
"""simple docstring"""
import copy

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


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

lowercase_       :						Optional[int]     					=	{
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}
class 			UpperCamelCase      (							SCREAMING_SNAKE_CASE__  ):
     A__      =			"""deta"""
     A__      =			{
         """hidden_size""": """d_model""",
         """num_attention_heads""": """encoder_attention_heads""",
     }
     def __init__(     self  ,     snake_case__=None  ,     snake_case__=900  ,     snake_case__=2048  ,     snake_case__=6  ,     snake_case__=2048  ,     snake_case__=8  ,     snake_case__=6  ,     snake_case__=1024  ,     snake_case__=8  ,     snake_case__=0.0  ,     snake_case__=True  ,     snake_case__="relu"  ,     snake_case__=256  ,     snake_case__=0.1  ,     snake_case__=0.0  ,     snake_case__=0.0  ,     snake_case__=0.02  ,     snake_case__=1.0  ,     snake_case__=True  ,     snake_case__=False  ,     snake_case__="sine"  ,     snake_case__=5  ,     snake_case__=4  ,     snake_case__=4  ,     snake_case__=True  ,     snake_case__=300  ,     snake_case__=True  ,     snake_case__=True  ,     snake_case__=1  ,     snake_case__=5  ,     snake_case__=2  ,     snake_case__=1  ,     snake_case__=1  ,     snake_case__=5  ,     snake_case__=2  ,     snake_case__=0.1  ,     snake_case__=0.25  ,     **snake_case__  ,     ):




       """simple docstring"""


       if backbone_config is None:
         logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."				)
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]				)
       else:
         if isinstance(snake_case__  ,     snake_case__				):
           _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = backbone_config.pop("model_type"				)
           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = CONFIG_MAPPING[backbone_model_type]
           _SCREAMING_SNAKE_CASE   :				Tuple							    = config_class.from_dict(snake_case__				)

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


       _SCREAMING_SNAKE_CASE   :				Dict							    = copy.deepcopy(self.__dict__				)
       _SCREAMING_SNAKE_CASE   :				int							    = self.backbone_config.to_dict()
       _SCREAMING_SNAKE_CASE   :				List[str]							    = self.__class__.model_type
       return output





"""simple docstring"""
import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


lowercase_       :						Union[str, Any]     					=	logging.get_logger(__name__)
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     def __init__(     self  ,     *snake_case__  ,     **snake_case__				):




       """simple docstring"""


       warnings.warn(
           "The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
           " Please use LayoutLMv2ImageProcessor instead."  ,     snake_case__  ,     )
       super().__init__(*snake_case__  ,     **snake_case__				)



