


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


_snake_case             =     logging.get_logger(__name__)

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

   '''simple docstring'''




   __A :      Any     		=		"table-transformer"
   __A :      int     		=		["past_key_values"]
   __A :      List[str]     		=		{
       "hidden_size": "d_model",
       "num_attention_heads": "encoder_attention_heads",
   }






   def __init__(		self						,				__A=True						,				__A=None						,				__A=3						,				__A=100						,				__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=1						,				__A=5						,				__A=2						,				__A=1						,				__A=1						,				__A=5						,				__A=2						,				__A=0.1						,				**__A						,				):






     """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." )
         lowerCamelCase :					Any    =				CONFIG_MAPPING["resnet"](out_features=["stage4"] )
       elif isinstance(__A						,				__A ):
         lowerCamelCase :					Optional[Any]    =				backbone_config.get("model_type" )
         lowerCamelCase :					Optional[Any]    =				CONFIG_MAPPING[backbone_model_type]
         lowerCamelCase :					Tuple    =				config_class.from_dict(__A )
       # set timm attributes to None
       lowerCamelCase    ,	lowerCamelCase    ,	lowerCamelCase :					int    =				None, None, None

     lowerCamelCase :					List[str]    =				use_timm_backbone
     lowerCamelCase :					Optional[Any]    =				backbone_config
     lowerCamelCase :					Optional[int]    =				num_channels
     lowerCamelCase :					Dict    =				num_queries
     lowerCamelCase :					int    =				d_model
     lowerCamelCase :					Optional[int]    =				encoder_ffn_dim
     lowerCamelCase :					Dict    =				encoder_layers
     lowerCamelCase :					Optional[Any]    =				encoder_attention_heads
     lowerCamelCase :					Tuple    =				decoder_ffn_dim
     lowerCamelCase :					Any    =				decoder_layers
     lowerCamelCase :					Union[str, Any]    =				decoder_attention_heads
     lowerCamelCase :					str    =				dropout
     lowerCamelCase :					Tuple    =				attention_dropout
     lowerCamelCase :					Optional[Any]    =				activation_dropout
     lowerCamelCase :					Union[str, Any]    =				activation_function
     lowerCamelCase :					Any    =				init_std
     lowerCamelCase :					str    =				init_xavier_std
     lowerCamelCase :					Any    =				encoder_layerdrop
     lowerCamelCase :					Optional[int]    =				decoder_layerdrop
     lowerCamelCase :					int    =				encoder_layers
     lowerCamelCase :					int    =				auxiliary_loss
     lowerCamelCase :					Optional[int]    =				position_embedding_type
     lowerCamelCase :					List[str]    =				backbone
     lowerCamelCase :					Union[str, Any]    =				use_pretrained_backbone
     lowerCamelCase :					List[str]    =				dilation
     # Hungarian matcher
     lowerCamelCase :					Tuple    =				class_cost
     lowerCamelCase :					List[Any]    =				bbox_cost
     lowerCamelCase :					int    =				giou_cost
     # Loss coefficients
     lowerCamelCase :					Optional[Any]    =				mask_loss_coefficient
     lowerCamelCase :					List[str]    =				dice_loss_coefficient
     lowerCamelCase :					Optional[Any]    =				bbox_loss_coefficient
     lowerCamelCase :					List[Any]    =				giou_loss_coefficient
     lowerCamelCase :					Any    =				eos_coefficient
     super().__init__(is_encoder_decoder=__A						,				**__A )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     return self.encoder_attention_heads






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     return self.d_model



class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      int     		=		version.parse("1.11"    )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





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






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     return 1e-5






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     return 12







import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Optional[int]     		=		"M-CLIP"






   def __init__(		self						,				__A=1024						,				__A=768						,				**__A ):






     """simple docstring"""





     lowerCamelCase :					str    =				transformerDimSize
     lowerCamelCase :					Any    =				imageDimSize
     super().__init__(**__A )



class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Tuple     		=		MCLIPConfig






   def __init__(		self						,				__A						,				*__A						,				**__A ):






     """simple docstring"""





     super().__init__(__A						,				*__A						,				**__A )
     lowerCamelCase :					Tuple    =				XLMRobertaModel(__A )
     lowerCamelCase :					Optional[Any]    =				torch.nn.Linear(
         in_features=config.transformerDimensions						,				out_features=config.numDims )






   def _snake_case    (		self						,				__A						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Any    =				self.transformer(input_ids=__A						,				attention_mask=__A )[0]
     lowerCamelCase :					int    =				(embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
     return self.LinearTransformation(__A ), embs



