
from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						torch.Tensor:
			# The first operation in a checkpoint can't be in-place, but it's
			# nice to have in-place addition during inference. Thus...
			if not inplace:
						lowerCAmelCase__ : Tuple   =						ma + ma
			else:
						ma += ma

			return ma
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						torch.Tensor:
			lowerCAmelCase__ : List[str]   =						-1 * len(SCREAMING_SNAKE_CASE_	)
			lowerCAmelCase__ : Union[str, Any]   =						list(range(len(tensor.shape[:zero_index]	)	)	)
			return tensor.permute(first_inds + [zero_index + i for i in inds]	)
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						torch.Tensor:
			return t.reshape(t.shape[:-no_dims] + (-1,)	)
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_ = 1e-4	)							->						torch.Tensor:
			lowerCAmelCase__ : str   =						mask.expand(*value.shape	)
			return torch.sum(mask * value			,				dim=SCREAMING_SNAKE_CASE_	) / (eps + torch.sum(SCREAMING_SNAKE_CASE_			,				dim=SCREAMING_SNAKE_CASE_	))
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_ = 2.3125			,				SCREAMING_SNAKE_CASE_ = 21.6875			,				SCREAMING_SNAKE_CASE_ = 64	)							->						torch.Tensor:
			lowerCAmelCase__ : Tuple   =						torch.linspace(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				no_bins - 1			,				device=pts.device	)
			lowerCAmelCase__ : Optional[int]   =						torch.sqrt(torch.sum((pts.unsqueeze(-2	) - pts.unsqueeze(-3	)) ** 2			,				dim=-1	)	)
			return torch.bucketize(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						dict:
			lowerCAmelCase__ : List[Any]   =						dicts[0]
			lowerCAmelCase__ : Tuple   =						{}
			for k, v in first.items():
						lowerCAmelCase__ : int   =						[d[k] for d in dicts]
						if isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	):
									lowerCAmelCase__ : Tuple   =						dict_multimap(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)
						else:
									lowerCAmelCase__ : Any   =						fn(SCREAMING_SNAKE_CASE_	)

			return new_dict
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						torch.Tensor:
			lowerCAmelCase__ : Optional[int]   =						v_bins.view(((1,) * len(x.shape	)) + (len(SCREAMING_SNAKE_CASE_	),)	)
			lowerCAmelCase__ : Optional[int]   =						x[..., None] - reshaped_bins
			lowerCAmelCase__ : List[str]   =						torch.argmin(torch.abs(SCREAMING_SNAKE_CASE_	)			,				dim=-1	)
			return nn.functional.one_hot(SCREAMING_SNAKE_CASE_			,				num_classes=len(SCREAMING_SNAKE_CASE_	)	).float()
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_ = 0			,				SCREAMING_SNAKE_CASE_ = 0	)							->						torch.Tensor:
			lowerCAmelCase__ : List[Union[slice, torch.Tensor]]   =						[]
			for i, s in enumerate(data.shape[:no_batch_dims]	):
						lowerCAmelCase__ : Dict   =						torch.arange(SCREAMING_SNAKE_CASE_	)
						lowerCAmelCase__ : Union[str, Any]   =						r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape	) - i - 1)))	)
						ranges.append(SCREAMING_SNAKE_CASE_	)

			lowerCAmelCase__ : List[Union[slice, torch.Tensor]]   =						[slice(SCREAMING_SNAKE_CASE_	) for _ in range(len(data.shape	) - no_batch_dims	)]
			lowerCAmelCase__ : Any   =						inds
			ranges.extend(SCREAMING_SNAKE_CASE_	)
			# Matt note: Editing this to get around the behaviour of using a list as an array index changing
			# in recent Numpy versions
			return data[tuple(SCREAMING_SNAKE_CASE_	)]


lowerCamelCase__        =						TypeVar("""T""")
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						Dict[Any, Union[dict, list, tuple, Any]]:
			lowerCAmelCase__ : Dict[Any, Union[dict, list, tuple, Any]]   =						{}
			for k, v in dic.items():
						if isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	):
									lowerCAmelCase__ : Tuple   =						dict_map(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)
						else:
									lowerCAmelCase__ : Optional[int]   =						tree_map(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)

			return new_dict
@overload
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						Any:
			...
@overload
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						dict:
			...
@overload
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						list:
			...
@overload
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						tuple:
			...
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						List[Any]:
			if isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	):
						return dict_map(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)
			elif isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	):
						return [tree_map(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	) for x in tree]
			elif isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	):
						return tuple(tree_map(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	) for x in tree	)
			elif isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	):
						return fn(SCREAMING_SNAKE_CASE_	)
			else:
						print(type(SCREAMING_SNAKE_CASE_	)	)
						raise ValueError('Not supported'	)







lowerCamelCase__        =						partial(tree_map, leaf_type=torch.Tensor)

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
lowerCamelCase__        =						load_iris()
iris.keys()


print(F"""Target names: \n {iris.target_names} """)
print(F"""\n Features: \n {iris.feature_names}""")

# Train set e Test set
lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__        =						train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

lowerCamelCase__        =						KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
lowerCamelCase__        =						[[1, 2, 1, 4], [2, 3, 4, 5]]

lowerCamelCase__        =						knn.predict(X_new)

print(
    F"""\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"""
    F""" {iris["target_names"][prediction]}"""
)