



import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowerCamelCase__	=       {
    "facebook/maskformer-swin-base-ade": (
        "https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowerCamelCase__	=       logging.get_logger(__name__)






class       SCREAMING_SNAKE_CASE	(							lowercase__    ):
							__lowerCamelCase		:		str			='maskformer'
							__lowerCamelCase		:		Dict			={'hidden_size': 'mask_feature_size'}
							__lowerCamelCase		:		Optional[Any]			=['resnet', 'swin']
							__lowerCamelCase		:		List[Any]			=['detr']





							def __init__(      self						:						Optional[int]					, __lowercase						:						int = 256					, __lowercase						:						int = 256					, __lowercase						:						float = 0.1					, __lowercase						:						bool = False					, __lowercase						:						Optional[Dict] = None					, __lowercase						:						Optional[Dict] = None					, __lowercase						:						float = 0.02					, __lowercase						:						float = 1.0					, __lowercase						:						float = 1.0					, __lowercase						:						float = 1.0					, __lowercase						:						float = 20.0					, __lowercase						:						Optional[bool] = None					, **__lowercase						:						Tuple					, ):

											'''simple docstring'''
											if backbone_config is None:
															# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
															__a        =						SwinConfig(
															    image_size=384					, in_channels=3					, patch_size=4					, embed_dim=128					, depths=[2, 2, 18, 2]					, num_heads=[4, 8, 16, 32]					, window_size=12					, drop_path_rate=0.3					, out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]					, )

											if isinstance(__lowercase					, __lowercase		):
															__a        =						backbone_config.pop("""model_type"""		)
															__a        =						CONFIG_MAPPING[backbone_model_type]
															__a        =						config_class.from_dict(__lowercase		)

											# verify that the backbone is supported
											if backbone_config.model_type not in self.backbones_supported:
															logger.warning_once(
															    F"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. "
															    F"Supported model types: {','.join(self.backbones_supported		)}"		)

											if decoder_config is None:
															# fall back to https://huggingface.co/facebook/detr-resnet-50
															__a        =						DetrConfig()
											else:
															# verify that the decoder is supported
															__a        =						(
															    decoder_config.pop("""model_type"""		) if isinstance(__lowercase					, __lowercase		) else decoder_config.model_type
															)
															if decoder_type not in self.decoders_supported:
																			raise ValueError(
																			    F"Transformer Decoder {decoder_type} not supported, please use one of"
																			    F" {','.join(self.decoders_supported		)}"		)
															if isinstance(__lowercase					, __lowercase		):
																			__a        =						CONFIG_MAPPING[decoder_type]
																			__a        =						config_class.from_dict(__lowercase		)

											__a        =						backbone_config
											__a        =						decoder_config
											# main feature dimension for the model
											__a        =						fpn_feature_size
											__a        =						mask_feature_size
											# initializer
											__a        =						init_std
											__a        =						init_xavier_std
											# Hungarian matcher && loss
											__a        =						cross_entropy_weight
											__a        =						dice_weight
											__a        =						mask_weight
											__a        =						use_auxiliary_loss
											__a        =						no_object_weight
											__a        =						output_auxiliary_logits

											__a        =						self.decoder_config.encoder_attention_heads
											__a        =						self.decoder_config.num_hidden_layers
											super().__init__(**__lowercase		)





							@classmethod
							def   UpperCamelCase_    (      cls						:						int					, __lowercase						:						PretrainedConfig					, __lowercase						:						PretrainedConfig					, **__lowercase						:						str		):

											'''simple docstring'''
											return cls(
											    backbone_config=__lowercase					, decoder_config=__lowercase					, **__lowercase					, )







							def   UpperCamelCase_    (      self						:						List[Any]		):

											'''simple docstring'''
											__a        =						copy.deepcopy(self.__dict__		)
											__a        =						self.backbone_config.to_dict()
											__a        =						self.decoder_config.to_dict()
											__a        =						self.__class__.model_type
											return output








from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

a		:       Dict			     = typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
a		:       Any			     = typing.Union[np.floataa, int, float]  # noqa: UP007






def 				lowerCamelCase__      (    __lowerCamelCase			:     Vector  ,		__lowerCamelCase			:     Vector		):
  return np.sqrt(np.sum((np.asarray(__lowerCamelCase		) - np.asarray(__lowerCamelCase		)) ** 2		)		)






def 				lowerCamelCase__      (    __lowerCamelCase			:     Vector  ,		__lowerCamelCase			:     Vector		):
  return sum((va - va) ** 2 for va, va in zip(__lowerCamelCase  ,		__lowerCamelCase		)		) ** (1 / 2)


if __name__ == "__main__":






       def 				lowerCamelCase__      (    ):
         from timeit import timeit

         print("""Without Numpy"""		)
         print(
             timeit(
                 """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"""  ,		number=10000  ,		globals=globals()  ,		)		)
         print("""With Numpy"""		)
         print(
             timeit(
                 """euclidean_distance([1, 2, 3], [4, 5, 6])"""  ,		number=10000  ,		globals=globals()  ,		)		)







       benchmark()


