




'''simple docstring'''







import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest


class 			_a  (	__a ):
       __a				:     Any   				=					(PNDMScheduler,)
       __a				:     Any   				=					(("""num_inference_steps""", 50),)
       def     A      (	self						:	Dict   ,				**lowercase						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		{
               '''num_train_timesteps''': 1_000,
               '''beta_start''': 0.0001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
           }

           config.update(**lowercase				)
           return config
       def     A      (	self						:	Dict   ,				lowercase						:	Dict=0   ,				**lowercase						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		dict(self.forward_default_kwargs				)
           UpperCAmelCase			       =		kwargs.pop('''num_inference_steps'''   ,				lowercase				)
           UpperCAmelCase			       =		self.dummy_sample
           UpperCAmelCase			       =		0.1 * sample
           UpperCAmelCase			       =		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

               UpperCAmelCase			       =		scheduler.step_prk(lowercase   ,				lowercase   ,				lowercase   ,				**lowercase				).prev_sample
               UpperCAmelCase			       =		new_scheduler.step_prk(lowercase   ,				lowercase   ,				lowercase   ,				**lowercase				).prev_sample

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

               UpperCAmelCase			       =		scheduler.step_plms(lowercase   ,				lowercase   ,				lowercase   ,				**lowercase				).prev_sample
               UpperCAmelCase			       =		new_scheduler.step_plms(lowercase   ,				lowercase   ,				lowercase   ,				**lowercase				).prev_sample

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



           '''simple docstring'''

           pass
       def     A      (	self						:	Any   ,				lowercase						:	Optional[Any]=0   ,				**lowercase						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		dict(self.forward_default_kwargs				)
           UpperCAmelCase			       =		kwargs.pop('''num_inference_steps'''   ,				lowercase				)
           UpperCAmelCase			       =		self.dummy_sample
           UpperCAmelCase			       =		0.1 * sample
           UpperCAmelCase			       =		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

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

               UpperCAmelCase			       =		scheduler.step_prk(lowercase   ,				lowercase   ,				lowercase   ,				**lowercase				).prev_sample
               UpperCAmelCase			       =		new_scheduler.step_prk(lowercase   ,				lowercase   ,				lowercase   ,				**lowercase				).prev_sample

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

               UpperCAmelCase			       =		scheduler.step_plms(lowercase   ,				lowercase   ,				lowercase   ,				**lowercase				).prev_sample
               UpperCAmelCase			       =		new_scheduler.step_plms(lowercase   ,				lowercase   ,				lowercase   ,				**lowercase				).prev_sample

               assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"
       def     A      (	self						:	Optional[Any]   ,				**lowercase						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.scheduler_classes[0]
           UpperCAmelCase			       =		self.get_scheduler_config(**lowercase				)
           UpperCAmelCase			       =		scheduler_class(**lowercase				)

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

           for i, t in enumerate(scheduler.prk_timesteps				):
               UpperCAmelCase			       =		model(lowercase   ,				lowercase				)
               UpperCAmelCase			       =		scheduler.step_prk(lowercase   ,				lowercase   ,				lowercase				).prev_sample

           for i, t in enumerate(scheduler.plms_timesteps				):
               UpperCAmelCase			       =		model(lowercase   ,				lowercase				)
               UpperCAmelCase			       =		scheduler.step_plms(lowercase   ,				lowercase   ,				lowercase				).prev_sample

           return sample
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		dict(self.forward_default_kwargs				)

           UpperCAmelCase			       =		kwargs.pop('''num_inference_steps'''   ,				lowercase				)

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

               UpperCAmelCase			       =		self.dummy_sample
               UpperCAmelCase			       =		0.1 * sample

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

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

               UpperCAmelCase			       =		scheduler.step_prk(lowercase   ,				0   ,				lowercase   ,				**lowercase				).prev_sample
               UpperCAmelCase			       =		scheduler.step_prk(lowercase   ,				1   ,				lowercase   ,				**lowercase				).prev_sample

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

               UpperCAmelCase			       =		scheduler.step_plms(lowercase   ,				0   ,				lowercase   ,				**lowercase				).prev_sample
               UpperCAmelCase			       =		scheduler.step_plms(lowercase   ,				1   ,				lowercase   ,				**lowercase				).prev_sample

               self.assertEqual(output_a.shape   ,				sample.shape				)
               self.assertEqual(output_a.shape   ,				output_a.shape				)
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           for timesteps in [100, 1_000]:
               self.check_over_configs(num_train_timesteps=lowercase				)
       def     A      (	self						:	Any				):



           '''simple docstring'''

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

           UpperCAmelCase			       =		self.scheduler_classes[0]
           UpperCAmelCase			       =		self.get_scheduler_config(steps_offset=1				)
           UpperCAmelCase			       =		scheduler_class(**lowercase				)
           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     A      (	self						:	Any				):



           '''simple docstring'''

           for beta_start, beta_end in zip([0.0001, 0.001]   ,				[0.002, 0.02]				):
               self.check_over_configs(beta_start=lowercase   ,				beta_end=lowercase				)
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=lowercase				)
       def     A      (	self						:	str				):



           '''simple docstring'''

           for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=lowercase				)
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           for t in [1, 5, 10]:
               self.check_over_forward(time_step=lowercase				)
       def     A      (	self						:	int				):



           '''simple docstring'''

           for t, num_inference_steps in zip([1, 5, 10]   ,				[10, 50, 100]				):
               self.check_over_forward(num_inference_steps=lowercase				)
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		27

           for scheduler_class in self.scheduler_classes:
               UpperCAmelCase			       =		self.dummy_sample
               UpperCAmelCase			       =		0.1 * sample

               UpperCAmelCase			       =		self.get_scheduler_config()
               UpperCAmelCase			       =		scheduler_class(**lowercase				)

               scheduler.set_timesteps(lowercase				)

               # 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]				):
                   UpperCAmelCase			       =		scheduler.step_prk(lowercase   ,				lowercase   ,				lowercase				).prev_sample
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           with self.assertRaises(lowercase				):
               UpperCAmelCase			       =		self.scheduler_classes[0]
               UpperCAmelCase			       =		self.get_scheduler_config()
               UpperCAmelCase			       =		scheduler_class(**lowercase				)

               scheduler.step_plms(self.dummy_sample   ,				1   ,				self.dummy_sample				).prev_sample
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.full_loop()
           UpperCAmelCase			       =		torch.sum(torch.abs(lowercase				)				)
           UpperCAmelCase			       =		torch.mean(torch.abs(lowercase				)				)

           assert abs(result_sum.item() - 198.1318				) < 1E-2
           assert abs(result_mean.item() - 0.2580				) < 1E-3
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.full_loop(prediction_type='''v_prediction'''				)
           UpperCAmelCase			       =		torch.sum(torch.abs(lowercase				)				)
           UpperCAmelCase			       =		torch.mean(torch.abs(lowercase				)				)

           assert abs(result_sum.item() - 67.3986				) < 1E-2
           assert abs(result_mean.item() - 0.0878				) < 1E-3
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.full_loop(set_alpha_to_one=lowercase   ,				beta_start=0.01				)
           UpperCAmelCase			       =		torch.sum(torch.abs(lowercase				)				)
           UpperCAmelCase			       =		torch.mean(torch.abs(lowercase				)				)

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







       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.full_loop(set_alpha_to_one=lowercase   ,				beta_start=0.01				)
           UpperCAmelCase			       =		torch.sum(torch.abs(lowercase				)				)
           UpperCAmelCase			       =		torch.mean(torch.abs(lowercase				)				)

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







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))






