






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest



class 							SCREAMING_SNAKE_CASE__						(    _a						):





					"""simple docstring"""






					a_     =		(PNDMScheduler,)
					a_     =		(("""num_inference_steps""", 5_0),)



					def 	_lowercase					( self					:  List[str]							,		**__A					:  List[Any]	):
						snake_case__			:				Optional[int]       =  {
						    'num_train_timesteps': 1_0_0_0,
						    'beta_start': 0.0_0_0_1,
						    'beta_end': 0.0_2,
						    'beta_schedule': 'linear',
						}

						config.update(**__A	)
						return config



					def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  str=0							,		**__A					:  Union[str, Any]	):
						snake_case__			:				Optional[Any]       =  dict(self.forward_default_kwargs	)
						snake_case__			:				int       =  kwargs.pop("num_inference_steps"							,		__A	)
						snake_case__			:				Optional[int]       =  self.dummy_sample
						snake_case__			:				List[str]       =  0.1 * sample
						snake_case__			:				str       =  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

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

							snake_case__			:				List[Any]       =  scheduler.step_prk(__A							,		__A							,		__A							,		**__A	).prev_sample
							snake_case__			:				Any       =  new_scheduler.step_prk(__A							,		__A							,		__A							,		**__A	).prev_sample

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

							snake_case__			:				Union[str, Any]       =  scheduler.step_plms(__A							,		__A							,		__A							,		**__A	).prev_sample
							snake_case__			:				Optional[int]       =  new_scheduler.step_plms(__A							,		__A							,		__A							,		**__A	).prev_sample

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



					def 	_lowercase					( self					:  List[Any]	):
						pass



					def 	_lowercase					( self					:  str							,		__A					:  Dict=0							,		**__A					:  Any	):
						snake_case__			:				Dict       =  dict(self.forward_default_kwargs	)
						snake_case__			:				Dict       =  kwargs.pop("num_inference_steps"							,		__A	)
						snake_case__			:				List[str]       =  self.dummy_sample
						snake_case__			:				List[Any]       =  0.1 * sample
						snake_case__			:				Tuple       =  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

						for scheduler_class in self.scheduler_classes:
							snake_case__			:				Union[str, Any]       =  self.get_scheduler_config()
							snake_case__			:				Any       =  scheduler_class(**__A	)
							scheduler.set_timesteps(__A	)

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

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(__A	)
								snake_case__			:				Any       =  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)
								snake_case__			:				Optional[int]       =  dummy_past_residuals[:]

							snake_case__			:				Any       =  scheduler.step_prk(__A							,		__A							,		__A							,		**__A	).prev_sample
							snake_case__			:				Union[str, Any]       =  new_scheduler.step_prk(__A							,		__A							,		__A							,		**__A	).prev_sample

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

							snake_case__			:				Tuple       =  scheduler.step_plms(__A							,		__A							,		__A							,		**__A	).prev_sample
							snake_case__			:				List[str]       =  new_scheduler.step_plms(__A							,		__A							,		__A							,		**__A	).prev_sample

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



					def 	_lowercase					( self					:  Union[str, Any]							,		**__A					:  str	):
						snake_case__			:				List[str]       =  self.scheduler_classes[0]
						snake_case__			:				int       =  self.get_scheduler_config(**__A	)
						snake_case__			:				Union[str, Any]       =  scheduler_class(**__A	)

						snake_case__			:				Optional[Any]       =  1_0
						snake_case__			:				Tuple       =  self.dummy_model()
						snake_case__			:				Optional[Any]       =  self.dummy_sample_deter
						scheduler.set_timesteps(__A	)

						for i, t in enumerate(scheduler.prk_timesteps	):
							snake_case__			:				Any       =  model(__A							,		__A	)
							snake_case__			:				Union[str, Any]       =  scheduler.step_prk(__A							,		__A							,		__A	).prev_sample

						for i, t in enumerate(scheduler.plms_timesteps	):
							snake_case__			:				Union[str, Any]       =  model(__A							,		__A	)
							snake_case__			:				Optional[int]       =  scheduler.step_plms(__A							,		__A							,		__A	).prev_sample

						return sample



					def 	_lowercase					( self					:  int	):
						snake_case__			:				Dict       =  dict(self.forward_default_kwargs	)

						snake_case__			:				Optional[Any]       =  kwargs.pop("num_inference_steps"							,		__A	)

						for scheduler_class in self.scheduler_classes:
							snake_case__			:				Dict       =  self.get_scheduler_config()
							snake_case__			:				List[str]       =  scheduler_class(**__A	)

							snake_case__			:				Optional[Any]       =  self.dummy_sample
							snake_case__			:				Dict       =  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"	):
								snake_case__			:				Any       =  num_inference_steps

							# copy over dummy past residuals (must be done after set_timesteps)
							snake_case__			:				List[str]       =  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
							snake_case__			:				List[Any]       =  dummy_past_residuals[:]

							snake_case__			:				Optional[int]       =  scheduler.step_prk(__A							,		0							,		__A							,		**__A	).prev_sample
							snake_case__			:				Union[str, Any]       =  scheduler.step_prk(__A							,		1							,		__A							,		**__A	).prev_sample

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

							snake_case__			:				Tuple       =  scheduler.step_plms(__A							,		0							,		__A							,		**__A	).prev_sample
							snake_case__			:				Any       =  scheduler.step_plms(__A							,		1							,		__A							,		**__A	).prev_sample

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



					def 	_lowercase					( self					:  int	):

						for timesteps in [1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=__A	)



					def 	_lowercase					( self					:  Optional[Any]	):

						for steps_offset in [0, 1]:
							self.check_over_configs(steps_offset=__A	)

						snake_case__			:				Any       =  self.scheduler_classes[0]
						snake_case__			:				Union[str, Any]       =  self.get_scheduler_config(steps_offset=1	)
						snake_case__			:				Any       =  scheduler_class(**__A	)
						scheduler.set_timesteps(1_0	)
						assert torch.equal(
						    scheduler.timesteps							,		torch.LongTensor(
						        [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]	)							,		)



					def 	_lowercase					( self					:  Tuple	):

						for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]							,		[0.0_0_2, 0.0_2]	):
							self.check_over_configs(beta_start=__A							,		beta_end=__A	)



					def 	_lowercase					( self					:  Any	):

						for schedule in ["linear", "squaredcos_cap_v2"]:
							self.check_over_configs(beta_schedule=__A	)



					def 	_lowercase					( self					:  Tuple	):

						for prediction_type in ["epsilon", "v_prediction"]:
							self.check_over_configs(prediction_type=__A	)



					def 	_lowercase					( self					:  List[Any]	):

						for t in [1, 5, 1_0]:
							self.check_over_forward(time_step=__A	)



					def 	_lowercase					( self					:  int	):

						for t, num_inference_steps in zip([1, 5, 1_0]							,		[1_0, 5_0, 1_0_0]	):
							self.check_over_forward(num_inference_steps=__A	)



					def 	_lowercase					( self					:  Tuple	):
						snake_case__			:				Optional[int]       =  2_7

						for scheduler_class in self.scheduler_classes:
							snake_case__			:				Tuple       =  self.dummy_sample
							snake_case__			:				Union[str, Any]       =  0.1 * sample

							snake_case__			:				int       =  self.get_scheduler_config()
							snake_case__			:				int       =  scheduler_class(**__A	)

							scheduler.set_timesteps(__A	)

							# before power of 3 fix, would error on first step, so we only need to do two
							for i, t in enumerate(scheduler.prk_timesteps[:2]	):
								snake_case__			:				List[str]       =  scheduler.step_prk(__A							,		__A							,		__A	).prev_sample



					def 	_lowercase					( self					:  Optional[int]	):

						with self.assertRaises(__A	):
							snake_case__			:				str       =  self.scheduler_classes[0]
							snake_case__			:				Optional[Any]       =  self.get_scheduler_config()
							snake_case__			:				List[Any]       =  scheduler_class(**__A	)

							scheduler.step_plms(self.dummy_sample							,		1							,		self.dummy_sample	).prev_sample



					def 	_lowercase					( self					:  str	):
						snake_case__			:				Dict       =  self.full_loop()
						snake_case__			:				Tuple       =  torch.sum(torch.abs(__A	)	)
						snake_case__			:				str       =  torch.mean(torch.abs(__A	)	)

						assert abs(result_sum.item() - 1_9_8.1_3_1_8	) < 1e-2
						assert abs(result_mean.item() - 0.2_5_8_0	) < 1e-3



					def 	_lowercase					( self					:  Optional[Any]	):
						snake_case__			:				Dict       =  self.full_loop(prediction_type="v_prediction"	)
						snake_case__			:				Union[str, Any]       =  torch.sum(torch.abs(__A	)	)
						snake_case__			:				Optional[Any]       =  torch.mean(torch.abs(__A	)	)

						assert abs(result_sum.item() - 6_7.3_9_8_6	) < 1e-2
						assert abs(result_mean.item() - 0.0_8_7_8	) < 1e-3



					def 	_lowercase					( self					:  Dict	):
						snake_case__			:				Any       =  self.full_loop(set_alpha_to_one=__A							,		beta_start=0.0_1	)
						snake_case__			:				Tuple       =  torch.sum(torch.abs(__A	)	)
						snake_case__			:				str       =  torch.mean(torch.abs(__A	)	)

						assert abs(result_sum.item() - 2_3_0.0_3_9_9	) < 1e-2
						assert abs(result_mean.item() - 0.2_9_9_5	) < 1e-3






					def 	_lowercase					( self					:  Tuple	):
						snake_case__			:				Any       =  self.full_loop(set_alpha_to_one=__A							,		beta_start=0.0_1	)
						snake_case__			:				Tuple       =  torch.sum(torch.abs(__A	)	)
						snake_case__			:				List[str]       =  torch.mean(torch.abs(__A	)	)

						assert abs(result_sum.item() - 1_8_6.9_4_8_2	) < 1e-2
						assert abs(result_mean.item() - 0.2_4_3_4	) < 1e-3











from datetime import datetime

import matplotlib.pyplot as plt
import torch







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int       ):
	for param in module.parameters():
		snake_case__			:				Tuple       =  False







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				Any       =  "cuda" if torch.cuda.is_available() else "cpu"
	if torch.backends.mps.is_available() and torch.backends.mps.is_built():
		snake_case__			:				List[Any]       =  "mps"
	if device == "mps":
		print(
		    "WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"
		    " errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"
		    " with generations."       )
	return device







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str       ):
	snake_case__			:				List[str]       =  plt.imshow(snake_case_       )
	fig.axes.get_xaxis().set_visible(snake_case_       )
	fig.axes.get_yaxis().set_visible(snake_case_       )
	plt.show()







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				str       =  datetime.now()
	snake_case__			:				Optional[Any]       =  current_time.strftime("%H:%M:%S"       )
	return timestamp



