






"""simple docstring"""



import requests

A_    =							''''''  # <-- Put your OpenWeatherMap appid here!
A_    =							'''https://api.openweathermap.org/data/2.5/'''




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str = "Chicago",						UpperCAmelCase__       :					str = APPID      )		->dict:
						return requests.get(URL_BASE + """weather""",						params=locals()      ).json()




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str = "Kolkata, India",						UpperCAmelCase__       :					str = APPID      )		->dict:
						return requests.get(URL_BASE + """forecast""",						params=locals()      ).json()




def      _lowerCAmelCase		(							UpperCAmelCase__       :					float = 55.68,						UpperCAmelCase__       :					float = 12.57,						UpperCAmelCase__       :					str = APPID      )		->dict:
						return requests.get(URL_BASE + """onecall""",						params=locals()      ).json()


if __name__ == "__main__":
						from pprint import pprint

						while True:
												A_    =							input('''Enter a location:''').strip()
												if location:
																		pprint(current_weather(location))
												else:
																		break














"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							42
							snake_case_     =							42


							def __init__(			self      :						Union[str, Any]			,    snake_case      :						UNetaDModel			,    snake_case      :						KarrasVeScheduler							):
													'''simple docstring'''





													super().__init__()
													self.register_modules(unet=snake_case			,    scheduler=snake_case							)


							@torch.no_grad()
							def __call__(			self      :						Any			,    snake_case      :						int = 1			,    snake_case      :						int = 50			,    snake_case      :						Optional[Union[torch.Generator, List[torch.Generator]]] = None			,    snake_case      :						Optional[str] = "pil"			,    snake_case      :						bool = True			,    **snake_case      :						Dict			,    ):
													'''simple docstring'''





													A__						:  Optional[int]		  =  self.unet.config.sample_size
													A__						:  Dict		  =  (batch_size, 3, img_size, img_size)

													A__						:  Tuple		  =  self.unet

													# sample x_0 ~ N(0, sigma_0^2 * I)
													A__						:  Union[str, Any]		  =  randn_tensor(snake_case			,    generator=snake_case			,    device=self.device							) * self.scheduler.init_noise_sigma

													self.scheduler.set_timesteps(snake_case							)

													for t in self.progress_bar(self.scheduler.timesteps							):
																			# here sigma_t == t_i from the paper
																			A__						:  Optional[Any]		  =  self.scheduler.schedule[t]
																			A__						:  Union[str, Any]		  =  self.scheduler.schedule[t - 1] if t > 0 else 0

																			# 1. Select temporarily increased noise level sigma_hat
																			# 2. Add new noise to move from sample_i to sample_hat
																			A__  ,  A__						:  Dict		  =  self.scheduler.add_noise_to_input(snake_case			,    snake_case			,    generator=snake_case							)

																			# 3. Predict the noise residual given the noise magnitude `sigma_hat`
																			# The model inputs and output are adjusted by following eq. (213) in [1].
																			A__						:  int		  =  (sigma_hat / 2) * model((sample_hat + 1) / 2			,    sigma_hat / 2							).sample

																			# 4. Evaluate dx/dt at sigma_hat
																			# 5. Take Euler step from sigma to sigma_prev
																			A__						:  Tuple		  =  self.scheduler.step(snake_case			,    snake_case			,    snake_case			,    snake_case							)

																			if sigma_prev != 0:
																									# 6. Apply 2nd order correction
																									# The model inputs and output are adjusted by following eq. (213) in [1].
																									A__						:  Dict		  =  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2			,    sigma_prev / 2							).sample
																									A__						:  Tuple		  =  self.scheduler.step_correct(
																									    snake_case			,    snake_case			,    snake_case			,    snake_case			,    step_output.prev_sample			,    step_output["""derivative"""]			,    )
																			A__						:  Tuple		  =  step_output.prev_sample

													A__						:  Dict		  =  (sample / 2 + 0.5).clamp(0			,    1							)
													A__						:  Dict		  =  sample.cpu().permute(0			,    2			,    3			,    1							).numpy()
													if output_type == "pil":
																			A__						:  Optional[Any]		  =  self.numpy_to_pil(snake_case							)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=snake_case							)






