



import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						UpperCAmelCase			:		int		=		(PNDMScheduler,)
						UpperCAmelCase			:		str		=		(('num_inference_steps', 50),)







						def       lowerCamelCase_				(  self    :				List[Any]       ,	**snake_case    :				Union[str, Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				{
											    'num_train_timesteps': 1000,
											    'beta_start': 0.0001,
											    'beta_end': 0.02,
											    'beta_schedule': 'linear',
											}

											config.update(**snake_case	)
											return config







						def       lowerCamelCase_				(  self    :				Optional[Any]       ,	snake_case    :				Dict=0       ,	**snake_case    :				Dict	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				dict(self.forward_default_kwargs	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				kwargs.pop('num_inference_steps'       ,	snake_case	)
											SCREAMING_SNAKE_CASE							:     Tuple						=				self.dummy_sample
											SCREAMING_SNAKE_CASE							:     Any						=				0.1 * sample
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

											for scheduler_class in self.scheduler_classes:
																SCREAMING_SNAKE_CASE							:     Optional[Any]						=				self.get_scheduler_config(**snake_case	)
																SCREAMING_SNAKE_CASE							:     int						=				scheduler_class(**snake_case	)
																scheduler.set_timesteps(snake_case	)
																# copy over dummy past residuals
																SCREAMING_SNAKE_CASE							:     Any						=				dummy_past_residuals[:]

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

																SCREAMING_SNAKE_CASE							:     Optional[Any]						=				scheduler.step_prk(snake_case       ,	snake_case       ,	snake_case       ,	**snake_case	).prev_sample
																SCREAMING_SNAKE_CASE							:     Dict						=				new_scheduler.step_prk(snake_case       ,	snake_case       ,	snake_case       ,	**snake_case	).prev_sample

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

																SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				scheduler.step_plms(snake_case       ,	snake_case       ,	snake_case       ,	**snake_case	).prev_sample
																SCREAMING_SNAKE_CASE							:     Optional[Any]						=				new_scheduler.step_plms(snake_case       ,	snake_case       ,	snake_case       ,	**snake_case	).prev_sample

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







						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											pass







						def       lowerCamelCase_				(  self    :				int       ,	snake_case    :				Union[str, Any]=0       ,	**snake_case    :				Dict	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				dict(self.forward_default_kwargs	)
											SCREAMING_SNAKE_CASE							:     Dict						=				kwargs.pop('num_inference_steps'       ,	snake_case	)
											SCREAMING_SNAKE_CASE							:     Any						=				self.dummy_sample
											SCREAMING_SNAKE_CASE							:     List[Any]						=				0.1 * sample
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

											for scheduler_class in self.scheduler_classes:
																SCREAMING_SNAKE_CASE							:     List[Any]						=				self.get_scheduler_config()
																SCREAMING_SNAKE_CASE							:     List[str]						=				scheduler_class(**snake_case	)
																scheduler.set_timesteps(snake_case	)

																# copy over dummy past residuals (must be after setting timesteps)
																SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				dummy_past_residuals[:]

																with tempfile.TemporaryDirectory() as tmpdirname:
																					scheduler.save_config(snake_case	)
																					SCREAMING_SNAKE_CASE							:     List[Any]						=				scheduler_class.from_pretrained(snake_case	)
																					# copy over dummy past residuals
																					new_scheduler.set_timesteps(snake_case	)

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

																SCREAMING_SNAKE_CASE							:     str						=				scheduler.step_prk(snake_case       ,	snake_case       ,	snake_case       ,	**snake_case	).prev_sample
																SCREAMING_SNAKE_CASE							:     List[str]						=				new_scheduler.step_prk(snake_case       ,	snake_case       ,	snake_case       ,	**snake_case	).prev_sample

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

																SCREAMING_SNAKE_CASE							:     Tuple						=				scheduler.step_plms(snake_case       ,	snake_case       ,	snake_case       ,	**snake_case	).prev_sample
																SCREAMING_SNAKE_CASE							:     Optional[int]						=				new_scheduler.step_plms(snake_case       ,	snake_case       ,	snake_case       ,	**snake_case	).prev_sample

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







						def       lowerCamelCase_				(  self    :				Dict       ,	**snake_case    :				Optional[int]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Any						=				self.scheduler_classes[0]
											SCREAMING_SNAKE_CASE							:     Tuple						=				self.get_scheduler_config(**snake_case	)
											SCREAMING_SNAKE_CASE							:     int						=				scheduler_class(**snake_case	)

											SCREAMING_SNAKE_CASE							:     str						=				10
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				self.dummy_model()
											SCREAMING_SNAKE_CASE							:     Any						=				self.dummy_sample_deter
											scheduler.set_timesteps(snake_case	)

											for i, t in enumerate(scheduler.prk_timesteps	):
																SCREAMING_SNAKE_CASE							:     Optional[Any]						=				model(snake_case       ,	snake_case	)
																SCREAMING_SNAKE_CASE							:     str						=				scheduler.step_prk(snake_case       ,	snake_case       ,	snake_case	).prev_sample

											for i, t in enumerate(scheduler.plms_timesteps	):
																SCREAMING_SNAKE_CASE							:     Any						=				model(snake_case       ,	snake_case	)
																SCREAMING_SNAKE_CASE							:     Dict						=				scheduler.step_plms(snake_case       ,	snake_case       ,	snake_case	).prev_sample

											return sample







						def       lowerCamelCase_				(  self    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     int						=				dict(self.forward_default_kwargs	)

											SCREAMING_SNAKE_CASE							:     int						=				kwargs.pop('num_inference_steps'       ,	snake_case	)

											for scheduler_class in self.scheduler_classes:
																SCREAMING_SNAKE_CASE							:     Optional[Any]						=				self.get_scheduler_config()
																SCREAMING_SNAKE_CASE							:     List[str]						=				scheduler_class(**snake_case	)

																SCREAMING_SNAKE_CASE							:     Optional[int]						=				self.dummy_sample
																SCREAMING_SNAKE_CASE							:     List[str]						=				0.1 * sample

																if num_inference_steps is not None and hasattr(snake_case       ,	'set_timesteps'	):
																					scheduler.set_timesteps(snake_case	)
																elif num_inference_steps is not None and not hasattr(snake_case       ,	'set_timesteps'	):
																					SCREAMING_SNAKE_CASE							:     List[Any]						=				num_inference_steps

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

																SCREAMING_SNAKE_CASE							:     Optional[Any]						=				scheduler.step_prk(snake_case       ,	0       ,	snake_case       ,	**snake_case	).prev_sample
																SCREAMING_SNAKE_CASE							:     int						=				scheduler.step_prk(snake_case       ,	1       ,	snake_case       ,	**snake_case	).prev_sample

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

																SCREAMING_SNAKE_CASE							:     Tuple						=				scheduler.step_plms(snake_case       ,	0       ,	snake_case       ,	**snake_case	).prev_sample
																SCREAMING_SNAKE_CASE							:     str						=				scheduler.step_plms(snake_case       ,	1       ,	snake_case       ,	**snake_case	).prev_sample

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







						def       lowerCamelCase_				(  self    :				Union[str, Any]	):



											'''simple docstring'''


											for timesteps in [100, 1000]:
																self.check_over_configs(num_train_timesteps=snake_case	)







						def       lowerCamelCase_				(  self    :				Optional[int]	):



											'''simple docstring'''


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

											SCREAMING_SNAKE_CASE							:     Tuple						=				self.scheduler_classes[0]
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				self.get_scheduler_config(steps_offset=1	)
											SCREAMING_SNAKE_CASE							:     str						=				scheduler_class(**snake_case	)
											scheduler.set_timesteps(10	)
											assert torch.equal(
											    scheduler.timesteps       ,	torch.LongTensor(
											        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]	)       ,	)







						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											for beta_start, beta_end in zip([0.0001, 0.001]       ,	[0.002, 0.02]	):
																self.check_over_configs(beta_start=snake_case       ,	beta_end=snake_case	)







						def       lowerCamelCase_				(  self    :				List[str]	):



											'''simple docstring'''


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







						def       lowerCamelCase_				(  self    :				Optional[Any]	):



											'''simple docstring'''


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







						def       lowerCamelCase_				(  self    :				Optional[int]	):



											'''simple docstring'''


											for t in [1, 5, 10]:
																self.check_over_forward(time_step=snake_case	)







						def       lowerCamelCase_				(  self    :				int	):



											'''simple docstring'''


											for t, num_inference_steps in zip([1, 5, 10]       ,	[10, 50, 100]	):
																self.check_over_forward(num_inference_steps=snake_case	)







						def       lowerCamelCase_				(  self    :				int	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Any						=				27

											for scheduler_class in self.scheduler_classes:
																SCREAMING_SNAKE_CASE							:     List[str]						=				self.dummy_sample
																SCREAMING_SNAKE_CASE							:     int						=				0.1 * sample

																SCREAMING_SNAKE_CASE							:     List[str]						=				self.get_scheduler_config()
																SCREAMING_SNAKE_CASE							:     int						=				scheduler_class(**snake_case	)

																scheduler.set_timesteps(snake_case	)

																# 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]	):
																					SCREAMING_SNAKE_CASE							:     List[str]						=				scheduler.step_prk(snake_case       ,	snake_case       ,	snake_case	).prev_sample







						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											with self.assertRaises(snake_case	):
																SCREAMING_SNAKE_CASE							:     List[Any]						=				self.scheduler_classes[0]
																SCREAMING_SNAKE_CASE							:     Optional[Any]						=				self.get_scheduler_config()
																SCREAMING_SNAKE_CASE							:     List[Any]						=				scheduler_class(**snake_case	)

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







						def       lowerCamelCase_				(  self    :				Optional[int]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				self.full_loop()
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				torch.sum(torch.abs(snake_case	)	)
											SCREAMING_SNAKE_CASE							:     List[str]						=				torch.mean(torch.abs(snake_case	)	)

											assert abs(result_sum.item() - 198.1318	) < 1E-2
											assert abs(result_mean.item() - 0.2580	) < 1E-3







						def       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[str]						=				self.full_loop(prediction_type='v_prediction'	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				torch.sum(torch.abs(snake_case	)	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				torch.mean(torch.abs(snake_case	)	)

											assert abs(result_sum.item() - 67.3986	) < 1E-2
											assert abs(result_mean.item() - 0.0878	) < 1E-3







						def       lowerCamelCase_				(  self    :				Union[str, Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				self.full_loop(set_alpha_to_one=snake_case       ,	beta_start=0.01	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				torch.sum(torch.abs(snake_case	)	)
											SCREAMING_SNAKE_CASE							:     Tuple						=				torch.mean(torch.abs(snake_case	)	)

											assert abs(result_sum.item() - 230.0399	) < 1E-2
											assert abs(result_mean.item() - 0.2995	) < 1E-3







						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     str						=				self.full_loop(set_alpha_to_one=snake_case       ,	beta_start=0.01	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				torch.sum(torch.abs(snake_case	)	)
											SCREAMING_SNAKE_CASE							:     Tuple						=				torch.mean(torch.abs(snake_case	)	)

											assert abs(result_sum.item() - 186.9482	) < 1E-2
											assert abs(result_mean.item() - 0.2434	) < 1E-3




from datetime import datetime

import matplotlib.pyplot as plt
import torch

def        __a			(   __lowerCAmelCase   )     ->     int:
					for param in module.parameters():
										SCREAMING_SNAKE_CASE							:     List[Any]						=				False

def        __a			(   )     ->     List[str]:
					SCREAMING_SNAKE_CASE							:     List[Any]						=				'cuda' if torch.cuda.is_available() else 'cpu'
					if torch.backends.mps.is_available() and torch.backends.mps.is_built():
										SCREAMING_SNAKE_CASE							:     List[str]						=				'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        __a			(   __lowerCAmelCase   )     ->     List[Any]:
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				plt.imshow(__lowerCAmelCase   )
					fig.axes.get_xaxis().set_visible(__lowerCAmelCase   )
					fig.axes.get_yaxis().set_visible(__lowerCAmelCase   )
					plt.show()



def        __a			(   )     ->     Optional[Any]:
					SCREAMING_SNAKE_CASE							:     str						=				datetime.now()
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				current_time.strftime('%H:%M:%S'   )
					return timestamp