




import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class      a__       (   snake_case__ ):
						_a				:       Tuple   =       4_2
						_a				:       Optional[Any]   =       None







def       _a (		SCREAMING_SNAKE_CASE_				:							Any , SCREAMING_SNAKE_CASE_				:							Optional[int]=0.9_99 , SCREAMING_SNAKE_CASE_				:							Tuple="cosine" , ):


					if alpha_transform_type == "cosine":

										def alpha_bar_fn(SCREAMING_SNAKE_CASE_				:							Any	):
															return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2	) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(SCREAMING_SNAKE_CASE_				:							List[str]	):
															return math.exp(t * -12.0	)

					else:
										raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""	)

					__lowerCAmelCase							  =			[]
					for i in range(SCREAMING_SNAKE_CASE_	):
										__lowerCAmelCase							  =			i / num_diffusion_timesteps
										__lowerCAmelCase							  =			(i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE_	) / alpha_bar_fn(SCREAMING_SNAKE_CASE_	) , SCREAMING_SNAKE_CASE_	)	)
					return torch.tensor(SCREAMING_SNAKE_CASE_ , dtype=torch.floataa	)


class      a__       (   snake_case__					,							snake_case__ ):
						_a				:       int   =       [e.name for e in KarrasDiffusionSchedulers]
						_a				:       Optional[Any]   =       1






						@register_to_config
						def __init__(  self						,     _A = 1_0_0_0						,     _A = 0.00_01						,     _A = 0.02						,     _A = "linear"						,     _A = None						,     _A = False						,     _A = "epsilon"						,     _A = "linspace"						,     _A = 0						,     ):




											"""simple docstring"""



											if trained_betas is not None:
																__lowerCAmelCase							  =			torch.tensor(__lowercase						,     dtype=torch.floataa     )
											elif beta_schedule == "linear":
																__lowerCAmelCase							  =			torch.linspace(__lowercase						,     __lowercase						,     __lowercase						,     dtype=torch.floataa     )
											elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																__lowerCAmelCase							  =			(
																    torch.linspace(beta_start**0.5						,     beta_end**0.5						,     __lowercase						,     dtype=torch.floataa     ) ** 2
																)
											elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																__lowerCAmelCase							  =			betas_for_alpha_bar(__lowercase     )
											else:
																raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""     )

											__lowerCAmelCase							  =			1.0 - self.betas
											__lowerCAmelCase							  =			torch.cumprod(self.alphas						,     dim=0     )

											__lowerCAmelCase							  =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
											__lowerCAmelCase							  =			np.concatenate([sigmas[::-1], [0.0]]     ).astype(np.floataa     )
											__lowerCAmelCase							  =			torch.from_numpy(__lowercase     )

											# setable values
											__lowerCAmelCase							  =			None
											__lowerCAmelCase							  =			use_karras_sigmas
											self.set_timesteps(__lowercase						,     __lowercase     )
											__lowerCAmelCase							  =			[]
											__lowerCAmelCase							  =			False






						@property
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											if self.config.timestep_spacing in ["linspace", "trailing"]:
																return self.sigmas.max()

											return (self.sigmas.max() ** 2 + 1) ** 0.5






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A     ):




											"""simple docstring"""



											if isinstance(__lowercase						,     torch.Tensor     ):
																__lowerCAmelCase							  =			timestep.to(self.timesteps.device     )
											__lowerCAmelCase							  =			(self.timesteps == timestep).nonzero().item()
											__lowerCAmelCase							  =			self.sigmas[step_index]
											__lowerCAmelCase							  =			sample / ((sigma**2 + 1) ** 0.5)
											__lowerCAmelCase							  =			True
											return sample






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A						,     _A     ):




											"""simple docstring"""



											def lms_derivative(_A     ):
																__lowerCAmelCase							  =			1.0
																for k in range(__lowercase     ):
																					if current_order == k:
																										continue
																					prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
																return prod

											__lowerCAmelCase							  =			integrate.quad(__lowercase						,     self.sigmas[t]						,     self.sigmas[t + 1]						,     epsrel=1E-4     )[0]

											return integrated_coeff






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A = None     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			num_inference_steps

											# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
											if self.config.timestep_spacing == "linspace":
																__lowerCAmelCase							  =			np.linspace(0						,     self.config.num_train_timesteps - 1						,     __lowercase						,     dtype=__lowercase     )[
																    ::-1
																].copy()
											elif self.config.timestep_spacing == "leading":
																__lowerCAmelCase							  =			self.config.num_train_timesteps // self.num_inference_steps
																# creates integer timesteps by multiplying by ratio
																# casting to int to avoid issues when num_inference_step is power of 3
																__lowerCAmelCase							  =			(np.arange(0						,     __lowercase     ) * step_ratio).round()[::-1].copy().astype(__lowercase     )
																timesteps += self.config.steps_offset
											elif self.config.timestep_spacing == "trailing":
																__lowerCAmelCase							  =			self.config.num_train_timesteps / self.num_inference_steps
																# creates integer timesteps by multiplying by ratio
																# casting to int to avoid issues when num_inference_step is power of 3
																__lowerCAmelCase							  =			(np.arange(self.config.num_train_timesteps						,     0						,     -step_ratio     )).round().copy().astype(__lowercase     )
																timesteps -= 1
											else:
																raise ValueError(
																    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""     )

											__lowerCAmelCase							  =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
											__lowerCAmelCase							  =			np.log(__lowercase     )
											__lowerCAmelCase							  =			np.interp(__lowercase						,     np.arange(0						,     len(__lowercase     )     )						,     __lowercase     )

											if self.use_karras_sigmas:
																__lowerCAmelCase							  =			self._convert_to_karras(in_sigmas=__lowercase     )
																__lowerCAmelCase							  =			np.array([self._sigma_to_t(__lowercase						,     __lowercase     ) for sigma in sigmas]     )

											__lowerCAmelCase							  =			np.concatenate([sigmas, [0.0]]     ).astype(np.floataa     )

											__lowerCAmelCase							  =			torch.from_numpy(__lowercase     ).to(device=__lowercase     )
											if str(__lowercase     ).startswith("mps"     ):
																# mps does not support float64
																__lowerCAmelCase							  =			torch.from_numpy(__lowercase     ).to(__lowercase						,     dtype=torch.floataa     )
											else:
																__lowerCAmelCase							  =			torch.from_numpy(__lowercase     ).to(device=__lowercase     )

											__lowerCAmelCase							  =			[]






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			np.log(__lowercase     )

											# get distribution
											__lowerCAmelCase							  =			log_sigma - log_sigmas[:, np.newaxis]

											# get sigmas range
											__lowerCAmelCase							  =			np.cumsum((dists >= 0)						,     axis=0     ).argmax(axis=0     ).clip(max=log_sigmas.shape[0] - 2     )
											__lowerCAmelCase							  =			low_idx + 1

											__lowerCAmelCase							  =			log_sigmas[low_idx]
											__lowerCAmelCase							  =			log_sigmas[high_idx]

											# interpolate sigmas
											__lowerCAmelCase							  =			(low - log_sigma) / (low - high)
											__lowerCAmelCase							  =			np.clip(__lowercase						,     0						,     1     )

											# transform interpolation to time range
											__lowerCAmelCase							  =			(1 - w) * low_idx + w * high_idx
											__lowerCAmelCase							  =			t.reshape(sigma.shape     )
											return t






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			in_sigmas[-1].item()
											__lowerCAmelCase							  =			in_sigmas[0].item()

											__lowerCAmelCase							  =			7.0  # 7.0 is the value used in the paper
											__lowerCAmelCase							  =			np.linspace(0						,     1						,     self.num_inference_steps     )
											__lowerCAmelCase							  =			sigma_min ** (1 / rho)
											__lowerCAmelCase							  =			sigma_max ** (1 / rho)
											__lowerCAmelCase							  =			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
											return sigmas






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A						,     _A						,     _A = 4						,     _A = True						,     ):




											"""simple docstring"""



											if not self.is_scale_input_called:
																warnings.warn(
																    "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
																    "See `StableDiffusionPipeline` for a usage example."     )

											if isinstance(__lowercase						,     torch.Tensor     ):
																__lowerCAmelCase							  =			timestep.to(self.timesteps.device     )
											__lowerCAmelCase							  =			(self.timesteps == timestep).nonzero().item()
											__lowerCAmelCase							  =			self.sigmas[step_index]

											# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
											if self.config.prediction_type == "epsilon":
																__lowerCAmelCase							  =			sample - sigma * model_output
											elif self.config.prediction_type == "v_prediction":
																# * c_out + input * c_skip
																__lowerCAmelCase							  =			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
											elif self.config.prediction_type == "sample":
																__lowerCAmelCase							  =			model_output
											else:
																raise ValueError(
																    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""     )

											# 2. Convert to an ODE derivative
											__lowerCAmelCase							  =			(sample - pred_original_sample) / sigma
											self.derivatives.append(__lowercase     )
											if len(self.derivatives     ) > order:
																self.derivatives.pop(0     )

											# 3. Compute linear multistep coefficients
											__lowerCAmelCase							  =			min(step_index + 1						,     __lowercase     )
											__lowerCAmelCase							  =			[self.get_lms_coefficient(__lowercase						,     __lowercase						,     __lowercase     ) for curr_order in range(__lowercase     )]

											# 4. Compute previous sample based on the derivatives path
											__lowerCAmelCase							  =			sample + sum(
											    coeff * derivative for coeff, derivative in zip(__lowercase						,     reversed(self.derivatives     )     )     )

											if not return_dict:
																return (prev_sample,)

											return LMSDiscreteSchedulerOutput(prev_sample=__lowercase						,     pred_original_sample=__lowercase     )






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A						,     _A						,     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.sigmas.to(device=original_samples.device						,     dtype=original_samples.dtype     )
											if original_samples.device.type == "mps" and torch.is_floating_point(__lowercase     ):
																# mps does not support float64
																__lowerCAmelCase							  =			self.timesteps.to(original_samples.device						,     dtype=torch.floataa     )
																__lowerCAmelCase							  =			timesteps.to(original_samples.device						,     dtype=torch.floataa     )
											else:
																__lowerCAmelCase							  =			self.timesteps.to(original_samples.device     )
																__lowerCAmelCase							  =			timesteps.to(original_samples.device     )

											__lowerCAmelCase							  =			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

											__lowerCAmelCase							  =			sigmas[step_indices].flatten()
											while len(sigma.shape     ) < len(original_samples.shape     ):
																__lowerCAmelCase							  =			sigma.unsqueeze(-1     )

											__lowerCAmelCase							  =			original_samples + noise * sigma
											return noisy_samples






						def __len__(  self     ):




											"""simple docstring"""



											return self.config.num_train_timesteps










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'     )
