




import copy

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


snake_case_ : Optional[int]    		=       logging.get_logger(__name__)

snake_case_ : Dict    		=       {
    "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 __snake_case			(			a		):
   UpperCAmelCase__  :    Any          =  '''deformable_detr'''
   UpperCAmelCase__  :    List[Any]          =  {
       '''hidden_size''': '''d_model''',
       '''num_attention_heads''': '''encoder_attention_heads''',
   }







   def __init__(      self		:						Optional[Any]					,						_snake_case		:						List[Any]=True					,						_snake_case		:						Tuple=None					,						_snake_case		:						Dict=3					,						_snake_case		:						Union[str, Any]=300					,						_snake_case		:						str=1024					,						_snake_case		:						Optional[Any]=6					,						_snake_case		:						Optional[Any]=1024					,						_snake_case		:						Optional[Any]=8					,						_snake_case		:						str=6					,						_snake_case		:						Any=1024					,						_snake_case		:						Union[str, Any]=8					,						_snake_case		:						Tuple=0.0					,						_snake_case		:						List[Any]=True					,						_snake_case		:						Optional[int]="relu"					,						_snake_case		:						Dict=256					,						_snake_case		:						Dict=0.1					,						_snake_case		:						Any=0.0					,						_snake_case		:						Optional[Any]=0.0					,						_snake_case		:						Tuple=0.0_2					,						_snake_case		:						Optional[int]=1.0					,						_snake_case		:						List[Any]=True					,						_snake_case		:						Optional[int]=False					,						_snake_case		:						Optional[int]="sine"					,						_snake_case		:						Any="resnet50"					,						_snake_case		:						Dict=True					,						_snake_case		:						Any=False					,						_snake_case		:						List[str]=4					,						_snake_case		:						Any=4					,						_snake_case		:						int=4					,						_snake_case		:						int=False					,						_snake_case		:						int=300					,						_snake_case		:						str=False					,						_snake_case		:						Union[str, Any]=1					,						_snake_case		:						str=5					,						_snake_case		:						Optional[int]=2					,						_snake_case		:						Optional[Any]=1					,						_snake_case		:						Dict=1					,						_snake_case		:						Optional[Any]=5					,						_snake_case		:						List[str]=2					,						_snake_case		:						Tuple=0.1					,						_snake_case		:						List[Any]=0.2_5					,						_snake_case		:						Any=False					,						**_snake_case		:						Optional[int]					,						):

        """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.''')
                  UpperCAmelCase_          =							CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
             elif isinstance(_snake_case					,						_snake_case):
                  UpperCAmelCase_          =							backbone_config.get('''model_type''')
                  UpperCAmelCase_          =							CONFIG_MAPPING[backbone_model_type]
                  UpperCAmelCase_          =							config_class.from_dict(_snake_case)
        UpperCAmelCase_          =							use_timm_backbone
        UpperCAmelCase_          =							backbone_config
        UpperCAmelCase_          =							num_channels
        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
        UpperCAmelCase_          =							backbone
        UpperCAmelCase_          =							use_pretrained_backbone
        UpperCAmelCase_          =							dilation
        # 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
        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
        UpperCAmelCase_          =							disable_custom_kernels
        super().__init__(is_encoder_decoder=_snake_case					,						**_snake_case)







   @property
   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        return self.encoder_attention_heads







   @property
   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        return self.d_model







   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        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










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase__      				=   {
    '''configuration_pix2struct''': [
        '''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Pix2StructConfig''',
        '''Pix2StructTextConfig''',
        '''Pix2StructVisionConfig''',
    ],
    '''processing_pix2struct''': ['''Pix2StructProcessor'''],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase__      				=   ['''Pix2StructImageProcessor''']


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase__      				=   [
         '''PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''Pix2StructPreTrainedModel''',
         '''Pix2StructForConditionalGeneration''',
         '''Pix2StructVisionModel''',
         '''Pix2StructTextModel''',
     ]

if TYPE_CHECKING:
     from .configuration_pixastruct import (
         PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         PixaStructConfig,
         PixaStructTextConfig,
         PixaStructVisionConfig,
     )
     from .processing_pixastruct import PixaStructProcessor

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .image_processing_pixastruct import PixaStructImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_pixastruct import (
              PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
              PixaStructForConditionalGeneration,
              PixaStructPreTrainedModel,
              PixaStructTextModel,
              PixaStructVisionModel,
          )

else:
     import sys

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





