


"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A	:  List[str]            =     logging.get_logger(__name__)

A	:  Optional[int]            =     {
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Union[str, Any]							 ="""deta"""
							__UpperCAmelCase		:       str							 ={
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """encoder_attention_heads""",
							}

							def __init__(	self       ,  __a=None       ,  __a=9_00       ,  __a=20_48       ,  __a=6       ,  __a=20_48       ,  __a=8       ,  __a=6       ,  __a=10_24       ,  __a=8       ,  __a=0.0       ,  __a=True       ,  __a="relu"       ,  __a=2_56       ,  __a=0.1       ,  __a=0.0       ,  __a=0.0       ,  __a=0.0_2       ,  __a=1.0       ,  __a=True       ,  __a=False       ,  __a="sine"       ,  __a=5       ,  __a=4       ,  __a=4       ,  __a=True       ,  __a=3_00       ,  __a=True       ,  __a=True       ,  __a=1       ,  __a=5       ,  __a=2       ,  __a=1       ,  __a=1       ,  __a=5       ,  __a=2       ,  __a=0.1       ,  __a=0.2_5       ,  **__a       ,  ):
										if backbone_config is None:
													logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."      )
													__lowerCAmelCase							       =     CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]      )
										else:
													if isinstance(__a       ,  __a      ):
																__lowerCAmelCase							       =     backbone_config.pop("model_type"      )
																__lowerCAmelCase							       =     CONFIG_MAPPING[backbone_model_type]
																__lowerCAmelCase							       =     config_class.from_dict(__a      )

										__lowerCAmelCase							       =     backbone_config
										__lowerCAmelCase							       =     num_queries
										__lowerCAmelCase							       =     max_position_embeddings
										__lowerCAmelCase							       =     d_model
										__lowerCAmelCase							       =     encoder_ffn_dim
										__lowerCAmelCase							       =     encoder_layers
										__lowerCAmelCase							       =     encoder_attention_heads
										__lowerCAmelCase							       =     decoder_ffn_dim
										__lowerCAmelCase							       =     decoder_layers
										__lowerCAmelCase							       =     decoder_attention_heads
										__lowerCAmelCase							       =     dropout
										__lowerCAmelCase							       =     attention_dropout
										__lowerCAmelCase							       =     activation_dropout
										__lowerCAmelCase							       =     activation_function
										__lowerCAmelCase							       =     init_std
										__lowerCAmelCase							       =     init_xavier_std
										__lowerCAmelCase							       =     encoder_layerdrop
										__lowerCAmelCase							       =     auxiliary_loss
										__lowerCAmelCase							       =     position_embedding_type
										# deformable attributes
										__lowerCAmelCase							       =     num_feature_levels
										__lowerCAmelCase							       =     encoder_n_points
										__lowerCAmelCase							       =     decoder_n_points
										__lowerCAmelCase							       =     two_stage
										__lowerCAmelCase							       =     two_stage_num_proposals
										__lowerCAmelCase							       =     with_box_refine
										__lowerCAmelCase							       =     assign_first_stage
										if two_stage is True and with_box_refine is False:
													raise ValueError("If two_stage is True, with_box_refine must be True."      )
										# Hungarian matcher
										__lowerCAmelCase							       =     class_cost
										__lowerCAmelCase							       =     bbox_cost
										__lowerCAmelCase							       =     giou_cost
										# Loss coefficients
										__lowerCAmelCase							       =     mask_loss_coefficient
										__lowerCAmelCase							       =     dice_loss_coefficient
										__lowerCAmelCase							       =     bbox_loss_coefficient
										__lowerCAmelCase							       =     giou_loss_coefficient
										__lowerCAmelCase							       =     eos_coefficient
										__lowerCAmelCase							       =     focal_alpha
										super().__init__(is_encoder_decoder=__a       ,  **__a      )

							@property
							def      snake_case							(	self      ):
										return self.encoder_attention_heads

							@property
							def      snake_case							(	self      ):
										return self.d_model



							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     copy.deepcopy(self.__dict__      )
										__lowerCAmelCase							       =     self.backbone_config.to_dict()
										__lowerCAmelCase							       =     self.__class__.model_type
										return output








"""simple docstring"""


from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




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


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




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

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

			if dataset.dtype != value_array.dtype:
						__lowerCAmelCase							       =     (
						    "Input data have different datatype... "
						    f"dataset : {dataset.dtype}, value_array : {value_array.dtype}"
						)
						raise TypeError(_UpperCamelCase )

			__lowerCAmelCase							       =     []

			for value in value_array:
						__lowerCAmelCase							       =     euclidean(_UpperCamelCase       ,					dataset[0] )
						__lowerCAmelCase							       =     dataset[0].tolist()

						for dataset_value in dataset[1:]:
									__lowerCAmelCase							       =     euclidean(_UpperCamelCase       ,					_UpperCamelCase )

									if dist > temp_dist:
												__lowerCAmelCase							       =     temp_dist
												__lowerCAmelCase							       =     dataset_value.tolist()

						answer.append([vector, dist] )

			return answer


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




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


if __name__ == "__main__":
	import doctest

	doctest.testmod()




