


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  :     Dict    					=    logging.get_logger(__name__)

A  :     str    					=    {
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}
class 						_UpperCamelCase						(     __lowerCAmelCase	):
							'''simple docstring'''







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

							def __init__(  self					,     __a=True					,     __a=None					,     __a=3					,     __a=1_00					,     __a=6					,     __a=20_48					,     __a=8					,     __a=6					,     __a=20_48					,     __a=8					,     __a=0.0					,     __a=0.0					,     __a=True					,     __a="relu"					,     __a=2_56					,     __a=0.1					,     __a=0.0					,     __a=0.0					,     __a=0.0_2					,     __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					,     ):
										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       					=    CONFIG_MAPPING["resnet"](out_features=["stage4"]			)
													elif isinstance(lowerCAmelCase_					,     lowerCAmelCase_			):
																__lowerCAmelCase       					=    backbone_config.get("model_type"			)
																__lowerCAmelCase       					=    CONFIG_MAPPING[backbone_model_type]
																__lowerCAmelCase       					=    config_class.from_dict(lowerCAmelCase_			)
													# set timm attributes to None
													__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    None, None, None

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

							@property
							def     snake_case       (  self			):
										return self.encoder_attention_heads



							@property
							def     snake_case       (  self			):
										return self.d_model
class 						_UpperCamelCase						(     __lowerCAmelCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					str          =version.parse("""1.11"""	)

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

							@property
							def     snake_case       (  self			):
										return 1e-5



							@property
							def     snake_case       (  self			):
										return 12








"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


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

A  :     List[str]    					=    {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}
class 						_UpperCamelCase						(     lowerCAmelCase__ ,lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					Any          ="""bit"""
							__UpperCAmelCase		:					Optional[int]          =["""preactivation""", """bottleneck"""]
							__UpperCAmelCase		:					List[str]          =["""SAME""", """VALID"""]

							def __init__(  self					,     __a=3					,     __a=64					,     __a=[2_56, 5_12, 10_24, 20_48]					,     __a=[3, 4, 6, 3]					,     __a="preactivation"					,     __a="relu"					,     __a=None					,     __a=32					,     __a=0.0					,     __a=False					,     __a=32					,     __a=1					,     __a=None					,     __a=None					,     **__a					,     ):
										super().__init__(**__a			)
										if layer_type not in self.layer_types:
													raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types			)}"			)
										if global_padding is not None:
													if global_padding.upper() in self.supported_padding:
																__lowerCAmelCase       					=    global_padding.upper()
													else:
																raise ValueError(f"Padding strategy {global_padding} not supported"			)
										__lowerCAmelCase       					=    num_channels
										__lowerCAmelCase       					=    embedding_size
										__lowerCAmelCase       					=    hidden_sizes
										__lowerCAmelCase       					=    depths
										__lowerCAmelCase       					=    layer_type
										__lowerCAmelCase       					=    hidden_act
										__lowerCAmelCase       					=    global_padding
										__lowerCAmelCase       					=    num_groups
										__lowerCAmelCase       					=    drop_path_rate
										__lowerCAmelCase       					=    embedding_dynamic_padding
										__lowerCAmelCase       					=    output_stride
										__lowerCAmelCase       					=    width_factor

										__lowerCAmelCase       					=    ["stem"] + [f"stage{idx}" for idx in range(1					,     len(__a			) + 1			)]
										__lowerCAmelCase					,			__lowerCAmelCase       					=    get_aligned_output_features_output_indices(
										    out_features=__a					,     out_indices=__a					,     stage_names=self.stage_names			)




