
'''simple docstring'''
import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest







class   a_ (      UpperCamelCase__					):
			lowerCamelCase__	:			List[Any]       =    (IPNDMScheduler,)
			lowerCamelCase__	:			Dict       =    (('num_inference_steps', 50),)




			def 					lowerCAmelCase__					(       self					,   **UpperCAmelCase	):
				a_      				=						{"""num_train_timesteps""": 10_00}
				config.update(**UpperCAmelCase	)
				return config




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase=0					,   **UpperCAmelCase	):
				a_      				=						dict(self.forward_default_kwargs	)
				a_      				=						kwargs.pop("""num_inference_steps"""					,   UpperCAmelCase	)
				a_      				=						self.dummy_sample
				a_      				=						0.1 * sample
				a_      				=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

				for scheduler_class in self.scheduler_classes:
					a_      				=						self.get_scheduler_config(**UpperCAmelCase	)
					a_      				=						scheduler_class(**UpperCAmelCase	)
					scheduler.set_timesteps(UpperCAmelCase	)
					# copy over dummy past residuals
					a_      				=						dummy_past_residuals[:]

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

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

					a_      				=						scheduler.step(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   **UpperCAmelCase	).prev_sample
					a_      				=						new_scheduler.step(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   **UpperCAmelCase	).prev_sample

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

					a_      				=						scheduler.step(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   **UpperCAmelCase	).prev_sample
					a_      				=						new_scheduler.step(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   **UpperCAmelCase	).prev_sample

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




			def 					lowerCAmelCase__					(       self	):
				pass




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase=0					,   **UpperCAmelCase	):
				a_      				=						dict(self.forward_default_kwargs	)
				a_      				=						kwargs.pop("""num_inference_steps"""					,   UpperCAmelCase	)
				a_      				=						self.dummy_sample
				a_      				=						0.1 * sample
				a_      				=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

				for scheduler_class in self.scheduler_classes:
					a_      				=						self.get_scheduler_config()
					a_      				=						scheduler_class(**UpperCAmelCase	)
					scheduler.set_timesteps(UpperCAmelCase	)

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

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

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

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

					a_      				=						scheduler.step(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   **UpperCAmelCase	).prev_sample
					a_      				=						new_scheduler.step(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   **UpperCAmelCase	).prev_sample

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

					a_      				=						scheduler.step(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   **UpperCAmelCase	).prev_sample
					a_      				=						new_scheduler.step(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   **UpperCAmelCase	).prev_sample

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




			def 					lowerCAmelCase__					(       self					,   **UpperCAmelCase	):
				a_      				=						self.scheduler_classes[0]
				a_      				=						self.get_scheduler_config(**UpperCAmelCase	)
				a_      				=						scheduler_class(**UpperCAmelCase	)

				a_      				=						10
				a_      				=						self.dummy_model()
				a_      				=						self.dummy_sample_deter
				scheduler.set_timesteps(UpperCAmelCase	)

				for i, t in enumerate(scheduler.timesteps	):
					a_      				=						model(UpperCAmelCase					,   UpperCAmelCase	)
					a_      				=						scheduler.step(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	).prev_sample

				for i, t in enumerate(scheduler.timesteps	):
					a_      				=						model(UpperCAmelCase					,   UpperCAmelCase	)
					a_      				=						scheduler.step(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	).prev_sample

				return sample




			def 					lowerCAmelCase__					(       self	):
				a_      				=						dict(self.forward_default_kwargs	)

				a_      				=						kwargs.pop("""num_inference_steps"""					,   UpperCAmelCase	)

				for scheduler_class in self.scheduler_classes:
					a_      				=						self.get_scheduler_config()
					a_      				=						scheduler_class(**UpperCAmelCase	)

					a_      				=						self.dummy_sample
					a_      				=						0.1 * sample

					if num_inference_steps is not None and hasattr(UpperCAmelCase					,   """set_timesteps"""	):
						scheduler.set_timesteps(UpperCAmelCase	)
					elif num_inference_steps is not None and not hasattr(UpperCAmelCase					,   """set_timesteps"""	):
						a_      				=						num_inference_steps

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

					a_      				=						scheduler.timesteps[5]
					a_      				=						scheduler.timesteps[6]

					a_      				=						scheduler.step(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   **UpperCAmelCase	).prev_sample
					a_      				=						scheduler.step(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   **UpperCAmelCase	).prev_sample

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

					a_      				=						scheduler.step(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   **UpperCAmelCase	).prev_sample
					a_      				=						scheduler.step(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   **UpperCAmelCase	).prev_sample

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




			def 					lowerCAmelCase__					(       self	):
				for timesteps in [1_00, 10_00]:
					self.check_over_configs(num_train_timesteps=UpperCAmelCase					,   time_step=UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				for t, num_inference_steps in zip([1, 5, 10]					,   [10, 50, 1_00]	):
					self.check_over_forward(num_inference_steps=UpperCAmelCase					,   time_step=UpperCAmelCase	)






			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.full_loop()
				a_      				=						torch.mean(torch.abs(UpperCAmelCase	)	)

				assert abs(result_mean.item() - 2_54_05_29	) < 10


'''simple docstring'''
import requests

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






def      UpperCamelCase_				(      A__ = "Chicago"  ,      A__ = APPID						):
	return requests.get(URL_BASE + """weather"""  ,      params=locals()						).json()






def      UpperCamelCase_				(      A__ = "Kolkata, India"  ,      A__ = APPID						):
	return requests.get(URL_BASE + """forecast"""  ,      params=locals()						).json()






def      UpperCamelCase_				(      A__ = 55.68  ,      A__ = 12.57  ,      A__ = APPID						):
	return requests.get(URL_BASE + """onecall"""  ,      params=locals()						).json()


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

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