



"""simple docstring"""


import shutil

import requests

def 					_lowerCAmelCase (  lowercase_						,      lowercase_ = False						,      lowercase_ = "."		):
						UpperCAmelCase			 =					'https://api.nasa.gov/planetary/apod'
						return requests.get(lowercase_						,      params={'api_key': api_key}		).json()

def 					_lowerCAmelCase (  lowercase_						,      lowercase_ = "."		):
						UpperCAmelCase			 =					get_apod_data(lowercase_		)
						UpperCAmelCase			 =					apod_data['url']
						UpperCAmelCase			 =					img_url.split('/'		)[-1]
						UpperCAmelCase			 =					requests.get(lowercase_						,      stream=lowercase_		)

						with open(F"""{path}/{img_name}"""						,      'wb+'		) as img_file:
												shutil.copyfileobj(response.raw						,      lowercase_		)
						del response
						return apod_data

def 					_lowerCAmelCase (  lowercase_		):
						UpperCAmelCase			 =					'https://images-api.nasa.gov/search'
						return requests.get(lowercase_						,      params={'q': query}		).json()


if __name__ == "__main__":
				print(save_apod("""YOUR API KEY"""))
				snake_case_								=			get_archive_data("""apollo 2011""")["""collection"""]["""items"""]
				print(apollo_aaaa_items[0]["""data"""][0]["""description"""])










"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						def __init__(   self					:List[str]  ,    lowercase_					:List[str]  ,    lowercase_					:List[Any]   )							->     List[str]:
												super().__init__()
												self.register_modules(unet=lowercase_  ,    scheduler=lowercase_   )






						@torch.no_grad()
						def __call__(   self					:int  ,    lowercase_					:int = 1  ,    lowercase_					:Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,    lowercase_					:int = 10_00  ,    lowercase_					:Optional[str] = "pil"  ,    lowercase_					:bool = True  ,    )							->     Union[ImagePipelineOutput, Tuple]:
												# Sample gaussian noise to begin loop
												if isinstance(self.unet.config.sample_size  ,    lowercase_   ):
																		UpperCAmelCase			 =					(
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
												else:
																		UpperCAmelCase			 =					(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

												if self.device.type == "mps":
																		# randn does not work reproducibly on mps
																		UpperCAmelCase			 =					randn_tensor(lowercase_  ,    generator=lowercase_   )
																		UpperCAmelCase			 =					image.to(self.device   )
												else:
																		UpperCAmelCase			 =					randn_tensor(lowercase_  ,    generator=lowercase_  ,    device=self.device   )

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

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

																		# 2. compute previous image: x_t -> x_t-1
																		UpperCAmelCase			 =					self.scheduler.step(lowercase_  ,    lowercase_  ,    lowercase_  ,    generator=lowercase_   ).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(lowercase_   )

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=lowercase_   )





