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


UpperCamelCase__    							= logging.get_logger(__name__)

UpperCamelCase__    							= {
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}





class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			List[Any]              =      'table-transformer'
   __UpperCAmelCase			:			Optional[Any]              =      ['past_key_values']
   __UpperCAmelCase			:			List[Any]              =      {
       'hidden_size': 'd_model',
       'num_attention_heads': 'encoder_attention_heads',
   }


   def __init__(self					:						Any	,  __UpperCAmelCase					:						str=True	,  __UpperCAmelCase					:						Any=None	,  __UpperCAmelCase					:						Optional[int]=3	,  __UpperCAmelCase					:						int=1_0_0	,  __UpperCAmelCase					:						List[Any]=6	,  __UpperCAmelCase					:						List[str]=2_0_4_8	,  __UpperCAmelCase					:						Union[str, Any]=8	,  __UpperCAmelCase					:						Optional[Any]=6	,  __UpperCAmelCase					:						List[Any]=2_0_4_8	,  __UpperCAmelCase					:						Union[str, Any]=8	,  __UpperCAmelCase					:						List[str]=0.0	,  __UpperCAmelCase					:						Union[str, Any]=0.0	,  __UpperCAmelCase					:						Any=True	,  __UpperCAmelCase					:						List[str]="relu"	,  __UpperCAmelCase					:						Optional[Any]=2_5_6	,  __UpperCAmelCase					:						List[Any]=0.1	,  __UpperCAmelCase					:						int=0.0	,  __UpperCAmelCase					:						Dict=0.0	,  __UpperCAmelCase					:						List[Any]=0.02	,  __UpperCAmelCase					:						Union[str, Any]=1.0	,  __UpperCAmelCase					:						Any=False	,  __UpperCAmelCase					:						Optional[int]="sine"	,  __UpperCAmelCase					:						int="resnet50"	,  __UpperCAmelCase					:						Any=True	,  __UpperCAmelCase					:						Optional[Any]=False	,  __UpperCAmelCase					:						List[Any]=1	,  __UpperCAmelCase					:						Optional[int]=5	,  __UpperCAmelCase					:						Optional[int]=2	,  __UpperCAmelCase					:						List[str]=1	,  __UpperCAmelCase					:						Optional[int]=1	,  __UpperCAmelCase					:						List[Any]=5	,  __UpperCAmelCase					:						Union[str, Any]=2	,  __UpperCAmelCase					:						Union[str, Any]=0.1	,  **__UpperCAmelCase					:						str	,  )						->    Any:
         """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(__UpperCAmelCase	,  __UpperCAmelCase		):
                     UpperCAmelCase__								=  backbone_config.get("model_type"		)
                     UpperCAmelCase__								=  CONFIG_MAPPING[backbone_model_type]
                     UpperCAmelCase__								=  config_class.from_dict(__UpperCAmelCase		)
               # set timm attributes to None
               UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__								=  None, None, None

         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__								=  bbox_loss_coefficient
         UpperCAmelCase__								=  giou_loss_coefficient
         UpperCAmelCase__								=  eos_coefficient
         super().__init__(is_encoder_decoder=__UpperCAmelCase	,  **__UpperCAmelCase		)


   @property
   def       lowercase_				(self					:						List[Any]		)						->    int:
         """simple docstring"""



         return self.encoder_attention_heads





   @property
   def       lowercase_				(self					:						Dict		)						->    int:
         """simple docstring"""



         return self.d_model





class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			Any              =      version.parse('1.11'					)


   @property
   def       lowercase_				(self					:						Tuple		)						->    Mapping[str, Mapping[int, str]]:
         """simple docstring"""



         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                 ("pixel_mask", {0: "batch"}),
             ]		)


   @property
   def       lowercase_				(self					:						Optional[int]		)						->    float:
         """simple docstring"""



         return 1E-5





   @property
   def       lowercase_				(self					:						Union[str, Any]		)						->    int:
         """simple docstring"""



         return 1_2




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
      from .camera import create_pan_cameras
      from .pipeline_shap_e import ShapEPipeline
      from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
      from .renderer import (
          BoundingBoxVolume,
          ImportanceRaySampler,
          MLPNeRFModelOutput,
          MLPNeRSTFModel,
          ShapEParamsProjModel,
          ShapERenderer,
          StratifiedRaySampler,
          VoidNeRFModel,
      )



