






from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase			:	Optional[Any]								=logging.get_logger(__name__)

_UpperCamelCase			:	str								={
    "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}






class 						UpperCAmelCase__ (							_a							):
		__snake_case      :       str     =   """xglm"""
		__snake_case      :       Dict     =   ["""past_key_values"""]

		__snake_case      :       str     =   {
		    """num_attention_heads""": """attention_heads""",
		    """hidden_size""": """d_model""",
		    """num_hidden_layers""": """num_layers""",
		}







		def __init__(					self       ,A__=256008       ,A__=2048       ,A__=1024       ,A__=4096       ,A__=24       ,A__=16       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=0.0       ,A__=0.0       ,A__=0.02       ,A__=True       ,A__=True       ,A__=2       ,A__=1       ,A__=0       ,A__=2       ,**A__       ,):
						_A	:				List[Any]   =      vocab_size
						_A	:				Union[str, Any]   =      max_position_embeddings
						_A	:				Tuple   =      d_model
						_A	:				Union[str, Any]   =      ffn_dim
						_A	:				str   =      num_layers
						_A	:				int   =      attention_heads
						_A	:				List[str]   =      activation_function
						_A	:				Tuple   =      dropout
						_A	:				int   =      attention_dropout
						_A	:				Optional[int]   =      activation_dropout
						_A	:				List[str]   =      layerdrop
						_A	:				Optional[Any]   =      init_std
						_A	:				Any   =      scale_embedding  # scale factor will be sqrt(d_model) if True
						_A	:				Union[str, Any]   =      use_cache

						super().__init__(
						    pad_token_id=A__       ,bos_token_id=A__       ,eos_token_id=A__       ,decoder_start_token_id=A__       ,**A__       ,)







import copy

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


SCREAMING_SNAKE_CASE			: List[str]				   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			: Any				   =					{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}






class _lowerCamelCase(		_a  ):
       lowercase_					:      Any       					=     """deta"""
       lowercase_					:      Union[str, Any]       					=     {
           """hidden_size""": """d_model""",
           """num_attention_heads""": """encoder_attention_heads""",
       }
       def __init__(   self, lowerCamelCase=None, lowerCamelCase=9_00, lowerCamelCase=20_48, lowerCamelCase=6, lowerCamelCase=20_48, 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=5, lowerCamelCase=4, lowerCamelCase=4, lowerCamelCase=True, lowerCamelCase=3_00, lowerCamelCase=True, lowerCamelCase=True, lowerCamelCase=1, lowerCamelCase=5, lowerCamelCase=2, lowerCamelCase=1, lowerCamelCase=1, lowerCamelCase=5, lowerCamelCase=2, lowerCamelCase=0.1, lowerCamelCase=0.2_5, **lowerCamelCase, )			->  Any:




           """simple docstring"""

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

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




           """simple docstring"""

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


