
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		     =    logging.get_logger(__name__)

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

class 						lowercase_				(				__lowerCamelCase ):
 A__  : List[Any]      					=			"table-transformer"
 A__  : List[Any]      					=			["past_key_values"]
 A__  : Tuple      					=			{
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
 }

 def __init__(					self   ,		__UpperCamelCase=True   ,		__UpperCamelCase=None   ,		__UpperCamelCase=3   ,		__UpperCamelCase=1_0_0   ,		__UpperCamelCase=6   ,		__UpperCamelCase=2_0_4_8   ,		__UpperCamelCase=8   ,		__UpperCamelCase=6   ,		__UpperCamelCase=2_0_4_8   ,		__UpperCamelCase=8   ,		__UpperCamelCase=0.0   ,		__UpperCamelCase=0.0   ,		__UpperCamelCase=True   ,		__UpperCamelCase="relu"   ,		__UpperCamelCase=2_5_6   ,		__UpperCamelCase=0.1   ,		__UpperCamelCase=0.0   ,		__UpperCamelCase=0.0   ,		__UpperCamelCase=0.02   ,		__UpperCamelCase=1.0   ,		__UpperCamelCase=False   ,		__UpperCamelCase="sine"   ,		__UpperCamelCase="resnet50"   ,		__UpperCamelCase=True   ,		__UpperCamelCase=False   ,		__UpperCamelCase=1   ,		__UpperCamelCase=5   ,		__UpperCamelCase=2   ,		__UpperCamelCase=1   ,		__UpperCamelCase=1   ,		__UpperCamelCase=5   ,		__UpperCamelCase=2   ,		__UpperCamelCase=0.1   ,		**__UpperCamelCase   ,		):




        """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_											=				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 lowerCamelCase_ (					self   ):




        """simple docstring"""


        return self.encoder_attention_heads

 @property
 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        return self.d_model



class 						lowercase_				(				__lowerCamelCase ):
 A__  : Tuple      					=			version.parse("""1.11""" )

 @property
 def lowerCamelCase_ (					self   ):




        """simple docstring"""


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

 @property
 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        return 1e-5

 @property
 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        return 1_2










import random




def 		lowerCAmelCase_    (  __UpperCAmelCase:			list		,   __UpperCAmelCase:			Optional[int]      )      ->	tuple:
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				List[Any]          =      [], [], []
    for element in data:
        if element < pivot:
            less.append(__UpperCAmelCase      )
        elif element > pivot:
            greater.append(__UpperCAmelCase      )
        else:
            equal.append(__UpperCAmelCase      )
    return less, equal, greater





def 		lowerCAmelCase_    (  __UpperCAmelCase:			list		,   __UpperCAmelCase:			int      )      ->	List[str]:
    # index = len(items) // 2 when trying to find the median
    #   (value of index when items is sorted)

    # invalid input
    if index >= len(__UpperCAmelCase      ) or index < 0:
        return None

    UpperCamelCase__							:				List[str]          =      items[random.randint(0		,   len(__UpperCAmelCase      ) - 1      )]
    UpperCamelCase__							:				List[Any]          =      0
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				int          =      _partition(__UpperCAmelCase		,   __UpperCAmelCase      )
    UpperCamelCase__							:				Union[str, Any]          =      len(__UpperCAmelCase      )
    UpperCamelCase__							:				Dict          =      len(__UpperCAmelCase      )

    # index is the pivot
    if m <= index < m + count:
        return pivot
    # must be in smaller
    elif m > index:
        return quick_select(__UpperCAmelCase		,   __UpperCAmelCase      )
    # must be in larger
    else:
        return quick_select(__UpperCAmelCase		,   index - (m + count)      )

