


import copy

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


SCREAMING_SNAKE_CASE			: Any				   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					{
    "SenseTime/deformable-detr": "https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json",
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}






class _lowerCamelCase(		_a  ):
       lowercase_					:      Dict       					=     """deformable_detr"""
       lowercase_					:      str       					=     {
           """hidden_size""": """d_model""",
           """num_attention_heads""": """encoder_attention_heads""",
       }
       def __init__(   self, lowerCamelCase=True, lowerCamelCase=None, lowerCamelCase=3, lowerCamelCase=3_00, lowerCamelCase=10_24, lowerCamelCase=6, lowerCamelCase=10_24, 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="resnet50", lowerCamelCase=True, lowerCamelCase=False, lowerCamelCase=4, lowerCamelCase=4, lowerCamelCase=4, lowerCamelCase=False, lowerCamelCase=3_00, lowerCamelCase=False, lowerCamelCase=1, lowerCamelCase=5, lowerCamelCase=2, lowerCamelCase=1, lowerCamelCase=1, lowerCamelCase=5, lowerCamelCase=2, lowerCamelCase=0.1, lowerCamelCase=0.2_5, lowerCamelCase=False, **lowerCamelCase, )			->  Dict:




           """simple docstring"""

           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.')
                   _lowercase			:				str 						=						CONFIG_MAPPING['resnet'](out_features=['stage4'])
               elif isinstance(lowerCamelCase, lowerCamelCase):
                   _lowercase			:				str 						=						backbone_config.get('model_type')
                   _lowercase			:				Union[str, Any] 						=						CONFIG_MAPPING[backbone_model_type]
                   _lowercase			:				str 						=						config_class.from_dict(lowerCamelCase)
           _lowercase			:				Dict 						=						use_timm_backbone
           _lowercase			:				Optional[Any] 						=						backbone_config
           _lowercase			:				List[str] 						=						num_channels
           _lowercase			:				Tuple 						=						num_queries
           _lowercase			:				Union[str, Any] 						=						max_position_embeddings
           _lowercase			:				Dict 						=						d_model
           _lowercase			:				List[str] 						=						encoder_ffn_dim
           _lowercase			:				str 						=						encoder_layers
           _lowercase			:				Optional[int] 						=						encoder_attention_heads
           _lowercase			:				Tuple 						=						decoder_ffn_dim
           _lowercase			:				Any 						=						decoder_layers
           _lowercase			:				Optional[int] 						=						decoder_attention_heads
           _lowercase			:				Union[str, Any] 						=						dropout
           _lowercase			:				int 						=						attention_dropout
           _lowercase			:				Optional[Any] 						=						activation_dropout
           _lowercase			:				Dict 						=						activation_function
           _lowercase			:				int 						=						init_std
           _lowercase			:				List[str] 						=						init_xavier_std
           _lowercase			:				str 						=						encoder_layerdrop
           _lowercase			:				Optional[int] 						=						auxiliary_loss
           _lowercase			:				str 						=						position_embedding_type
           _lowercase			:				List[str] 						=						backbone
           _lowercase			:				List[str] 						=						use_pretrained_backbone
           _lowercase			:				List[str] 						=						dilation
           # deformable attributes
           _lowercase			:				str 						=						num_feature_levels
           _lowercase			:				Optional[int] 						=						encoder_n_points
           _lowercase			:				Optional[int] 						=						decoder_n_points
           _lowercase			:				Union[str, Any] 						=						two_stage
           _lowercase			:				Optional[Any] 						=						two_stage_num_proposals
           _lowercase			:				List[str] 						=						with_box_refine
           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			:				Dict 						=						class_cost
           _lowercase			:				Optional[Any] 						=						bbox_cost
           _lowercase			:				Dict 						=						giou_cost
           # Loss coefficients
           _lowercase			:				str 						=						mask_loss_coefficient
           _lowercase			:				Dict 						=						dice_loss_coefficient
           _lowercase			:				Optional[int] 						=						bbox_loss_coefficient
           _lowercase			:				Optional[int] 						=						giou_loss_coefficient
           _lowercase			:				Tuple 						=						eos_coefficient
           _lowercase			:				List[str] 						=						focal_alpha
           _lowercase			:				Dict 						=						disable_custom_kernels
           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)			->  int:




           """simple docstring"""

           _lowercase			:				Tuple 						=						copy.deepcopy(self.__dict__)
           if self.backbone_config is not None:
               _lowercase			:				str 						=						self.backbone_config.to_dict()
           _lowercase			:				List[str] 						=						self.__class__.model_type
           return output






from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands



def 							UpperCamelCase_( )       ->		List[Any]:
    _lowercase			:				int 						=						ArgumentParser('Transformers CLI tool'				,					usage='transformers-cli <command> [<args>]'					)
    _lowercase			:				Optional[Any] 						=						parser.add_subparsers(help='transformers-cli command helpers'					)

    # Register commands
    ConvertCommand.register_subcommand(lowerCamelCase_					)
    DownloadCommand.register_subcommand(lowerCamelCase_					)
    EnvironmentCommand.register_subcommand(lowerCamelCase_					)
    RunCommand.register_subcommand(lowerCamelCase_					)
    ServeCommand.register_subcommand(lowerCamelCase_					)
    UserCommands.register_subcommand(lowerCamelCase_					)
    AddNewModelCommand.register_subcommand(lowerCamelCase_					)
    AddNewModelLikeCommand.register_subcommand(lowerCamelCase_					)
    LfsCommands.register_subcommand(lowerCamelCase_					)
    PTtoTFCommand.register_subcommand(lowerCamelCase_					)

    # Let's go
    _lowercase			:				Any 						=						parser.parse_args()

    if not hasattr(lowerCamelCase_				,					'func'					):
        parser.print_help()
        exit(1					)

    # Run
    _lowercase			:				Optional[int] 						=						args.func(lowerCamelCase_					)
    service.run()


if __name__ == "__main__":
 main()


