



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


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

_lowerCAmelCase	:   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   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	'segformer'
		def __init__(							self    ,a_=3    ,a_=4    ,a_=[2, 2, 2, 2]    ,a_=[8, 4, 2, 1]    ,a_=[32, 64, 160, 256]    ,a_=[7, 3, 3, 3]    ,a_=[4, 2, 2, 2]    ,a_=[1, 2, 5, 8]    ,a_=[4, 4, 4, 4]    ,a_="gelu"    ,a_=0.0    ,a_=0.0    ,a_=0.1    ,a_=0.02    ,a_=0.1    ,a_=1e-6    ,a_=256    ,a_=255    ,**a_    ,):







						"""simple docstring"""
						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_    ,)

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



class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	version.parse('1.11' )
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return OrderedDict(
						    [
						        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
						    ]						)
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return 1e-4



		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						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]))
