





'''simple docstring'''




import warnings
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


a__					:			Optional[int]          =     logging.get_logger(__name__)

a__					:			Optional[Any]          =     {
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}






class 				__snake_case     (					__magic_name__							):
	__lowerCAmelCase       						=  '''segformer'''





	def __init__(		self      ,       UpperCamelCase_=3      ,       UpperCamelCase_=4      ,       UpperCamelCase_=[2, 2, 2, 2]      ,       UpperCamelCase_=[8, 4, 2, 1]      ,       UpperCamelCase_=[32, 64, 160, 256]      ,       UpperCamelCase_=[7, 3, 3, 3]      ,       UpperCamelCase_=[4, 2, 2, 2]      ,       UpperCamelCase_=[1, 2, 5, 8]      ,       UpperCamelCase_=[4, 4, 4, 4]      ,       UpperCamelCase_="gelu"      ,       UpperCamelCase_=0.0      ,       UpperCamelCase_=0.0      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=0.0_2      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=1E-6      ,       UpperCamelCase_=256      ,       UpperCamelCase_=255      ,       **UpperCamelCase_      ,       )      ->		List[Any]:
		super().__init__(**a_		)

		if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
			warnings.warn(
			    'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
			    ' removed, as the behaviour will default to that of reshape_last_stage = True.'      ,       a_      ,       )

		snake_case__	       =   num_channels
		snake_case__	       =   num_encoder_blocks
		snake_case__	       =   depths
		snake_case__	       =   sr_ratios
		snake_case__	       =   hidden_sizes
		snake_case__	       =   patch_sizes
		snake_case__	       =   strides
		snake_case__	       =   mlp_ratios
		snake_case__	       =   num_attention_heads
		snake_case__	       =   hidden_act
		snake_case__	       =   hidden_dropout_prob
		snake_case__	       =   attention_probs_dropout_prob
		snake_case__	       =   classifier_dropout_prob
		snake_case__	       =   initializer_range
		snake_case__	       =   drop_path_rate
		snake_case__	       =   layer_norm_eps
		snake_case__	       =   decoder_hidden_size
		snake_case__	       =   kwargs.get('reshape_last_stage'      ,       a_		)
		snake_case__	       =   semantic_loss_ignore_index






class 				__snake_case     (					__magic_name__							):
	__lowerCAmelCase       						=  version.parse('''1.11'''							)





	@property
	def   _snake_case   (		self		)      ->		str:
		return OrderedDict(
		    [
		        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
		    ]		)





	@property
	def   _snake_case   (		self		)      ->		Any:
		return 1E-4





	@property
	def   _snake_case   (		self		)      ->		Any:
		return 12








from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_lowerCAmelCase	:   int    =   datasets.load_iris()

_lowerCAmelCase	:   Tuple    =   np.array(data["data"])
_lowerCAmelCase	:   Any    =   np.array(data["target"])
_lowerCAmelCase	:   Union[str, Any]    =   data["target_names"]

_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase	:   Optional[Any]    =   train_test_split(X, y)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Tuple:



				"""simple docstring"""





				return np.linalg.norm(np.array(snake_case__					) - np.array(snake_case__					)					)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__=5					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  zip(snake_case__    ,			snake_case__					)
				# List of distances of all points from the point to be classified
				lowerCAmelCase__		  =  []
				for data_point in data:
								lowerCAmelCase__		  =  euclidean_distance(data_point[0]    ,			snake_case__					)
								distances.append((distance, data_point[1])					)
				# Choosing 'k' points with the least distances.
				lowerCAmelCase__		  =  [i[1] for i in sorted(snake_case__					)[:k]]
				# Most commonly occurring class among them
				# is the class into which the point is classified
				lowerCAmelCase__		  =  Counter(snake_case__					).most_common(1					)[0][0]
				return classes[result]


if __name__ == "__main__":
	print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
