





"""simple docstring"""
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 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Tuple  ,     _UpperCAmelCase					:    Optional[int]=10			):
 lowerCAmelCase    				= []
 for _ in range(_UpperCAmelCase			):
  lrs.append(scheduler.get_lr()[0]			)
  scheduler.step()
 return lrs

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

    lowerCAmelCase    				= torch.load(_UpperCAmelCase			)
    scheduler.load_state_dict(_UpperCAmelCase			)
 return lrs







@require_torch
class       a							(   unittest.TestCase  ):



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  self.assertEqual(len(_SCREAMING_SNAKE_CASE     )				,       len(_SCREAMING_SNAKE_CASE     )     )
  for a, b in zip(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE     ):
   self.assertAlmostEqual(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       delta=_SCREAMING_SNAKE_CASE     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= torch.tensor([0.1, -0.2, -0.1]				,       requires_grad=_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase    				= torch.tensor([0.4, 0.2, -0.5]     )
  lowerCAmelCase    				= nn.MSELoss()
  # No warmup, constant schedule, no gradient clipping
  lowerCAmelCase    				= AdamW(params=[w]				,       lr=2E-1				,       weight_decay=0.0     )
  for _ in range(1_00     ):
   lowerCAmelCase    				= criterion(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE     )
   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    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= torch.tensor([0.1, -0.2, -0.1]				,       requires_grad=_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase    				= torch.tensor([0.4, 0.2, -0.5]     )
  lowerCAmelCase    				= nn.MSELoss()
  # No warmup, constant schedule, no gradient clipping
  lowerCAmelCase    				= Adafactor(
      params=[w]				,       lr=1E-2				,       eps=(1E-30, 1E-3)				,       clip_threshold=1.0				,       decay_rate=-0.8				,       betaa=_SCREAMING_SNAKE_CASE				,       weight_decay=0.0				,       relative_step=_SCREAMING_SNAKE_CASE				,       scale_parameter=_SCREAMING_SNAKE_CASE				,       warmup_init=_SCREAMING_SNAKE_CASE				,       )
  for _ in range(10_00     ):
   lowerCAmelCase    				= criterion(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE     )
   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       a							(   unittest.TestCase  ):
 snake_case__             =       nn.Linear(5_0   , 5_0  ) if is_torch_available() else None
 snake_case__             =       AdamW(m.parameters()   , lr=10.0  ) if is_torch_available() else None
 snake_case__             =       1_0



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case=None     ):







  """simple docstring"""






  self.assertEqual(len(_SCREAMING_SNAKE_CASE     )				,       len(_SCREAMING_SNAKE_CASE     )     )
  for a, b in zip(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE     ):
   self.assertAlmostEqual(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       delta=_SCREAMING_SNAKE_CASE				,       msg=_SCREAMING_SNAKE_CASE     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= {'num_warmup_steps': 2, 'num_training_steps': 10}
  # schedulers doct format
  # function: (sched_args_dict, expected_learning_rates)
  lowerCAmelCase    				= {
      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():
   lowerCAmelCase				,lowerCAmelCase    				= data

   lowerCAmelCase    				= scheduler_func(self.optimizer				,       **_SCREAMING_SNAKE_CASE     )
   self.assertEqual(len([scheduler.get_lr()[0]]     )				,       1     )
   lowerCAmelCase    				= unwrap_schedule(_SCREAMING_SNAKE_CASE				,       self.num_steps     )
   self.assertListAlmostEqual(
       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       tol=1E-2				,       msg=F'failed for {scheduler_func} in normal scheduler'				,       )

   lowerCAmelCase    				= scheduler_func(self.optimizer				,       **_SCREAMING_SNAKE_CASE     )
   if scheduler_func.__name__ != "get_constant_schedule":
    LambdaScheduleWrapper.wrap_scheduler(_SCREAMING_SNAKE_CASE     )  # wrap to test picklability of the schedule
   lowerCAmelCase    				= unwrap_and_save_reload_schedule(_SCREAMING_SNAKE_CASE				,       self.num_steps     )
   self.assertListEqual(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       msg=F'failed for {scheduler_func} in save and reload'     )







class       a							:



 def __init__(     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= fn



 def __call__(     self				,       *_snake_case				,       **_snake_case     ):







  """simple docstring"""






  return self.fn(*_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE     )



 @classmethod
 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= list(map(self				,       scheduler.lr_lambdas     )     )








'''simple docstring'''



import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       Union[str, Any]				=     (DDPMParallelScheduler,)

  def   _SCREAMING_SNAKE_CASE				(				self      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							{
          'num_train_timesteps': 10_00,
          'beta_start': 0.0_001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
          'variance_type': 'fixed_small',
          'clip_sample': True,
      }

      config.update(**_SCREAMING_SNAKE_CASE	)
      return config

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for timesteps in [1, 5, 1_00, 10_00]:
          self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]      , [0.002, 0.02, 0.2, 2]	):
          self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE      , beta_end=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for variance in ["fixed_small", "fixed_large", "other"]:
          self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for clip_sample in [True, False]:
          self.check_over_configs(clip_sample=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE	)
      for threshold in [0.5, 1.0, 2.0]:
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(
                  thresholding=_SCREAMING_SNAKE_CASE      , prediction_type=_SCREAMING_SNAKE_CASE      , sample_max_value=_SCREAMING_SNAKE_CASE      , )

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for t in [0, 5_00, 9_99]:
          self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      assert torch.sum(torch.abs(scheduler._get_variance(0	) - 0.0	)	) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_87	) - 0.00_979	)	) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_99	) - 0.02	)	) < 1e-5

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							self.dummy_model()
      lowerCAmelCase 						=							self.dummy_sample_deter
      lowerCAmelCase 						=							self.dummy_sample_deter + 0.1
      lowerCAmelCase 						=							self.dummy_sample_deter - 0.1

      lowerCAmelCase 						=							samplea.shape[0]
      lowerCAmelCase 						=							torch.stack([samplea, samplea, samplea]      , dim=0	)
      lowerCAmelCase 						=							torch.arange(_SCREAMING_SNAKE_CASE	)[0:3, None].repeat(1      , _SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							model(samples.flatten(0      , 1	)      , timesteps.flatten(0      , 1	)	)
      lowerCAmelCase 						=							scheduler.batch_step_no_noise(_SCREAMING_SNAKE_CASE      , timesteps.flatten(0      , 1	)      , samples.flatten(0      , 1	)	)

      lowerCAmelCase 						=							torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_sum.item() - 1_153.1_833	) < 1e-2
      assert abs(result_mean.item() - 0.5_005	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

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

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

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

          lowerCAmelCase 						=							pred_prev_sample

      lowerCAmelCase 						=							torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_sum.item() - 258.9_606	) < 1e-2
      assert abs(result_mean.item() - 0.3_372	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config(prediction_type='v_prediction'	)
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

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

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

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

          lowerCAmelCase 						=							pred_prev_sample

      lowerCAmelCase 						=							torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_sum.item() - 202.0_296	) < 1e-2
      assert abs(result_mean.item() - 0.2_631	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

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

      scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							scheduler.timesteps

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

          lowerCAmelCase 						=							scheduler.previous_timestep(_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							prev_t.item()

          self.assertEqual(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

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

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							[1_00, 87, 50, 1, 0]
      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

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


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							[scheduler.config.num_train_timesteps]

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





