




"""simple docstring"""
import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest

class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						int     		= (IPNDMScheduler,)
					__a						:						List[str]     		= (('num_inference_steps', 50),)
					def     snake_case_				(    self						,	**__a     ):
								__lowerCamelCase					:	str	       =				{'num_train_timesteps': 1000}
								config.update(**__a     )
								return config
					def     snake_case_				(    self						,	__a=0						,	**__a     ):
								__lowerCamelCase					:	int	       =				dict(self.forward_default_kwargs     )
								__lowerCamelCase					:	Union[str, Any]	       =				kwargs.pop('num_inference_steps'						,	__a     )
								__lowerCamelCase					:	Union[str, Any]	       =				self.dummy_sample
								__lowerCamelCase					:	int	       =				0.1 * sample
								__lowerCamelCase					:	Tuple	       =				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
											__lowerCamelCase					:	List[str]	       =				self.get_scheduler_config(**__a     )
											__lowerCamelCase					:	Tuple	       =				scheduler_class(**__a     )
											scheduler.set_timesteps(__a     )
											# copy over dummy past residuals
											__lowerCamelCase					:	List[Any]	       =				dummy_past_residuals[:]

											if time_step is None:
														__lowerCamelCase					:	Optional[Any]	       =				scheduler.timesteps[len(scheduler.timesteps     ) // 2]

											with tempfile.TemporaryDirectory() as tmpdirname:
														scheduler.save_config(__a     )
														__lowerCamelCase					:	Any	       =				scheduler_class.from_pretrained(__a     )
														new_scheduler.set_timesteps(__a     )
														# copy over dummy past residuals
														__lowerCamelCase					:	Tuple	       =				dummy_past_residuals[:]

											__lowerCamelCase					:	int	       =				scheduler.step(__a						,	__a						,	__a						,	**__a     ).prev_sample
											__lowerCamelCase					:	str	       =				new_scheduler.step(__a						,	__a						,	__a						,	**__a     ).prev_sample

											assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"

											__lowerCamelCase					:	str	       =				scheduler.step(__a						,	__a						,	__a						,	**__a     ).prev_sample
											__lowerCamelCase					:	Tuple	       =				new_scheduler.step(__a						,	__a						,	__a						,	**__a     ).prev_sample

											assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"
					def     snake_case_				(    self     ):
								pass
					def     snake_case_				(    self						,	__a=0						,	**__a     ):
								__lowerCamelCase					:	Optional[Any]	       =				dict(self.forward_default_kwargs     )
								__lowerCamelCase					:	Optional[int]	       =				kwargs.pop('num_inference_steps'						,	__a     )
								__lowerCamelCase					:	Optional[int]	       =				self.dummy_sample
								__lowerCamelCase					:	str	       =				0.1 * sample
								__lowerCamelCase					:	Optional[Any]	       =				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
											__lowerCamelCase					:	Any	       =				self.get_scheduler_config()
											__lowerCamelCase					:	Dict	       =				scheduler_class(**__a     )
											scheduler.set_timesteps(__a     )

											# copy over dummy past residuals (must be after setting timesteps)
											__lowerCamelCase					:	Optional[Any]	       =				dummy_past_residuals[:]

											if time_step is None:
														__lowerCamelCase					:	List[str]	       =				scheduler.timesteps[len(scheduler.timesteps     ) // 2]

											with tempfile.TemporaryDirectory() as tmpdirname:
														scheduler.save_config(__a     )
														__lowerCamelCase					:	int	       =				scheduler_class.from_pretrained(__a     )
														# copy over dummy past residuals
														new_scheduler.set_timesteps(__a     )

														# copy over dummy past residual (must be after setting timesteps)
														__lowerCamelCase					:	int	       =				dummy_past_residuals[:]

											__lowerCamelCase					:	Union[str, Any]	       =				scheduler.step(__a						,	__a						,	__a						,	**__a     ).prev_sample
											__lowerCamelCase					:	Dict	       =				new_scheduler.step(__a						,	__a						,	__a						,	**__a     ).prev_sample

											assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"

											__lowerCamelCase					:	Any	       =				scheduler.step(__a						,	__a						,	__a						,	**__a     ).prev_sample
											__lowerCamelCase					:	str	       =				new_scheduler.step(__a						,	__a						,	__a						,	**__a     ).prev_sample

											assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"
					def     snake_case_				(    self						,	**__a     ):
								__lowerCamelCase					:	Tuple	       =				self.scheduler_classes[0]
								__lowerCamelCase					:	Dict	       =				self.get_scheduler_config(**__a     )
								__lowerCamelCase					:	str	       =				scheduler_class(**__a     )

								__lowerCamelCase					:	str	       =				10
								__lowerCamelCase					:	Tuple	       =				self.dummy_model()
								__lowerCamelCase					:	int	       =				self.dummy_sample_deter
								scheduler.set_timesteps(__a     )

								for i, t in enumerate(scheduler.timesteps     ):
											__lowerCamelCase					:	List[Any]	       =				model(__a						,	__a     )
											__lowerCamelCase					:	List[str]	       =				scheduler.step(__a						,	__a						,	__a     ).prev_sample

								for i, t in enumerate(scheduler.timesteps     ):
											__lowerCamelCase					:	int	       =				model(__a						,	__a     )
											__lowerCamelCase					:	List[Any]	       =				scheduler.step(__a						,	__a						,	__a     ).prev_sample

								return sample
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[Any]	       =				dict(self.forward_default_kwargs     )

								__lowerCamelCase					:	Tuple	       =				kwargs.pop('num_inference_steps'						,	__a     )

								for scheduler_class in self.scheduler_classes:
											__lowerCamelCase					:	Optional[Any]	       =				self.get_scheduler_config()
											__lowerCamelCase					:	str	       =				scheduler_class(**__a     )

											__lowerCamelCase					:	str	       =				self.dummy_sample
											__lowerCamelCase					:	Union[str, Any]	       =				0.1 * sample

											if num_inference_steps is not None and hasattr(__a						,	'set_timesteps'     ):
														scheduler.set_timesteps(__a     )
											elif num_inference_steps is not None and not hasattr(__a						,	'set_timesteps'     ):
														__lowerCamelCase					:	Optional[Any]	       =				num_inference_steps

											# copy over dummy past residuals (must be done after set_timesteps)
											__lowerCamelCase					:	Optional[Any]	       =				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
											__lowerCamelCase					:	List[str]	       =				dummy_past_residuals[:]

											__lowerCamelCase					:	Optional[Any]	       =				scheduler.timesteps[5]
											__lowerCamelCase					:	Tuple	       =				scheduler.timesteps[6]

											__lowerCamelCase					:	Any	       =				scheduler.step(__a						,	__a						,	__a						,	**__a     ).prev_sample
											__lowerCamelCase					:	Any	       =				scheduler.step(__a						,	__a						,	__a						,	**__a     ).prev_sample

											self.assertEqual(output_a.shape						,	sample.shape     )
											self.assertEqual(output_a.shape						,	output_a.shape     )

											__lowerCamelCase					:	Tuple	       =				scheduler.step(__a						,	__a						,	__a						,	**__a     ).prev_sample
											__lowerCamelCase					:	Tuple	       =				scheduler.step(__a						,	__a						,	__a						,	**__a     ).prev_sample

											self.assertEqual(output_a.shape						,	sample.shape     )
											self.assertEqual(output_a.shape						,	output_a.shape     )
					def     snake_case_				(    self     ):
								for timesteps in [100, 1000]:
											self.check_over_configs(num_train_timesteps=__a						,	time_step=__a     )
					def     snake_case_				(    self     ):
								for t, num_inference_steps in zip([1, 5, 10]						,	[10, 50, 100]     ):
											self.check_over_forward(num_inference_steps=__a						,	time_step=__a     )





					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Any	       =				self.full_loop()
								__lowerCamelCase					:	Any	       =				torch.mean(torch.abs(__a     )     )

								assert abs(result_mean.item() - 2540529     ) < 10









"""simple docstring"""
import requests

a_				:    Optional[Any]										=     ''''''  # <-- Put your OpenWeatherMap appid here!
a_				:    str										=     '''https://api.openweathermap.org/data/2.5/'''





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





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





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


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

					while True:
										a_				:    List[Any]										=     input('''Enter a location:''').strip()
										if location:
															pprint(current_weather(location))
										else:
															break



