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						:			int     		=	logging.get_logger(__name__)

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





class    lowercase__		(  a__):
						UpperCamelCase_	=						"""table-transformer"""
						UpperCamelCase_	=						["""past_key_values"""]
						UpperCamelCase_	=						{
						    """hidden_size""": """d_model""",
						    """num_attention_heads""": """encoder_attention_heads""",
						}


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





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

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


						@property
						def 			__A			(			self      :							Any		):





												'''simple docstring'''





												return self.encoder_attention_heads



						@property
						def 			__A			(			self      :							Tuple		):





												'''simple docstring'''





												return self.d_model






class    lowercase__		(  a__):
						UpperCamelCase_	=						version.parse("""1.11""")


						@property
						def 			__A			(			self      :							Optional[int]		):





												'''simple docstring'''





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


						@property
						def 			__A			(			self      :							Any		):





												'''simple docstring'''





												return 1E-5



						@property
						def 			__A			(			self      :							Tuple		):





												'''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()