




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    snake_case   (				A		,  A		,  A     )				->				torch.Tensor:




	'''simple docstring'''


	if not inplace:
		lowerCamelCase_				:						int				  =				ma + ma
	else:
		ma += ma

	return ma






def    snake_case   (				A		,  A     )				->				torch.Tensor:




	'''simple docstring'''


	lowerCamelCase_				:						Union[str, Any]				  =				-1 * len(A     )
	lowerCamelCase_				:						List[Any]				  =				list(range(len(tensor.shape[:zero_index]     )     )     )
	return tensor.permute(first_inds + [zero_index + i for i in inds]     )






def    snake_case   (				A		,  A     )				->				torch.Tensor:




	'''simple docstring'''


	return t.reshape(t.shape[:-no_dims] + (-1,)     )






def    snake_case   (				A		,  A		,  A		,  A = 1e-4     )				->				torch.Tensor:




	'''simple docstring'''


	lowerCamelCase_				:						Optional[Any]				  =				mask.expand(*value.shape     )
	return torch.sum(mask * value		,  dim=A     ) / (eps + torch.sum(A		,  dim=A     ))






def    snake_case   (				A		,  A = 2.3125		,  A = 21.6875		,  A = 64     )				->				torch.Tensor:




	'''simple docstring'''


	lowerCamelCase_				:						Dict				  =				torch.linspace(A		,  A		,  no_bins - 1		,  device=pts.device     )
	lowerCamelCase_				:						str				  =				torch.sqrt(torch.sum((pts.unsqueeze(-2     ) - pts.unsqueeze(-3     )) ** 2		,  dim=-1     )     )
	return torch.bucketize(A		,  A     )






def    snake_case   (				A		,  A     )				->				dict:




	'''simple docstring'''


	lowerCamelCase_				:						Optional[int]				  =				dicts[0]
	lowerCamelCase_				:						Union[str, Any]				  =				{}
	for k, v in first.items():
		lowerCamelCase_				:						List[str]				  =				[d[k] for d in dicts]
		if isinstance(A		,  A     ):
			lowerCamelCase_				:						Union[str, Any]				  =				dict_multimap(A		,  A     )
		else:
			lowerCamelCase_				:						Any				  =				fn(A     )

	return new_dict






def    snake_case   (				A		,  A     )				->				torch.Tensor:




	'''simple docstring'''


	lowerCamelCase_				:						str				  =				v_bins.view(((1,) * len(x.shape     )) + (len(A     ),)     )
	lowerCamelCase_				:						int				  =				x[..., None] - reshaped_bins
	lowerCamelCase_				:						List[Any]				  =				torch.argmin(torch.abs(A     )		,  dim=-1     )
	return nn.functional.one_hot(A		,  num_classes=len(A     )     ).float()






def    snake_case   (				A		,  A		,  A = 0		,  A = 0     )				->				torch.Tensor:




	'''simple docstring'''


	lowerCamelCase_				:						List[Union[slice, torch.Tensor]]				  =				[]
	for i, s in enumerate(data.shape[:no_batch_dims]     ):
		lowerCamelCase_				:						Dict				  =				torch.arange(A     )
		lowerCamelCase_				:						int				  =				r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape     ) - i - 1)))     )
		ranges.append(A     )

	lowerCamelCase_				:						List[Union[slice, torch.Tensor]]				  =				[slice(A     ) for _ in range(len(data.shape     ) - no_batch_dims     )]
	lowerCamelCase_				:						Any				  =				inds
	ranges.extend(A     )
	# 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(A     )]


UpperCamelCase__						=		TypeVar('T')






def    snake_case   (				A		,  A		,  A     )				->				Dict[Any, Union[dict, list, tuple, Any]]:




	'''simple docstring'''


	lowerCamelCase_				:						Dict[Any, Union[dict, list, tuple, Any]]				  =				{}
	for k, v in dic.items():
		if isinstance(A		,  A     ):
			lowerCamelCase_				:						str				  =				dict_map(A		,  A		,  A     )
		else:
			lowerCamelCase_				:						int				  =				tree_map(A		,  A		,  A     )

	return new_dict






@overload
def    snake_case   (				A		,  A		,  A     )				->				Any:




	'''simple docstring'''


	...






@overload
def    snake_case   (				A		,  A		,  A     )				->				dict:




	'''simple docstring'''


	...






@overload
def    snake_case   (				A		,  A		,  A     )				->				list:




	'''simple docstring'''


	...






@overload
def    snake_case   (				A		,  A		,  A     )				->				tuple:




	'''simple docstring'''


	...






def    snake_case   (				A		,  A		,  A     )				->				Optional[int]:




	'''simple docstring'''


	if isinstance(A		,  A     ):
		return dict_map(A		,  A		,  A     )
	elif isinstance(A		,  A     ):
		return [tree_map(A		,  A		,  A     ) for x in tree]
	elif isinstance(A		,  A     ):
		return tuple(tree_map(A		,  A		,  A     ) for x in tree     )
	elif isinstance(A		,  A     ):
		return fn(A     )
	else:
		print(type(A     )     )
		raise ValueError('Not supported'     )



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






import requests

try:
							from django.contrib.auth import authenticate, login
							from django.shortcuts import redirect, render
except ImportError:
							UpperCamelCase__						=		UpperCamelCase__						=		UpperCamelCase__						=		UpperCamelCase__						=		print






def    snake_case   (				A     )				->				List[str]:




	'''simple docstring'''


	lowerCamelCase_				:						Optional[int]				  =				'secretKey'
	lowerCamelCase_				:						str				  =				'https://www.google.com/recaptcha/api/siteverify'

	# when method is not POST, direct user to login page
	if request.method != "POST":
		return render(A		,  'login.html'     )

	# from the frontend, get username, password, and client_key
	lowerCamelCase_				:						int				  =				request.POST.get('username'     )
	lowerCamelCase_				:						str				  =				request.POST.get('password'     )
	lowerCamelCase_				:						List[Any]				  =				request.POST.get('g-recaptcha-response'     )

	# post recaptcha response to Google's recaptcha api
	lowerCamelCase_				:						Union[str, Any]				  =				requests.post(A		,  data={'secret': secret_key, 'response': client_key}     )
	# if the recaptcha api verified our keys
	if response.json().get('success'		,  A     ):
		# authenticate the user
		lowerCamelCase_				:						int				  =				authenticate(A		,  username=A		,  password=A     )
		if user_in_database:
			login(A		,  A     )
			return redirect('/your-webpage'     )
	return render(A		,  'login.html'     )
