
'''simple docstring'''

import copy

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


__snake_case:  Tuple								=			logging.get_logger(__name__)

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


class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					"deta"
     a_    	=					{
         "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_=0.02							,		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_=0.25							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

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

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


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return self.encoder_attention_heads


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return self.d_model






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							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_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case:  Dict								=			{"configuration_encoder_decoder": ["EncoderDecoderConfig"]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case:  List[str]								=			["EncoderDecoderModel"]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case:  Any								=			["TFEncoderDecoderModel"]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case:  List[str]								=			["FlaxEncoderDecoderModel"]

if TYPE_CHECKING:
    from .configuration_encoder_decoder import EncoderDecoderConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_encoder_decoder import EncoderDecoderModel

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
    import sys

    __snake_case:  Tuple								=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


