


import copy

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


_A		       =						logging.get_logger(__name__)

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






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Dict      =  "deta"
    UpperCAmelCase__						:		str      =  {
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }






    def __init__(			self					,   A_=None					,   A_=900					,   A_=2048					,   A_=6					,   A_=2048					,   A_=8					,   A_=6					,   A_=1024					,   A_=8					,   A_=0.0					,   A_=True					,   A_="relu"					,   A_=256					,   A_=0.1					,   A_=0.0					,   A_=0.0					,   A_=0.02					,   A_=1.0					,   A_=True					,   A_=False					,   A_="sine"					,   A_=5					,   A_=4					,   A_=4					,   A_=True					,   A_=300					,   A_=True					,   A_=True					,   A_=1					,   A_=5					,   A_=2					,   A_=1					,   A_=1					,   A_=5					,   A_=2					,   A_=0.1					,   A_=0.25					,   **A_					,   )  ->       Optional[int]:
         if backbone_config is None:
              logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'    )
              __UpperCamelCase    							=CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4']    )
         else:
              if isinstance(A_					,   A_    ):
                   __UpperCamelCase    							=backbone_config.pop('model_type'    )
                   __UpperCamelCase    							=CONFIG_MAPPING[backbone_model_type]
                   __UpperCamelCase    							=config_class.from_dict(A_    )

         __UpperCamelCase    							=backbone_config
         __UpperCamelCase    							=num_queries
         __UpperCamelCase    							=max_position_embeddings
         __UpperCamelCase    							=d_model
         __UpperCamelCase    							=encoder_ffn_dim
         __UpperCamelCase    							=encoder_layers
         __UpperCamelCase    							=encoder_attention_heads
         __UpperCamelCase    							=decoder_ffn_dim
         __UpperCamelCase    							=decoder_layers
         __UpperCamelCase    							=decoder_attention_heads
         __UpperCamelCase    							=dropout
         __UpperCamelCase    							=attention_dropout
         __UpperCamelCase    							=activation_dropout
         __UpperCamelCase    							=activation_function
         __UpperCamelCase    							=init_std
         __UpperCamelCase    							=init_xavier_std
         __UpperCamelCase    							=encoder_layerdrop
         __UpperCamelCase    							=auxiliary_loss
         __UpperCamelCase    							=position_embedding_type
         # deformable attributes
         __UpperCamelCase    							=num_feature_levels
         __UpperCamelCase    							=encoder_n_points
         __UpperCamelCase    							=decoder_n_points
         __UpperCamelCase    							=two_stage
         __UpperCamelCase    							=two_stage_num_proposals
         __UpperCamelCase    							=with_box_refine
         __UpperCamelCase    							=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
         __UpperCamelCase    							=class_cost
         __UpperCamelCase    							=bbox_cost
         __UpperCamelCase    							=giou_cost
         # Loss coefficients
         __UpperCamelCase    							=mask_loss_coefficient
         __UpperCamelCase    							=dice_loss_coefficient
         __UpperCamelCase    							=bbox_loss_coefficient
         __UpperCamelCase    							=giou_loss_coefficient
         __UpperCamelCase    							=eos_coefficient
         __UpperCamelCase    							=focal_alpha
         super().__init__(is_encoder_decoder=A_					,   **A_    )






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






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






    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=copy.deepcopy(self.__dict__    )
         __UpperCamelCase    							=self.backbone_config.to_dict()
         __UpperCamelCase    							=self.__class__.model_type
         return output









import warnings

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


_A		       =						logging.get_logger(__name__)

_A		       =						{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "mvp"
    UpperCAmelCase__						:		Tuple      =  ["past_key_values"]
    UpperCAmelCase__						:		Union[str, Any]      =  {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






    def __init__(			self					,   A_=50267					,   A_=1024					,   A_=12					,   A_=4096					,   A_=16					,   A_=12					,   A_=4096					,   A_=16					,   A_=0.0					,   A_=0.0					,   A_="gelu"					,   A_=1024					,   A_=0.1					,   A_=0.0					,   A_=0.0					,   A_=0.02					,   A_=0.0					,   A_=False					,   A_=True					,   A_=1					,   A_=0					,   A_=2					,   A_=True					,   A_=2					,   A_=2					,   A_=False					,   A_=100					,   A_=800					,   **A_					,   )  ->       Union[str, Any]:
         __UpperCamelCase    							=vocab_size
         __UpperCamelCase    							=max_position_embeddings
         __UpperCamelCase    							=d_model
         __UpperCamelCase    							=encoder_ffn_dim
         __UpperCamelCase    							=encoder_layers
         __UpperCamelCase    							=encoder_attention_heads
         __UpperCamelCase    							=decoder_ffn_dim
         __UpperCamelCase    							=decoder_layers
         __UpperCamelCase    							=decoder_attention_heads
         __UpperCamelCase    							=dropout
         __UpperCamelCase    							=attention_dropout
         __UpperCamelCase    							=activation_dropout
         __UpperCamelCase    							=activation_function
         __UpperCamelCase    							=init_std
         __UpperCamelCase    							=encoder_layerdrop
         __UpperCamelCase    							=decoder_layerdrop
         __UpperCamelCase    							=classifier_dropout
         __UpperCamelCase    							=use_cache
         __UpperCamelCase    							=encoder_layers
         __UpperCamelCase    							=scale_embedding  # scale factor will be sqrt(d_model) if True
         __UpperCamelCase    							=use_prompt
         __UpperCamelCase    							=prompt_length
         __UpperCamelCase    							=prompt_mid_dim

         super().__init__(
             pad_token_id=A_					,   bos_token_id=A_					,   eos_token_id=A_					,   is_encoder_decoder=A_					,   decoder_start_token_id=A_					,   forced_eos_token_id=A_					,   **A_					,   )

         if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'					,   A_    ):
              __UpperCamelCase    							=self.bos_token_id
              warnings.warn(
                  f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
                  'The config can simply be saved and uploaded again to be fixed.'    )





