



"""simple docstring"""


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest







class       A_			(     SCREAMING_SNAKE_CASE_				):



						"""simple docstring"""
						__UpperCamelCase						=							(DDPMScheduler,)






						def 	UpperCAmelCase__     (					self :int   ,  **lowercase_ :Optional[Any]					)				->			Tuple:
												UpperCAmelCase             = {
												    'num_train_timesteps': 10_00,
												    'beta_start': 0.0001,
												    'beta_end': 0.02,
												    'beta_schedule': 'linear',
												    'variance_type': 'fixed_small',
												    'clip_sample': True,
												}

												config.update(**lowercase_					)
												return config






						def 	UpperCAmelCase__     (					self :int					)				->			Union[str, Any]:
												for timesteps in [1, 5, 1_00, 10_00]:
																		self.check_over_configs(num_train_timesteps=lowercase_					)






						def 	UpperCAmelCase__     (					self :int					)				->			Optional[Any]:
												for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]   ,  [0.002, 0.02, 0.2, 2]					):
																		self.check_over_configs(beta_start=lowercase_   ,  beta_end=lowercase_					)






						def 	UpperCAmelCase__     (					self :int					)				->			int:
												for schedule in ["linear", "squaredcos_cap_v2"]:
																		self.check_over_configs(beta_schedule=lowercase_					)






						def 	UpperCAmelCase__     (					self :List[Any]					)				->			List[Any]:
												for variance in ["fixed_small", "fixed_large", "other"]:
																		self.check_over_configs(variance_type=lowercase_					)






						def 	UpperCAmelCase__     (					self :Optional[int]					)				->			Tuple:
												for clip_sample in [True, False]:
																		self.check_over_configs(clip_sample=lowercase_					)






						def 	UpperCAmelCase__     (					self :List[str]					)				->			Dict:
												self.check_over_configs(thresholding=lowercase_					)
												for threshold in [0.5, 1.0, 2.0]:
																		for prediction_type in ["epsilon", "sample", "v_prediction"]:
																								self.check_over_configs(
																								    thresholding=lowercase_   ,  prediction_type=lowercase_   ,  sample_max_value=lowercase_   ,  )






						def 	UpperCAmelCase__     (					self :List[str]					)				->			Dict:
												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																		self.check_over_configs(prediction_type=lowercase_					)






						def 	UpperCAmelCase__     (					self :Optional[Any]					)				->			Optional[Any]:
												for t in [0, 5_00, 9_99]:
																		self.check_over_forward(time_step=lowercase_					)






						def 	UpperCAmelCase__     (					self :Any					)				->			Tuple:
												UpperCAmelCase             = self.scheduler_classes[0]
												UpperCAmelCase             = self.get_scheduler_config()
												UpperCAmelCase             = scheduler_class(**lowercase_					)

												assert torch.sum(torch.abs(scheduler._get_variance(0					) - 0.0					)					) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(4_87					) - 0.0_0979					)					) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(9_99					) - 0.02					)					) < 1E-5






						def 	UpperCAmelCase__     (					self :Union[str, Any]					)				->			Dict:
												UpperCAmelCase             = self.scheduler_classes[0]
												UpperCAmelCase             = self.get_scheduler_config()
												UpperCAmelCase             = scheduler_class(**lowercase_					)

												UpperCAmelCase             = len(lowercase_					)

												UpperCAmelCase             = self.dummy_model()
												UpperCAmelCase             = self.dummy_sample_deter
												UpperCAmelCase             = torch.manual_seed(0					)

												for t in reversed(range(lowercase_					)					):
																		# 1. predict noise residual
																		UpperCAmelCase             = model(lowercase_   ,  lowercase_					)

																		# 2. predict previous mean of sample x_t-1
																		UpperCAmelCase             = scheduler.step(lowercase_   ,  lowercase_   ,  lowercase_   ,  generator=lowercase_					).prev_sample

																		# if t > 0:
																		#     noise = self.dummy_sample_deter
																		#     variance = scheduler.get_variance(t) ** (0.5) * noise
																		#
																		# sample = pred_prev_sample + variance
																		UpperCAmelCase             = pred_prev_sample

												UpperCAmelCase             = torch.sum(torch.abs(lowercase_					)					)
												UpperCAmelCase             = torch.mean(torch.abs(lowercase_					)					)

												assert abs(result_sum.item() - 258.9606					) < 1E-2
												assert abs(result_mean.item() - 0.3372					) < 1E-3






						def 	UpperCAmelCase__     (					self :Optional[int]					)				->			Optional[Any]:
												UpperCAmelCase             = self.scheduler_classes[0]
												UpperCAmelCase             = self.get_scheduler_config(prediction_type='v_prediction'					)
												UpperCAmelCase             = scheduler_class(**lowercase_					)

												UpperCAmelCase             = len(lowercase_					)

												UpperCAmelCase             = self.dummy_model()
												UpperCAmelCase             = self.dummy_sample_deter
												UpperCAmelCase             = torch.manual_seed(0					)

												for t in reversed(range(lowercase_					)					):
																		# 1. predict noise residual
																		UpperCAmelCase             = model(lowercase_   ,  lowercase_					)

																		# 2. predict previous mean of sample x_t-1
																		UpperCAmelCase             = scheduler.step(lowercase_   ,  lowercase_   ,  lowercase_   ,  generator=lowercase_					).prev_sample

																		# if t > 0:
																		#     noise = self.dummy_sample_deter
																		#     variance = scheduler.get_variance(t) ** (0.5) * noise
																		#
																		# sample = pred_prev_sample + variance
																		UpperCAmelCase             = pred_prev_sample

												UpperCAmelCase             = torch.sum(torch.abs(lowercase_					)					)
												UpperCAmelCase             = torch.mean(torch.abs(lowercase_					)					)

												assert abs(result_sum.item() - 202.0296					) < 1E-2
												assert abs(result_mean.item() - 0.2631					) < 1E-3






						def 	UpperCAmelCase__     (					self :Union[str, Any]					)				->			List[Any]:
												UpperCAmelCase             = self.scheduler_classes[0]
												UpperCAmelCase             = self.get_scheduler_config()
												UpperCAmelCase             = scheduler_class(**lowercase_					)

												UpperCAmelCase             = [1_00, 87, 50, 1, 0]

												scheduler.set_timesteps(timesteps=lowercase_					)

												UpperCAmelCase             = scheduler.timesteps

												for i, timestep in enumerate(lowercase_					):
																		if i == len(lowercase_					) - 1:
																								UpperCAmelCase             = -1
																		else:
																								UpperCAmelCase             = timesteps[i + 1]

																		UpperCAmelCase             = scheduler.previous_timestep(lowercase_					)
																		UpperCAmelCase             = prev_t.item()

																		self.assertEqual(lowercase_   ,  lowercase_					)






						def 	UpperCAmelCase__     (					self :Any					)				->			Union[str, Any]:
												UpperCAmelCase             = self.scheduler_classes[0]
												UpperCAmelCase             = self.get_scheduler_config()
												UpperCAmelCase             = scheduler_class(**lowercase_					)

												UpperCAmelCase             = [1_00, 87, 50, 51, 0]

												with self.assertRaises(lowercase_   ,  msg='`custom_timesteps` must be in descending order.'					):
																		scheduler.set_timesteps(timesteps=lowercase_					)






						def 	UpperCAmelCase__     (					self :Any					)				->			Optional[Any]:
												UpperCAmelCase             = self.scheduler_classes[0]
												UpperCAmelCase             = self.get_scheduler_config()
												UpperCAmelCase             = scheduler_class(**lowercase_					)

												UpperCAmelCase             = [1_00, 87, 50, 1, 0]
												UpperCAmelCase             = len(lowercase_					)

												with self.assertRaises(lowercase_   ,  msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'					):
																		scheduler.set_timesteps(num_inference_steps=lowercase_   ,  timesteps=lowercase_					)






						def 	UpperCAmelCase__     (					self :Optional[Any]					)				->			Union[str, Any]:
												UpperCAmelCase             = self.scheduler_classes[0]
												UpperCAmelCase             = self.get_scheduler_config()
												UpperCAmelCase             = scheduler_class(**lowercase_					)

												UpperCAmelCase             = [scheduler.config.num_train_timesteps]

												with self.assertRaises(
												    lowercase_   ,  msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'   ,  ):
																		scheduler.set_timesteps(timesteps=lowercase_					)








import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
						import torch
						from torch import nn

						from transformers import (
						    Adafactor,
						    AdamW,
						    get_constant_schedule,
						    get_constant_schedule_with_warmup,
						    get_cosine_schedule_with_warmup,
						    get_cosine_with_hard_restarts_schedule_with_warmup,
						    get_inverse_sqrt_schedule,
						    get_linear_schedule_with_warmup,
						    get_polynomial_decay_schedule_with_warmup,
						)







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=10    ):
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       []
						for _ in range(_UpperCAmelCase    ):
												lrs.append(scheduler.get_lr()[0]    )
												scheduler.step()
						return lrs







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=10    ):
						SCREAMING_SNAKE_CASE_: List[str]  		=       []
						for step in range(_UpperCAmelCase    ):
												lrs.append(scheduler.get_lr()[0]    )
												scheduler.step()
												if step == num_steps // 2:
																		with tempfile.TemporaryDirectory() as tmpdirname:
																								SCREAMING_SNAKE_CASE_: Optional[int]  		=       os.path.join(_UpperCAmelCase				,	"schedule.bin"    )
																								torch.save(scheduler.state_dict()				,	_UpperCAmelCase    )

																								SCREAMING_SNAKE_CASE_: Optional[Any]  		=       torch.load(_UpperCAmelCase    )
																								scheduler.load_state_dict(_UpperCAmelCase    )
						return lrs



@require_torch
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     Dict   ,						lowerCAmelCase__  :     Tuple):
									self.assertEqual(len(lowerCAmelCase__)   ,						len(lowerCAmelCase__))
									for a, b in zip(lowerCAmelCase__   ,						lowerCAmelCase__):
															self.assertAlmostEqual(lowerCAmelCase__   ,						lowerCAmelCase__   ,						delta=lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.tensor([0.1, -0.2, -0.1]   ,						requires_grad=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: str  		=       torch.tensor([0.4, 0.2, -0.5])
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       nn.MSELoss()
									# No warmup, constant schedule, no gradient clipping
									SCREAMING_SNAKE_CASE_: int  		=       AdamW(params=[w]   ,						lr=2E-1   ,						weight_decay=0.0)
									for _ in range(100):
															SCREAMING_SNAKE_CASE_: Dict  		=       criterion(lowerCAmelCase__   ,						lowerCAmelCase__)
															loss.backward()
															optimizer.step()
															w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
															w.grad.zero_()
									self.assertListAlmostEqual(w.tolist()   ,						[0.4, 0.2, -0.5]   ,						tol=1E-2)




			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.tensor([0.1, -0.2, -0.1]   ,						requires_grad=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: int  		=       torch.tensor([0.4, 0.2, -0.5])
									SCREAMING_SNAKE_CASE_: Any  		=       nn.MSELoss()
									# No warmup, constant schedule, no gradient clipping
									SCREAMING_SNAKE_CASE_: int  		=       Adafactor(
									    params=[w]   ,						lr=1E-2   ,						eps=(1E-30, 1E-3)   ,						clip_threshold=1.0   ,						decay_rate=-0.8   ,						betaa=lowerCAmelCase__   ,						weight_decay=0.0   ,						relative_step=lowerCAmelCase__   ,						scale_parameter=lowerCAmelCase__   ,						warmup_init=lowerCAmelCase__   ,						)
									for _ in range(1000):
															SCREAMING_SNAKE_CASE_: List[Any]  		=       criterion(lowerCAmelCase__   ,						lowerCAmelCase__)
															loss.backward()
															optimizer.step()
															w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
															w.grad.zero_()
									self.assertListAlmostEqual(w.tolist()   ,						[0.4, 0.2, -0.5]   ,						tol=1E-2)







@require_torch
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							Union[str, Any]										=     nn.Linear(50		,       50	) if is_torch_available() else None
			_UpperCAmelCase   :							List[Any]										=     AdamW(m.parameters()		,       lr=10.0	) if is_torch_available() else None
			_UpperCAmelCase   :							Optional[Any]										=     10



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     Optional[Any]=None):
									self.assertEqual(len(lowerCAmelCase__)   ,						len(lowerCAmelCase__))
									for a, b in zip(lowerCAmelCase__   ,						lowerCAmelCase__):
															self.assertAlmostEqual(lowerCAmelCase__   ,						lowerCAmelCase__   ,						delta=lowerCAmelCase__   ,						msg=lowerCAmelCase__)




			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_: Dict  		=       {"num_warmup_steps": 2, "num_training_steps": 10}
									# schedulers doct format
									# function: (sched_args_dict, expected_learning_rates)
									SCREAMING_SNAKE_CASE_: Dict  		=       {
									    get_constant_schedule: ({}, [10.0] * self.num_steps),
									    get_constant_schedule_with_warmup: (
									        {"num_warmup_steps": 4},
									        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
									    ),
									    get_linear_schedule_with_warmup: (
									        {**common_kwargs},
									        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
									    ),
									    get_cosine_schedule_with_warmup: (
									        {**common_kwargs},
									        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
									    ),
									    get_cosine_with_hard_restarts_schedule_with_warmup: (
									        {**common_kwargs, "num_cycles": 2},
									        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
									    ),
									    get_polynomial_decay_schedule_with_warmup: (
									        {**common_kwargs, "power": 2.0, "lr_end": 1E-7},
									        [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
									    ),
									    get_inverse_sqrt_schedule: (
									        {"num_warmup_steps": 2},
									        [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
									    ),
									}

									for scheduler_func, data in scheds.items():
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       data

															SCREAMING_SNAKE_CASE_: List[Any]  		=       scheduler_func(self.optimizer   ,						**lowerCAmelCase__)
															self.assertEqual(len([scheduler.get_lr()[0]])   ,						1)
															SCREAMING_SNAKE_CASE_: int  		=       unwrap_schedule(lowerCAmelCase__   ,						self.num_steps)
															self.assertListAlmostEqual(
															    lowerCAmelCase__   ,						lowerCAmelCase__   ,						tol=1E-2   ,						msg=F"failed for {scheduler_func} in normal scheduler"   ,						)

															SCREAMING_SNAKE_CASE_: List[str]  		=       scheduler_func(self.optimizer   ,						**lowerCAmelCase__)
															if scheduler_func.__name__ != "get_constant_schedule":
																					LambdaScheduleWrapper.wrap_scheduler(lowerCAmelCase__)  # wrap to test picklability of the schedule
															SCREAMING_SNAKE_CASE_: Tuple  		=       unwrap_and_save_reload_schedule(lowerCAmelCase__   ,						self.num_steps)
															self.assertListEqual(lowerCAmelCase__   ,						lowerCAmelCase__   ,						msg=F"failed for {scheduler_func} in save and reload")







class   __lowercase				:




			"""simple docstring"""







			def __init__( self  :     str   ,						lowerCAmelCase__  :     List[str]):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       fn



			def __call__( self  :     Optional[int]   ,						*lowerCAmelCase__  :     List[Any]   ,						**lowerCAmelCase__  :     Tuple):
									return self.fn(*lowerCAmelCase__   ,						**lowerCAmelCase__)




			@classmethod
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     str):
									SCREAMING_SNAKE_CASE_: str  		=       list(map(self   ,						scheduler.lr_lambdas))






