"""simple docstring"""






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class 							snake_case			(					__UpperCAmelCase							):




					"""simple docstring"""

					def __init__(							self  :      Optional[Any]       ,lowerCamelCase__  :      List[Any]       ,lowerCamelCase__  :      List[Any]     ):
									super().__init__()
									self.register_modules(unet=lowerCamelCase__       ,scheduler=lowerCamelCase__     )







					@torch.no_grad()
					def __call__(							self  :      int       ,lowerCamelCase__  :      int = 1       ,lowerCamelCase__  :      Optional[torch.Generator] = None       ,lowerCamelCase__  :      int = 50       ,lowerCamelCase__  :      Optional[str] = "pil"       ,lowerCamelCase__  :      bool = True       ,**lowerCamelCase__  :      Union[str, Any]       ,):
									UpperCAmelCase__			 =				torch.randn(
									    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,generator=lowerCamelCase__       ,)
									UpperCAmelCase__			 =				image.to(self.device     )

									# set step values
									self.scheduler.set_timesteps(lowerCamelCase__     )

									for t in self.progress_bar(self.scheduler.timesteps     ):
													# 1. predict noise model_output
													UpperCAmelCase__			 =				self.unet(lowerCamelCase__       ,lowerCamelCase__     ).sample

													# 2. predict previous mean of image x_t-1 and add variance depending on eta
													# eta corresponds to η in paper and should be between [0, 1]
													# do x_t -> x_t-1
													UpperCAmelCase__			 =				self.scheduler.step(lowerCamelCase__       ,lowerCamelCase__       ,lowerCamelCase__     ).prev_sample

									UpperCAmelCase__			 =				(image / 2 + 0.5).clamp(0       ,1     )
									UpperCAmelCase__			 =				image.cpu().permute(0       ,2       ,3       ,1     ).numpy()
									if output_type == "pil":
													UpperCAmelCase__			 =				self.numpy_to_pil(lowerCamelCase__     )

									if not return_dict:
													return (image,), "This is a local test"

									return ImagePipelineOutput(images=lowerCamelCase__     ), "This is a local test"




"""simple docstring"""






import requests

try:
						from django.contrib.auth import authenticate, login
						from django.shortcuts import redirect, render
except ImportError:
						lowerCAmelCase__    :    Union[str, Any]			=      print



def     a_		(   lowerCamelCase			):
				# Enter your recaptcha secret key here
				UpperCAmelCase__			 =				'secretKey'
				UpperCAmelCase__			 =				'https://www.google.com/recaptcha/api/siteverify'

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

				# from the frontend, get username, password, and client_key
				UpperCAmelCase__			 =				request.POST.get('username'			)
				UpperCAmelCase__			 =				request.POST.get('password'			)
				UpperCAmelCase__			 =				request.POST.get('g-recaptcha-response'			)

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



