
from collections import Counter

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

_A   =     datasets.load_iris()

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

_A,      _A,      _A,      _A   =     train_test_split(X, y)






def    lowerCamelCase__				(				__lowerCAmelCase  :  Union[str, Any]    ,							__lowerCAmelCase  :  Union[str, Any]				):




			"""simple docstring"""


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






def    lowerCamelCase__				(				__lowerCAmelCase  :  Union[str, Any]    ,							__lowerCAmelCase  :  Any    ,							__lowerCAmelCase  :  Optional[Any]    ,							__lowerCAmelCase  :  Any    ,							__lowerCAmelCase  :  Dict=5				):




			"""simple docstring"""


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





import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel





class 	_lowerCAmelCase					(	pl.LightningModule       ):




				def __init__(					self				,				_UpperCamelCase     )				->      List[str]:
							super().__init__()
							lowerCAmelCase_						=	model
							lowerCAmelCase_						=	2
							lowerCAmelCase_						=	nn.Linear(self.model.config.hidden_size				,				self.num_labels     )




				def      __a						(					self     )				->      Tuple:
							pass






def    lowerCamelCase__				(				__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  str				):




			"""simple docstring"""


			lowerCAmelCase_						=	LongformerModel.from_pretrained(__lowerCAmelCase				)
			lowerCAmelCase_						=	LightningModel(__lowerCAmelCase				)

			lowerCAmelCase_						=	torch.load(__lowerCAmelCase    ,							map_location=torch.device("cpu"				)				)
			lightning_model.load_state_dict(ckpt["state_dict"]				)

			# init longformer question answering model
			lowerCAmelCase_						=	LongformerForQuestionAnswering.from_pretrained(__lowerCAmelCase				)

			# transfer weights
			longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()				)
			longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()				)
			longformer_for_qa.eval()

			# save model
			longformer_for_qa.save_pretrained(__lowerCAmelCase				)

			print(F"""Conversion successful. Model saved under {pytorch_dump_folder_path}"""				)


if __name__ == "__main__":
			_A   =     argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--longformer_model",
			    default=None,
			    type=str,
			    required=True,
			    help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
			)
			parser.add_argument(
			    "--longformer_question_answering_ckpt_path",
			    default=None,
			    type=str,
			    required=True,
			    help="Path the official PyTorch Lightning Checkpoint.",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			_A   =     parser.parse_args()
			convert_longformer_qa_checkpoint_to_pytorch(
			    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
			)



