





'''simple docstring'''




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


lowercase__       : int         =       logging.get_logger(__name__)

lowercase__       : Union[str, Any]         =       {
    '''microsoft/table-transformer-detection''': (
        '''https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'''
    ),
}


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


     def __init__(		self		,      _UpperCAmelCase=True		,      _UpperCAmelCase=None		,      _UpperCAmelCase=3		,      _UpperCAmelCase=100		,      _UpperCAmelCase=6		,      _UpperCAmelCase=2048		,      _UpperCAmelCase=8		,      _UpperCAmelCase=6		,      _UpperCAmelCase=2048		,      _UpperCAmelCase=8		,      _UpperCAmelCase=0.0		,      _UpperCAmelCase=0.0		,      _UpperCAmelCase=True		,      _UpperCAmelCase="relu"		,      _UpperCAmelCase=256		,      _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.')
                    __A		:					int						=			CONFIG_MAPPING['resnet'](out_features=['stage4'])
               elif isinstance(_UpperCAmelCase		,      _UpperCAmelCase):
                    __A		:					Any						=			backbone_config.get('model_type')
                    __A		:					int						=			CONFIG_MAPPING[backbone_model_type]
                    __A		:					str						=			config_class.from_dict(_UpperCAmelCase)
               # set timm attributes to None
               __A					,__A					,__A		:					Optional[Any]						=			None, None, None

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


     @property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return self.encoder_attention_heads


     @property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return self.d_model




class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			version.parse('''1.11'''				)


     @property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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


     @property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return 1e-5


     @property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return 12






'''simple docstring'''




import math
import sys






def 			_lowerCAmelCase     (    __snake_case    :					int     )	->	int:
     if number != int(__snake_case     ):
          raise ValueError('the value of input must be a natural number'     )
     if number < 0:
          raise ValueError('the value of input must not be a negative number'     )
     if number == 0:
          return 1
     __A		:					str						=			[-1] * (number + 1)
     __A		:					Dict						=			0
     for i in range(1					,				number + 1     ):
          __A		:					int						=			sys.maxsize
          __A		:					int						=			int(math.sqrt(__snake_case     )     )
          for j in range(1					,				root + 1     ):
               __A		:					str						=			1 + answers[i - (j**2)]
               __A		:					Dict						=			min(__snake_case					,				__snake_case     )
          __A		:					Union[str, Any]						=			answer
     return answers[number]


if __name__ == "__main__":
  import doctest

  doctest.testmod()