






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


__magic_name__:					int							=     logging.get_logger(__name__)

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





class 	snake_case__ (							lowerCamelCase__				):
	lowercase__						:					Optional[int]											=   '''table-transformer'''
	lowercase__						:					Dict											=   ['''past_key_values''']
	lowercase__						:					Optional[Any]											=   {
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''encoder_attention_heads''',
	}
	def __init__(    self      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=3      ,       lowerCAmelCase__=1_00      ,       lowerCAmelCase__=6      ,       lowerCAmelCase__=20_48      ,       lowerCAmelCase__=8      ,       lowerCAmelCase__=6      ,       lowerCAmelCase__=20_48      ,       lowerCAmelCase__=8      ,       lowerCAmelCase__=0.0      ,       lowerCAmelCase__=0.0      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__="relu"      ,       lowerCAmelCase__=2_56      ,       lowerCAmelCase__=0.1      ,       lowerCAmelCase__=0.0      ,       lowerCAmelCase__=0.0      ,       lowerCAmelCase__=0.0_2      ,       lowerCAmelCase__=1.0      ,       lowerCAmelCase__=False      ,       lowerCAmelCase__="sine"      ,       lowerCAmelCase__="resnet50"      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__=False      ,       lowerCAmelCase__=1      ,       lowerCAmelCase__=5      ,       lowerCAmelCase__=2      ,       lowerCAmelCase__=1      ,       lowerCAmelCase__=1      ,       lowerCAmelCase__=5      ,       lowerCAmelCase__=2      ,       lowerCAmelCase__=0.1      ,       **lowerCAmelCase__      ,       )							->				Union[str, Any]:




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

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







	@property
	def       __magic_name__    (    self			)							->				List[Any]:
							return self.d_model





class 	snake_case__ (							lowerCamelCase__				):
	lowercase__						:					int											=   version.parse('''1.11'''				)
	@property
	def       __magic_name__    (    self			)							->				Optional[int]:
							return OrderedDict(
							    [
							        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
							        ("""pixel_mask""", {0: """batch"""}),
							    ]			)
	@property
	def       __magic_name__    (    self			)							->				Optional[int]:
							return 1e-5







	@property
	def       __magic_name__    (    self			)							->				Dict:
							return 12








from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						return math.sqrt(sum(pow(a - b,   2			) for a, b in zip(_A,   _A			)			)			)

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						if dataset.ndim != value_array.ndim:
												__magic_name__      :       str       						= (
												    """Wrong input data's dimensions... """
												    f'dataset : {dataset.ndim}, value_array : {value_array.ndim}'
												)
												raise ValueError(_A			)

						try:
												if dataset.shape[1] != value_array.shape[1]:
																		__magic_name__      :       Optional[Any]       						= (
																		    """Wrong input data's shape... """
																		    f'dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'
																		)
																		raise ValueError(_A			)
						except IndexError:
												if dataset.ndim != value_array.ndim:
																		raise TypeError("""Wrong shape"""			)

						if dataset.dtype != value_array.dtype:
												__magic_name__      :       List[Any]       						= (
												    """Input data have different datatype... """
												    f'dataset : {dataset.dtype}, value_array : {value_array.dtype}'
												)
												raise TypeError(_A			)

						__magic_name__      :       Dict       						= []

						for value in value_array:
												__magic_name__      :       Tuple       						= euclidean(_A,   dataset[0]			)
												__magic_name__      :       Any       						= dataset[0].tolist()

												for dataset_value in dataset[1:]:
																		__magic_name__      :       Any       						= euclidean(_A,   _A			)

																		if dist > temp_dist:
																								__magic_name__      :       Dict       						= temp_dist
																								__magic_name__      :       Any       						= dataset_value.tolist()

												answer.append([vector, dist]			)

						return answer

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						return np.dot(_A,   _A			) / (norm(_A			) * norm(_A			))


if __name__ == "__main__":
		import doctest

		doctest.testmod()
