





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

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






class        snake_case_    (		a__						):
  A_      							=   'table-transformer'
  A_      							=   ['past_key_values']
  A_      							=   {
      'hidden_size': 'd_model',
      'num_attention_heads': 'encoder_attention_heads',
  }






  def __init__(							self			:					Any       ,  _snake_case			:					Union[str, Any]=True       ,  _snake_case			:					Dict=None       ,  _snake_case			:					int=3       ,  _snake_case			:					Any=100       ,  _snake_case			:					List[Any]=6       ,  _snake_case			:					Tuple=2048       ,  _snake_case			:					Any=8       ,  _snake_case			:					Dict=6       ,  _snake_case			:					Tuple=2048       ,  _snake_case			:					int=8       ,  _snake_case			:					Optional[int]=0.0       ,  _snake_case			:					List[Any]=0.0       ,  _snake_case			:					List[Any]=True       ,  _snake_case			:					Optional[int]="relu"       ,  _snake_case			:					Union[str, Any]=256       ,  _snake_case			:					Any=0.1       ,  _snake_case			:					Tuple=0.0       ,  _snake_case			:					Optional[int]=0.0       ,  _snake_case			:					str=0.02       ,  _snake_case			:					Tuple=1.0       ,  _snake_case			:					Dict=False       ,  _snake_case			:					str="sine"       ,  _snake_case			:					str="resnet50"       ,  _snake_case			:					Any=True       ,  _snake_case			:					List[str]=False       ,  _snake_case			:					Any=1       ,  _snake_case			:					int=5       ,  _snake_case			:					Tuple=2       ,  _snake_case			:					Optional[int]=1       ,  _snake_case			:					Any=1       ,  _snake_case			:					Dict=5       ,  _snake_case			:					str=2       ,  _snake_case			:					Union[str, Any]=0.1       ,  **_snake_case			:					int       ,  )->int:






   '''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					:	int      =       CONFIG_MAPPING['''resnet'''](out_features=["""stage4"""]      )
    elif isinstance(_lowerCamelCase       ,  _lowerCamelCase      ):
     __lowerCAmelCase					:	str      =       backbone_config.get("""model_type"""      )
     __lowerCAmelCase					:	Optional[int]      =       CONFIG_MAPPING[backbone_model_type]
     __lowerCAmelCase					:	List[str]      =       config_class.from_dict(_lowerCamelCase      )
    # set timm attributes to None
    __lowerCAmelCase					:	Union[str, Any]      =       None, None, None

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






  @property
  def 				UpperCAmelCase__						(							self			:					List[Any]      )->Union[str, Any]:






   '''simple docstring'''

   return self.encoder_attention_heads






  @property
  def 				UpperCAmelCase__						(							self			:					Any      )->Any:






   '''simple docstring'''

   return self.d_model






class        snake_case_    (		a__						):
  A_      							=   version.parse('1.11'						)






  @property
  def 				UpperCAmelCase__						(							self			:					Tuple      )->Tuple:






   '''simple docstring'''

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






  @property
  def 				UpperCAmelCase__						(							self			:					Optional[int]      )->List[str]:






   '''simple docstring'''

   return 1E-5






  @property
  def 				UpperCAmelCase__						(							self			:					str      )->Union[str, Any]:






   '''simple docstring'''

   return 12






import random






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:Dict   )      ->   tuple:
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	List[str]      =       [], [], []
 for element in data:
  if element < pivot:
   less.append(SCREAMING_SNAKE_CASE   )
  elif element > pivot:
   greater.append(SCREAMING_SNAKE_CASE   )
  else:
   equal.append(SCREAMING_SNAKE_CASE   )
 return less, equal, greater







def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:int   )      ->   Dict:
 # index = len(items) // 2 when trying to find the median
 #   (value of index when items is sorted)

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

 __lowerCAmelCase					:	Union[str, Any]      =       items[random.randint(0   ,   len(SCREAMING_SNAKE_CASE   ) - 1   )]
 __lowerCAmelCase					:	int      =       0
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	Tuple      =       _partition(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[int]      =       len(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	List[str]      =       len(SCREAMING_SNAKE_CASE   )

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