


import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType






class       _UpperCamelCase    :

				"""simple docstring"""







				def __init__(						self				,   a__=2				,   a__=3				,   a__=64				,   a__=None   )	->				Optional[Any]:
					A								=				np.random.default_rng(a__   )
					A								=				length
					A								=				rng.normal(size=(length,)   ).astype(np.floataa   )
					A								=				a * self.x + b + rng.normal(scale=0.1				,   size=(length,)   ).astype(np.floataa   )

				def __len__(						self   )	->				Optional[Any]:
					return self.length

				def __getitem__(						self				,   a__   )	->				Optional[int]:
					return {"x": self.x[i], "y": self.y[i]}






class       _UpperCamelCase    (					torch.nn.Module				):

				"""simple docstring"""







				def __init__(						self				,   a__=0				,   a__=0				,   a__=False   )	->				List[Any]:
					super().__init__()
					A								=				torch.nn.Parameter(torch.tensor([2, 3]   ).float()   )
					A								=				torch.nn.Parameter(torch.tensor([2, 3]   ).float()   )
					A								=				True

				def      _UpperCAmelCase					(						self				,   a__=None   )	->				List[Any]:
					if self.first_batch:
						print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'   )
						A								=				False
					return x * self.a[0] + self.b[0]






class       _UpperCamelCase    (					torch.nn.Module				):

				"""simple docstring"""







				def __init__(						self				,   a__=0				,   a__=0				,   a__=False   )	->				List[str]:
					super().__init__()
					A								=				torch.nn.Parameter(torch.tensor(a__   ).float()   )
					A								=				torch.nn.Parameter(torch.tensor(a__   ).float()   )
					A								=				True

				def      _UpperCAmelCase					(						self				,   a__=None   )	->				int:
					if self.first_batch:
						print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'   )
						A								=				False
					return x * self.a + self.b





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[Any]							,					UpperCamelCase__:     int = 16						) ->   Any:





	"""simple docstring"""







	from datasets import load_dataset
	from transformers import AutoTokenizer

	A								=				AutoTokenizer.from_pretrained("""bert-base-cased"""						)
	A								=				{"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
	A								=				load_dataset("""csv"""							,					data_files=UpperCamelCase__						)
	A								=				datasets["""train"""].unique("""label"""						)

	A								=				{v: i for i, v in enumerate(UpperCamelCase__						)}

	def tokenize_function(UpperCamelCase__:     Optional[Any]						):
		# max_length=None => use the model max length (it's actually the default)
		A								=				tokenizer(
		    examples["""sentence1"""]							,					examples["""sentence2"""]							,					truncation=UpperCamelCase__							,					max_length=UpperCamelCase__							,					padding="""max_length"""						)
		if "label" in examples:
			A								=				[label_to_id[l] for l in examples["""label"""]]
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	A								=				datasets.map(
	    UpperCamelCase__							,					batched=UpperCamelCase__							,					remove_columns=["""sentence1""", """sentence2""", """label"""]							,					)

	def collate_fn(UpperCamelCase__:     str						):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		if accelerator.distributed_type == DistributedType.TPU:
			return tokenizer.pad(UpperCamelCase__							,					padding="""max_length"""							,					max_length=1_28							,					return_tensors="""pt"""						)
		return tokenizer.pad(UpperCamelCase__							,					padding="""longest"""							,					return_tensors="""pt"""						)

	# Instantiate dataloaders.
	A								=				DataLoader(tokenized_datasets["""train"""]							,					shuffle=UpperCamelCase__							,					collate_fn=UpperCamelCase__							,					batch_size=2						)
	A								=				DataLoader(tokenized_datasets["""validation"""]							,					shuffle=UpperCamelCase__							,					collate_fn=UpperCamelCase__							,					batch_size=1						)

	return train_dataloader, eval_dataloader







from collections import Counter

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

_lowercase       :      Any	=	datasets.load_iris()

_lowercase       :      Tuple	=	np.array(data["data"])
_lowercase       :      int	=	np.array(data["target"])
_lowercase       :      int	=	data["target_names"]

_lowercase	,							_lowercase	,							_lowercase	,							_lowercase       :      Any	=	train_test_split(X, y)





def   _lowerCAmelCase				(   UpperCamelCase__:     int							,					UpperCamelCase__:     Any						) ->   List[Any]:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     int							,					UpperCamelCase__:     List[Any]							,					UpperCamelCase__:     int=5						) ->   Tuple:





	"""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]))



