text: # Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import math
  import random
  
  import omegaconf
  import pytest
  import pytorch_lightning as pl
  import torch
  import torch.optim
  
  from nemo.core import config, optim
  from nemo.core.optim.lr_scheduler import AVAILABLE_SCHEDULERS
  from nemo.core.optim.optimizers import AVAILABLE_OPTIMIZERS
  from nemo.utils import logging
  
  
  class TempModel(torch.nn.Module):
      def __init__(self):
          super(TempModel, self).__init__()
          self.layer = torch.nn.Linear(5, 1)
  
      def forward(self, x):
          x = self.layer(x)
          return x
  
  
  class OptCounter(torch.optim.SGD):
      def __init__(self, *args, **kwargs):
          self.count = 0
          super().__init__(*args, **kwargs)
  
      def step(self, closure=None):
          self.count += 1
          super().step(closure)
  
  
  class RandomDataset(torch.utils.data.Dataset):
      def __init__(self, dataset_len):
          super().__init__()
          self.__dataset_len = dataset_len
  
      def __getitem__(self, *args):
          return torch.randn(2)
  
      def __len__(self):
          return self.__dataset_len
  
  
  class ExampleModel(pl.LightningModule):
      def __init__(self, batch_size, dataset_len, drop_last, max_steps):
          super().__init__()
          self.l1 = torch.nn.modules.Linear(in_features=2, out_features=1)
          self.batch_size = batch_size
          self.dataset_len = dataset_len
          self.drop_last = drop_last
          self.max_steps = max_steps
  
      def train_dataloader(self):
          dataset = RandomDataset(self.dataset_len)
          return torch.utils.data.DataLoader(dataset, batch_size=self.batch_size, drop_last=self.drop_last)
  
      def training_step(self, batch, batch_idx):
          output = self.l1(batch)
          output = torch.nn.functional.l1_loss(output, torch.ones(output.size()).to(output.device))
          return {"loss": output}
  
      def configure_optimizers(self):
          self.my_opt = OptCounter(self.parameters(), lr=0.02)
          return self.my_opt
  
  
  class Callback(pl.callbacks.Callback):
      @pl.utilities.distributed.rank_zero_only
      def on_train_end(self, trainer, module):
          if trainer.global_step != module.my_opt.count or trainer.global_step != module.max_steps:
              logging.debug(f"max_epochs: {trainer.max_epochs}")
              logging.debug(f"accumulate_grad_batches: {trainer.accumulate_grad_batches}")
              logging.debug(f"limit_train_batches: {trainer.limit_train_batches}")
              logging.debug(f"num_processes: {trainer.num_processes}")
              logging.debug(f"batch_size: {module.batch_size}")
              logging.debug(f"dataset_len: {module.dataset_len}")
              logging.debug(f"drop_last: {module.drop_last}")
              logging.debug(f"{len(trainer.train_dataloader)}")
              logging.debug(f"{trainer.num_training_batches }")
          assert (
              trainer.global_step == module.my_opt.count
          ), f"{trainer.global_step} != {module.my_opt.count} != {module.max_steps}"
          assert (
              trainer.global_step == module.max_steps
          ), f"{trainer.global_step} != {module.my_opt.count} != {module.max_steps}"
  
  
  class TestOptimizersSchedulers:
      INITIAL_LR = 0.1
      MIN_LR = 1e-3
      MAX_STEPS = 10
  
      @pytest.mark.unit
      def test_get_optimizer(self):
          model = TempModel()
  
          for opt_name in AVAILABLE_OPTIMIZERS.keys():
              opt_cls = optim.get_optimizer(opt_name)
              opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
  
              assert isinstance(opt, AVAILABLE_OPTIMIZERS[opt_name])
  
      @pytest.mark.unit
      def test_register_optimizer(self):
          class TempOpt(torch.optim.SGD):
              pass
  
          class TempOptParams(config.optimizers.SGDParams):
              pass
  
          optim.register_optimizer('TempOpt', TempOpt, TempOptParams)
  
          model = TempModel()
          opt_cls = optim.get_optimizer('TempOpt')
          opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
  
          assert isinstance(opt, TempOpt)
  
      @pytest.mark.unit
      def test_optim_config_parse_bypass(self):
          basic_optim_config = {'weight_decay': 0.001, 'betas': [0.8, 0.5]}
          parsed_params = optim.parse_optimizer_args('novograd', basic_optim_config)
          assert parsed_params['weight_decay'] == basic_optim_config['weight_decay']
          assert parsed_params['betas'][0] == basic_optim_config['betas'][0]
          assert parsed_params['betas'][1] == basic_optim_config['betas'][1]
  
          dict_config = omegaconf.OmegaConf.create(basic_optim_config)
          parsed_params = optim.parse_optimizer_args('novograd', dict_config)
          assert parsed_params['weight_decay'] == dict_config['weight_decay']
          assert parsed_params['betas'][0] == dict_config['betas'][0]
          assert parsed_params['betas'][1] == dict_config['betas'][1]
  
      @pytest.mark.unit
      def test_optim_config_parse_arg_by_name(self):
          basic_optim_config = {'name': 'auto', 'weight_decay': 0.001, 'betas': [0.8, 0.5]}
          parsed_params = optim.parse_optimizer_args('novograd', basic_optim_config)
          assert parsed_params['weight_decay'] == basic_optim_config['weight_decay']
          assert parsed_params['betas'][0] == basic_optim_config['betas'][0]
          assert parsed_params['betas'][1] == basic_optim_config['betas'][1]
  
          dict_config = omegaconf.OmegaConf.create(basic_optim_config)
          parsed_params = optim.parse_optimizer_args('novograd', dict_config)
          assert parsed_params['weight_decay'] == dict_config['weight_decay']
          assert parsed_params['betas'][0] == dict_config['betas'][0]
          assert parsed_params['betas'][1] == dict_config['betas'][1]
  
          with pytest.raises(omegaconf.errors.ConfigKeyError):
              optim.parse_optimizer_args('sgd', dict_config)
  
      @pytest.mark.unit
      def test_optim_config_parse_arg_by_target(self):
          basic_optim_config = {
              'target': 'nemo.core.config.NovogradParams',
              'params': {'weight_decay': 0.001, 'betas': [0.8, 0.5]},
          }
          basic_optim_config = omegaconf.OmegaConf.create(basic_optim_config)
          parsed_params = optim.parse_optimizer_args('novograd', basic_optim_config)
          assert parsed_params['weight_decay'] == basic_optim_config['params']['weight_decay']
          assert parsed_params['betas'][0] == basic_optim_config['params']['betas'][0]
          assert parsed_params['betas'][1] == basic_optim_config['params']['betas'][1]
  
          dict_config = omegaconf.OmegaConf.create(basic_optim_config)
          parsed_params = optim.parse_optimizer_args('novograd', dict_config)
          assert parsed_params['weight_decay'] == dict_config['params']['weight_decay']
          assert parsed_params['betas'][0] == dict_config['params']['betas'][0]
          assert parsed_params['betas'][1] == dict_config['params']['betas'][1]
  
          # Names are ignored when passing class path
          # This will be captured during optimizer instantiation
          output_config = optim.parse_optimizer_args('sgd', dict_config)
          sgd_config = vars(config.SGDParams())
          novograd_config = vars(config.NovogradParams())
  
          assert set(output_config.keys()) != set(sgd_config.keys())
          assert set(output_config.keys()) == set(novograd_config)
  
      @pytest.mark.unit
      def test_get_scheduler(self):
          model = TempModel()
          optimizer = optim.Novograd(model.parameters(), lr=self.INITIAL_LR)
  
          for sched_name in AVAILABLE_SCHEDULERS.keys():
              sched_cls = optim.lr_scheduler.get_scheduler(sched_name)
  
              try:
                  sched = sched_cls(optimizer)
                  assert isinstance(sched, AVAILABLE_SCHEDULERS[sched_name])
                  continue
              except Exception:
                  pass
  
              try:
                  sched = sched_cls(optimizer, max_steps=self.MAX_STEPS)
                  assert isinstance(sched, AVAILABLE_SCHEDULERS[sched_name])
                  continue
              except Exception:
                  pass
  
      @pytest.mark.unit
      def test_register_scheduler(self):
          class TempSched(optim.lr_scheduler.CosineAnnealing):
              pass
  
          class TempSchedParams(config.schedulers.CosineAnnealingParams):
              pass
  
          optim.lr_scheduler.register_scheduler('TempSched', TempSched, TempSchedParams)
  
          model = TempModel()
          opt_cls = optim.get_optimizer('novograd')
          opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
          sched_cls = optim.lr_scheduler.get_scheduler('TempSched')
          sched = sched_cls(opt, max_steps=self.MAX_STEPS)
  
          assert isinstance(sched, TempSched)
  
      @pytest.mark.unit
      def test_sched_config_parse_simple(self):
          model = TempModel()
          opt_cls = optim.get_optimizer('novograd')
          opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
  
          basic_sched_config = {'name': 'CosineAnnealing', 'max_steps': 10}
          scheduler_setup = optim.lr_scheduler.prepare_lr_scheduler(opt, basic_sched_config)
          assert isinstance(scheduler_setup['scheduler'], optim.lr_scheduler.CosineAnnealing)
  
          dict_config = omegaconf.OmegaConf.create(basic_sched_config)
          scheduler_setup = optim.lr_scheduler.prepare_lr_scheduler(opt, dict_config)
          assert isinstance(scheduler_setup['scheduler'], optim.lr_scheduler.CosineAnnealing)
  
      @pytest.mark.unit
      def test_sched_config_parse_from_cls(self):
          model = TempModel()
          opt_cls = optim.get_optimizer('novograd')
          opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
  
          basic_sched_config = {
              'target': 'nemo.core.config.CosineAnnealingParams',
              'params': {'min_lr': 0.1},
              'max_steps': self.MAX_STEPS,
          }
          scheduler_setup = optim.lr_scheduler.prepare_lr_scheduler(opt, basic_sched_config)
          assert isinstance(scheduler_setup['scheduler'], optim.lr_scheduler.CosineAnnealing)
  
          dict_config = omegaconf.OmegaConf.create(basic_sched_config)
          scheduler_setup = optim.lr_scheduler.prepare_lr_scheduler(opt, dict_config)
          assert isinstance(scheduler_setup['scheduler'], optim.lr_scheduler.CosineAnnealing)
  
      @pytest.mark.unit
      def test_WarmupPolicy(self):
          model = TempModel()
          opt_cls = optim.get_optimizer('novograd')
          opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
  
          # No warmup case
          policy = optim.lr_scheduler.WarmupPolicy(opt, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
          initial_lr = policy.get_last_lr()[0]
  
          assert initial_lr == self.INITIAL_LR
  
          for i in range(self.MAX_STEPS):
              assert policy.get_last_lr()[0] == self.INITIAL_LR
              opt.step()
              policy.step()
  
          policy.step()
          final_lr = policy.get_last_lr()[0]
  
          assert final_lr == self.MIN_LR
  
          # Warmup steps available
          policy = optim.lr_scheduler.WarmupPolicy(opt, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
          initial_lr = policy.get_last_lr()[0]
  
          assert initial_lr < self.INITIAL_LR
  
          for i in range(self.MAX_STEPS):
              if i <= 4:
                  assert policy.get_last_lr()[0] <= self.INITIAL_LR
              else:
                  assert policy.get_last_lr()[0] == self.INITIAL_LR
              opt.step()
              policy.step()
  
          policy.step()
          final_lr = policy.get_last_lr()[0]
  
          assert final_lr == self.MIN_LR
  
      @pytest.mark.unit
      def test_WarmupHoldPolicy(self):
          model = TempModel()
          opt_cls = optim.get_optimizer('novograd')
          opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
  
          # No warmup case
          policy = optim.lr_scheduler.WarmupHoldPolicy(opt, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
          initial_lr = policy.get_last_lr()[0]
  
          assert initial_lr == self.INITIAL_LR
  
          for i in range(self.MAX_STEPS):
              assert policy.get_last_lr()[0] == self.INITIAL_LR
              opt.step()
              policy.step()
  
          policy.step()
          final_lr = policy.get_last_lr()[0]
  
          assert final_lr == self.MIN_LR
  
          # Warmup steps available
          policy = optim.lr_scheduler.WarmupHoldPolicy(opt, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
          initial_lr = policy.get_last_lr()[0]
  
          assert initial_lr < self.INITIAL_LR
  
          for i in range(self.MAX_STEPS):
              if i <= 4:
                  assert policy.get_last_lr()[0] <= self.INITIAL_LR
              else:
                  assert policy.get_last_lr()[0] == self.INITIAL_LR
  
              opt.step()
              policy.step()
  
          policy.step()
          final_lr = policy.get_last_lr()[0]
  
          assert final_lr == self.MIN_LR
  
          # Warmup + Hold steps available
          policy = optim.lr_scheduler.WarmupHoldPolicy(
              opt, warmup_steps=5, hold_steps=3, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
          )
          initial_lr = policy.get_last_lr()[0]
  
          assert initial_lr < self.INITIAL_LR
  
          for i in range(self.MAX_STEPS):
              if i <= 4:
                  assert policy.get_last_lr()[0] <= self.INITIAL_LR
              else:
                  assert policy.get_last_lr()[0] == self.INITIAL_LR
              opt.step()
              policy.step()
  
          policy.step()
          final_lr = policy.get_last_lr()[0]
  
          assert final_lr == self.MIN_LR
  
      @pytest.mark.unit
      def test_WarmupAnnealing(self):
          model = TempModel()
          opt_cls = optim.get_optimizer('novograd')
          opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
  
          # No warmup case
          policy = optim.lr_scheduler.WarmupAnnealing(opt, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
          initial_lr = policy.get_last_lr()[0]
  
          assert initial_lr == self.INITIAL_LR
  
          for i in range(self.MAX_STEPS):
              assert policy.get_last_lr()[0] <= self.INITIAL_LR
              opt.step()
              policy.step()
  
          policy.step()
          final_lr = policy.get_last_lr()[0]
  
          assert final_lr == self.MIN_LR
  
          # Warmup steps available
          policy = optim.lr_scheduler.WarmupAnnealing(opt, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
          initial_lr = policy.get_last_lr()[0]
  
          assert initial_lr < self.INITIAL_LR
  
          for i in range(self.MAX_STEPS):
              if i <= 5:
                  assert policy.get_last_lr()[0] <= self.INITIAL_LR
              else:
                  assert policy.get_last_lr()[0] < self.INITIAL_LR
  
              opt.step()
              policy.step()
  
          policy.step()
          final_lr = policy.get_last_lr()[0]
  
          assert final_lr == self.MIN_LR
  
          # Warmup + Hold steps available
          policy = optim.lr_scheduler.WarmupHoldPolicy(
              opt, warmup_steps=5, hold_steps=3, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
          )
          initial_lr = policy.get_last_lr()[0]
  
          assert initial_lr < self.INITIAL_LR
  
          for i in range(self.MAX_STEPS):
              if i <= 4:
                  assert policy.get_last_lr()[0] <= self.INITIAL_LR
              else:
                  assert policy.get_last_lr()[0] == self.INITIAL_LR
              opt.step()
              policy.step()
  
          policy.step()
          final_lr = policy.get_last_lr()[0]
  
          assert final_lr == self.MIN_LR
  
      @pytest.mark.unit
      def test_SquareAnnealing(self):
          model = TempModel()
          opt_cls = optim.get_optimizer('novograd')
          opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
  
          # No warmup case
          policy = optim.lr_scheduler.SquareAnnealing(opt, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
          initial_lr = policy.get_last_lr()[0]
  
          assert initial_lr == self.INITIAL_LR
  
          for i in range(self.MAX_STEPS):
              assert policy.get_last_lr()[0] <= self.INITIAL_LR
              opt.step()
              policy.step()
  
          policy.step()
          final_lr = policy.get_last_lr()[0]
  
          assert final_lr == self.MIN_LR
  
          # Warmup steps available
          policy = optim.lr_scheduler.SquareAnnealing(opt, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
          initial_lr = policy.get_last_lr()[0]
  
          assert initial_lr < self.INITIAL_LR
  
          for i in range(self.MAX_STEPS):
              if i <= 5:
                  assert policy.get_last_lr()[0] <= self.INITIAL_LR
              else:
                  assert policy.get_last_lr()[0] < self.INITIAL_LR
  
              opt.step()
              policy.step()
  
          policy.step()
          final_lr = policy.get_last_lr()[0]
  
          assert final_lr == self.MIN_LR
  
      @pytest.mark.unit
      def test_SquareRootAnnealing(self):
          model = TempModel()
          opt_cls = optim.get_optimizer('novograd')
          opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
  
          # No warmup case
          policy = optim.lr_scheduler.SquareRootAnnealing(opt, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
          initial_lr = policy.get_last_lr()[0]
  
          assert initial_lr == self.INITIAL_LR
  
          for i in range(self.MAX_STEPS):
              assert policy.get_last_lr()[0] <= self.INITIAL_LR
              opt.step()
              policy.step()
  
          policy.step()
          final_lr = policy.get_last_lr()[0]
  
          assert final_lr == self.MIN_LR
  
          # Warmup steps available
          policy = optim.lr_scheduler.SquareRootAnnealing(
              opt, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
          )
          initial_lr = policy.get_last_lr()[0]
  
          assert initial_lr < self.INITIAL_LR
  
          for i in range(self.MAX_STEPS):
              if i <= 5:
                  assert policy.get_last_lr()[0] <= self.INITIAL_LR
              else:
                  assert policy.get_last_lr()[0] < self.INITIAL_LR
  
              opt.step()
              policy.step()
  
          policy.step()
          final_lr = policy.get_last_lr()[0]
  
          assert final_lr == self.MIN_LR
  
      @pytest.mark.unit
      def test_CosineAnnealing(self):
          model = TempModel()
          opt_cls = optim.get_optimizer('novograd')
          opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
  
          # No warmup case
          policy = optim.lr_scheduler.CosineAnnealing(opt, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
          initial_lr = policy.get_last_lr()[0]
  
          assert initial_lr == self.INITIAL_LR
  
          for i in range(self.MAX_STEPS):
              assert policy.get_last_lr()[0] <= self.INITIAL_LR
              opt.step()
              policy.step()
  
          policy.step()
          final_lr = policy.get_last_lr()[0]
  
          assert final_lr == self.MIN_LR
  
          # Warmup steps available
          policy = optim.lr_scheduler.CosineAnnealing(opt, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
          initial_lr = policy.get_last_lr()[0]
  
          assert initial_lr < self.INITIAL_LR
  
          for i in range(self.MAX_STEPS):
              if i <= 5:
                  assert policy.get_last_lr()[0] <= self.INITIAL_LR
              else:
                  assert policy.get_last_lr()[0] < self.INITIAL_LR
  
              opt.step()
              policy.step()
  
          policy.step()
          final_lr = policy.get_last_lr()[0]
  
          assert final_lr == self.MIN_LR
  
      @pytest.mark.unit
      def test_PolynomialDecayAnnealing(self):
          model = TempModel()
          opt_cls = optim.get_optimizer('novograd')
          opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
  
          # No warmup case
          policy = optim.lr_scheduler.PolynomialDecayAnnealing(
              opt, power=2, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
          )
          initial_lr = policy.get_last_lr()[0]
  
          assert initial_lr == self.INITIAL_LR
  
          for i in range(self.MAX_STEPS):
              assert policy.get_last_lr()[0] <= self.INITIAL_LR
              opt.step()
              policy.step()
  
          policy.step()
          final_lr = policy.get_last_lr()[0]
  
          assert final_lr == self.MIN_LR
  
          # Warmup steps available
          policy = optim.lr_scheduler.PolynomialDecayAnnealing(
              opt, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
          )
          initial_lr = policy.get_last_lr()[0]
  
          assert initial_lr < self.INITIAL_LR
  
          for i in range(self.MAX_STEPS):
              if i <= 5:
                  assert policy.get_last_lr()[0] <= self.INITIAL_LR
              else:
                  assert policy.get_last_lr()[0] < self.INITIAL_LR
  
              opt.step()
              policy.step()
  
          policy.step()
          final_lr = policy.get_last_lr()[0]
  
          assert final_lr == self.MIN_LR
  
      @pytest.mark.unit
      def test_PolynomialHoldDecayAnnealing(self):
          model = TempModel()
          opt_cls = optim.get_optimizer('novograd')
          opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
  
          # No warmup case
          policy = optim.lr_scheduler.PolynomialHoldDecayAnnealing(
              opt, power=2, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
          )
          initial_lr = policy.get_last_lr()[0]
  
          assert initial_lr == self.INITIAL_LR
  
          for i in range(self.MAX_STEPS):
              assert policy.get_last_lr()[0] <= self.INITIAL_LR
              opt.step()
              policy.step()
  
          policy.step()
          final_lr = policy.get_last_lr()[0]
  
          assert final_lr == self.MIN_LR
  
          # Warmup steps available
          policy = optim.lr_scheduler.PolynomialHoldDecayAnnealing(
              opt, power=2, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
          )
          initial_lr = policy.get_last_lr()[0]
  
          assert initial_lr < self.INITIAL_LR
  
          for i in range(self.MAX_STEPS):
              if i <= 5:
                  assert policy.get_last_lr()[0] <= self.INITIAL_LR
              else:
                  assert policy.get_last_lr()[0] < self.INITIAL_LR
  
              opt.step()
              policy.step()
  
          policy.step()
          final_lr = policy.get_last_lr()[0]
  
          assert final_lr == self.MIN_LR
  
          # Warmup + Hold steps available
          policy = optim.lr_scheduler.PolynomialHoldDecayAnnealing(
              opt, warmup_steps=5, hold_steps=3, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR, power=2
          )
          initial_lr = policy.get_last_lr()[0]
  
          assert initial_lr < self.INITIAL_LR
  
          for i in range(self.MAX_STEPS):
              if i <= 4:
                  assert policy.get_last_lr()[0] <= self.INITIAL_LR
              elif i <= 8:
                  assert policy.get_last_lr()[0] == self.INITIAL_LR
              else:
                  assert policy.get_last_lr()[0] < self.INITIAL_LR
              opt.step()
              policy.step()
  
          policy.step()
          final_lr = policy.get_last_lr()[0]
  
          assert final_lr == self.MIN_LR
  
      @pytest.mark.unit
      def test_InverseSquareRootAnnealing(self):
          model = TempModel()
          opt_cls = optim.get_optimizer('novograd')
          opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
  
          # No warmup case
          policy = optim.lr_scheduler.InverseSquareRootAnnealing(opt, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
          initial_lr = policy.get_last_lr()[0]
  
          assert initial_lr == self.INITIAL_LR
  
          for i in range(self.MAX_STEPS):
              assert policy.get_last_lr()[0] <= self.INITIAL_LR
              opt.step()
              policy.step()
  
          policy.step()
          final_lr = policy.get_last_lr()[0]
  
          assert final_lr == self.MIN_LR
  
          # Warmup steps available
          policy = optim.lr_scheduler.InverseSquareRootAnnealing(
              opt, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
          )
          initial_lr = policy.get_last_lr()[0]
  
          assert initial_lr < self.INITIAL_LR
  
          for i in range(self.MAX_STEPS):
              if i <= 5:
                  assert policy.get_last_lr()[0] <= self.INITIAL_LR
              else:
                  assert policy.get_last_lr()[0] < self.INITIAL_LR
  
              opt.step()
              policy.step()
  
          policy.step()
          final_lr = policy.get_last_lr()[0]
  
          assert final_lr == self.MIN_LR
  
      @pytest.mark.unit
      @pytest.mark.run_only_on('CPU')
      def test_max_step_computation(self, cleanup_local_folder):
          def train(
              max_epochs, accumulate_grad_batches, limit_train_batches, num_processes, batch_size, dataset_len, drop_last
          ):
              trainer = pl.Trainer(
                  max_epochs=max_epochs,
                  accelerator="ddp_cpu",
                  num_processes=num_processes,
                  accumulate_grad_batches=accumulate_grad_batches,
                  limit_train_batches=limit_train_batches,
                  checkpoint_callback=False,
                  progress_bar_refresh_rate=0,
                  weights_summary=None,
              )
              max_steps = optim.lr_scheduler.compute_max_steps(
                  max_epochs,
                  accumulate_grad_batches,
                  limit_train_batches,
                  num_processes,
                  dataset_len,
                  batch_size,
                  drop_last,
              )
              model = ExampleModel(batch_size, dataset_len, drop_last, max_steps)
              trainer.callbacks.append(Callback())
              trainer.fit(model)
  
          # This test will break once we and lightning upgrade to pytorch 1.7.0 due to a bug fix in pytorch 1.7.0
          train(
              31,
              accumulate_grad_batches=1,
              limit_train_batches=1.0,
              num_processes=9,
              batch_size=60,
              dataset_len=1613,
              drop_last=True,
          )
          train(
              5,
              accumulate_grad_batches=1,
              limit_train_batches=0.17382691901706027,
              num_processes=4,
              batch_size=97,
              dataset_len=498,
              drop_last=False,
          )
          train(
              5,
              accumulate_grad_batches=8,
              limit_train_batches=0.1663306588594945,
              num_processes=4,
              batch_size=54,
              dataset_len=629,
              drop_last=True,
          )
          train(
              5,
              accumulate_grad_batches=1,
              limit_train_batches=0.2121376533631948,
              num_processes=1,
              batch_size=68,
              dataset_len=488,
              drop_last=False,
          )
          for _ in range(5):
              drop_last = bool(random.randint(0, 1))
              accumulate_grad_batches = random.randint(1, 10)
  
              limit_train_batches_int = random.randint(1, 10)
              limit_train_batches_float = random.uniform(0, 1)
              limit_train_batches = random.choice([limit_train_batches_int, limit_train_batches_float])
              max_epochs = random.randint(4, 20)
              num_processes = random.randint(1, 5)
              dataset_len = random.randint(20, num_processes * 500)
              batch_size = random.randint(math.ceil(5.0 / num_processes), min(dataset_len // num_processes, 128))
              train(
                  max_epochs,
                  accumulate_grad_batches,
                  limit_train_batches,
                  num_processes,
                  batch_size,
                  dataset_len,
                  drop_last,
              )
  