self.__dict__)
          __A						: int      =       self.backbone_config.to_dict()
          __A						: Dict      =       self.__class__.model_type
          return output






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__    :      List[str]						=    {
    '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
    '''processing_git''': ['''GitProcessor'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__    :      Any						=    [
      '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''GitForCausalLM''',
      '''GitModel''',
      '''GitPreTrainedModel''',
      '''GitVisionModel''',
  ]

if TYPE_CHECKING:
  from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
  from .processing_git import GitProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_git import (
        GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        GitForCausalLM,
        GitModel,
        GitPreTrainedModel,
        GitVisionModel,
    )

else:
  import sys

  lowercase__    :      List[str]						=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)