

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    :List[str]      =			logging.get_logger(__name__)

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

class   _a					(			snake_case_				):




      """simple docstring"""

      _lowerCamelCase       :			Dict      		=       'table-transformer'
      _lowerCamelCase       :			Any      		=       ['past_key_values']
      _lowerCamelCase       :			Optional[int]      		=       {
          'hidden_size': 'd_model',
          'num_attention_heads': 'encoder_attention_heads',
      }





      def __init__(   self							:				Dict						,			UpperCAmelCase							:				str=True						,			UpperCAmelCase							:				str=None						,			UpperCAmelCase							:				Optional[int]=3						,			UpperCAmelCase							:				List[str]=100						,			UpperCAmelCase							:				Union[str, Any]=6						,			UpperCAmelCase							:				Optional[int]=2048						,			UpperCAmelCase							:				Any=8						,			UpperCAmelCase							:				Union[str, Any]=6						,			UpperCAmelCase							:				str=2048						,			UpperCAmelCase							:				Dict=8						,			UpperCAmelCase							:				Tuple=0.0						,			UpperCAmelCase							:				Tuple=0.0						,			UpperCAmelCase							:				int=True						,			UpperCAmelCase							:				Any="relu"						,			UpperCAmelCase							:				int=256						,			UpperCAmelCase							:				int=0.1						,			UpperCAmelCase							:				Tuple=0.0						,			UpperCAmelCase							:				str=0.0						,			UpperCAmelCase							:				str=0.02						,			UpperCAmelCase							:				List[str]=1.0						,			UpperCAmelCase							:				Union[str, Any]=False						,			UpperCAmelCase							:				Dict="sine"						,			UpperCAmelCase							:				List[str]="resnet50"						,			UpperCAmelCase							:				str=True						,			UpperCAmelCase							:				Optional[Any]=False						,			UpperCAmelCase							:				Union[str, Any]=1						,			UpperCAmelCase							:				Union[str, Any]=5						,			UpperCAmelCase							:				List[Any]=2						,			UpperCAmelCase							:				Dict=1						,			UpperCAmelCase							:				Tuple=1						,			UpperCAmelCase							:				Tuple=5						,			UpperCAmelCase							:				Optional[Any]=2						,			UpperCAmelCase							:				Optional[int]=0.1						,			**UpperCAmelCase							:				int						,			):
            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."	)
                        A_						  =				CONFIG_MAPPING["resnet"](out_features=["stage4"]	)
                  elif isinstance(UpperCAmelCase						,			UpperCAmelCase	):
                        A_						  =				backbone_config.get("model_type"	)
                        A_						  =				CONFIG_MAPPING[backbone_model_type]
                        A_						  =				config_class.from_dict(UpperCAmelCase	)
                  # set timm attributes to None
                  A_  ,     A_  ,     A_						  =				None, None, None

            A_						  =				use_timm_backbone
            A_						  =				backbone_config
            A_						  =				num_channels
            A_						  =				num_queries
            A_						  =				d_model
            A_						  =				encoder_ffn_dim
            A_						  =				encoder_layers
            A_						  =				encoder_attention_heads
            A_						  =				decoder_ffn_dim
            A_						  =				decoder_layers
            A_						  =				decoder_attention_heads
            A_						  =				dropout
            A_						  =				attention_dropout
            A_						  =				activation_dropout
            A_						  =				activation_function
            A_						  =				init_std
            A_						  =				init_xavier_std
            A_						  =				encoder_layerdrop
            A_						  =				decoder_layerdrop
            A_						  =				encoder_layers
            A_						  =				auxiliary_loss
            A_						  =				position_embedding_type
            A_						  =				backbone
            A_						  =				use_pretrained_backbone
            A_						  =				dilation
            # Hungarian matcher
            A_						  =				class_cost
            A_						  =				bbox_cost
            A_						  =				giou_cost
            # Loss coefficients
            A_						  =				mask_loss_coefficient
            A_						  =				dice_loss_coefficient
            A_						  =				bbox_loss_coefficient
            A_						  =				giou_loss_coefficient
            A_						  =				eos_coefficient
            super().__init__(is_encoder_decoder=UpperCAmelCase						,			**UpperCAmelCase	)





      @property
      def 		__A	(   self							:				int	):
            return self.encoder_attention_heads







      @property
      def 		__A	(   self							:				int	):
            return self.d_model




class   _a					(			snake_case_				):




      """simple docstring"""

      _lowerCamelCase       :			Union[str, Any]      		=       version.parse('1.11'				)





      @property
      def 		__A	(   self							:				Any	):
            return OrderedDict(
                [
                    ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                    ("pixel_mask", {0: "batch"}),
                ]	)





      @property
      def 		__A	(   self							:				Any	):
            return 1E-5







      @property
      def 		__A	(   self							:				List[Any]	):
            return 12
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	{
    "configuration_bridgetower": [
        "BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BridgeTowerConfig",
        "BridgeTowerTextConfig",
        "BridgeTowerVisionConfig",
    ],
    "processing_bridgetower": ["BridgeTowerProcessor"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	SCREAMING_SNAKE_CASE		:     Optional[Any]         =	["BridgeTowerImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	SCREAMING_SNAKE_CASE		:     Tuple         =	[
	    "BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "BridgeTowerForContrastiveLearning",
	    "BridgeTowerForImageAndTextRetrieval",
	    "BridgeTowerForMaskedLM",
	    "BridgeTowerModel",
	    "BridgeTowerPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_bridgetower import (
	    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BridgeTowerConfig,
	    BridgeTowerTextConfig,
	    BridgeTowerVisionConfig,
	)
	from .processing_bridgetower import BridgeTowerProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_bridgetower import BridgeTowerImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_bridgetower import (
		    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BridgeTowerForContrastiveLearning,
		    BridgeTowerForImageAndTextRetrieval,
		    BridgeTowerForMaskedLM,
		    BridgeTowerModel,
		    BridgeTowerPreTrainedModel,
		)


else:
	import sys

	SCREAMING_SNAKE_CASE		:     List[Any]         =	_LazyModule(__name__, globals()["__file__"], _import_structure)






