


import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_A		       =						logging.get_logger(__name__)

_A		       =						{
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		List[Any]      =  "conditional_detr"
    UpperCAmelCase__						:		Optional[Any]      =  ["past_key_values"]
    UpperCAmelCase__						:		int      =  {
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }






    def __init__(			self					,   A_=True					,   A_=None					,   A_=3					,   A_=300					,   A_=6					,   A_=2048					,   A_=8					,   A_=6					,   A_=2048					,   A_=8					,   A_=0.0					,   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_=False					,   A_="sine"					,   A_="resnet50"					,   A_=True					,   A_=False					,   A_=2					,   A_=5					,   A_=2					,   A_=1					,   A_=1					,   A_=2					,   A_=5					,   A_=2					,   A_=0.25					,   **A_					,   )  ->       Dict:
         if backbone_config is not None and use_timm_backbone:
              raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'    )

         if not use_timm_backbone:
              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=['stage4']    )
              elif isinstance(A_					,   A_    ):
                   __UpperCamelCase    							=backbone_config.get('model_type'    )
                   __UpperCamelCase    							=CONFIG_MAPPING[backbone_model_type]
                   __UpperCamelCase    							=config_class.from_dict(A_    )

         __UpperCamelCase    							=use_timm_backbone
         __UpperCamelCase    							=backbone_config
         __UpperCamelCase    							=num_channels
         __UpperCamelCase    							=num_queries
         __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    							=decoder_layerdrop
         __UpperCamelCase    							=encoder_layers
         __UpperCamelCase    							=auxiliary_loss
         __UpperCamelCase    							=position_embedding_type
         __UpperCamelCase    							=backbone
         __UpperCamelCase    							=use_pretrained_backbone
         __UpperCamelCase    							=dilation
         # Hungarian matcher
         __UpperCamelCase    							=class_cost
         __UpperCamelCase    							=bbox_cost
         __UpperCamelCase    							=giou_cost
         # Loss coefficients
         __UpperCamelCase    							=mask_loss_coefficient
         __UpperCamelCase    							=dice_loss_coefficient
         __UpperCamelCase    							=cls_loss_coefficient
         __UpperCamelCase    							=bbox_loss_coefficient
         __UpperCamelCase    							=giou_loss_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    )  ->       Optional[Any]:
         __UpperCamelCase    							=copy.deepcopy(self.__dict__    )
         if self.backbone_config is not None:
              __UpperCamelCase    							=self.backbone_config.to_dict()
         __UpperCamelCase    							=self.__class__.model_type
         return output






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  version.parse("1.11"		)






    @property
    def _a					(			self    )  ->       Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                 ('pixel_mask', {0: 'batch'}),
             ]    )






    @property
    def _a					(			self    )  ->       float:
         return 1E-5






    @property
    def _a					(			self    )  ->       int:
         return 12









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A		       =						{'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ViTMAEForPreTraining',
       'ViTMAELayer',
       'ViTMAEModel',
       'ViTMAEPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'TFViTMAEForPreTraining',
       'TFViTMAEModel',
       'TFViTMAEPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit_mae import (
          VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTMAEForPreTraining,
          ViTMAELayer,
          ViTMAEModel,
          ViTMAEPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





