"""simple docstring"""




from collections import Counter

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

__lowerCamelCase											=  datasets.load_iris()

__lowerCamelCase											=  np.array(data["data"])
__lowerCamelCase											=  np.array(data["target"])
__lowerCamelCase											=  data["target_names"]

__lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase											=  train_test_split(X, y)






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__=5				):






							"""simple docstring"""





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






"""simple docstring"""




import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Union[str, Any]													=		'M-CLIP'





						def __init__(       self		,__UpperCAmelCase=10_24		,__UpperCAmelCase=7_68		,**__UpperCAmelCase     )					->				List[str]:
													A__	     =							transformerDimSize
													A__	     =							imageDimSize
													super().__init__(**__UpperCAmelCase     )






class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Union[str, Any]													=		MCLIPConfig





						def __init__(       self		,__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				List[Any]:
													super().__init__(__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase     )
													A__	     =							XLMRobertaModel(__UpperCAmelCase     )
													A__	     =							torch.nn.Linear(
													    in_features=config.transformerDimensions		,out_features=config.numDims     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Tuple:
													A__	     =							self.transformer(input_ids=__UpperCAmelCase		,attention_mask=__UpperCAmelCase     )[0]
													A__	     =							(embs * attention_mask.unsqueeze(2     )).sum(dim=1     ) / attention_mask.sum(dim=1     )[:, None]
													return self.LinearTransformation(__UpperCAmelCase     ), embs





