



'''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 lowerCAmelCase						(__A						,			__A=10):



     """simple docstring"""



     _a											=						[]
     for _ in range(__A):
          lrs.append(scheduler.get_lr()[0])
          scheduler.step()
     return lrs




def lowerCAmelCase						(__A						,			__A=10):



     """simple docstring"""



     _a											=						[]
     for step in range(__A):
          lrs.append(scheduler.get_lr()[0])
          scheduler.step()
          if step == num_steps // 2:
               with tempfile.TemporaryDirectory() as tmpdirname:
                    _a											=						os.path.join(__A						,			'''schedule.bin''')
                    torch.save(scheduler.state_dict()						,			__A)

                    _a											=						torch.load(__A)
                    scheduler.load_state_dict(__A)
     return lrs






@require_torch
class __A    (						unittest.TestCase		):



 '''simple docstring'''







 def       a__						(self				,     A				,     A				,     A )					->  Optional[Any]:


      """simple docstring"""



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



 def       a__						(self )					->  int:


      """simple docstring"""



      _a											=						torch.tensor([0.1, -0.2, -0.1]				,     requires_grad=A )
      _a											=						torch.tensor([0.4, 0.2, -0.5] )
      _a											=						nn.MSELoss()
      # No warmup, constant schedule, no gradient clipping
      _a											=						AdamW(params=[w]				,     lr=2E-1				,     weight_decay=0.0 )
      for _ in range(100 ):
           _a											=						criterion(A				,     A )
           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       a__						(self )					->  Any:


      """simple docstring"""



      _a											=						torch.tensor([0.1, -0.2, -0.1]				,     requires_grad=A )
      _a											=						torch.tensor([0.4, 0.2, -0.5] )
      _a											=						nn.MSELoss()
      # No warmup, constant schedule, no gradient clipping
      _a											=						Adafactor(
          params=[w]				,     lr=1E-2				,     eps=(1E-30, 1E-3)				,     clip_threshold=1.0				,     decay_rate=-0.8				,     betaa=A				,     weight_decay=0.0				,     relative_step=A				,     scale_parameter=A				,     warmup_init=A				,     )
      for _ in range(1_000 ):
           _a											=						criterion(A				,     A )
           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		):



 '''simple docstring'''







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



 def       a__						(self				,     A				,     A				,     A				,     A=None )					->  Any:


      """simple docstring"""



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



 def       a__						(self )					->  Any:


      """simple docstring"""



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

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

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






class __A    :



 '''simple docstring'''







 def __init__(self				,     A )					->  Union[str, Any]:


      """simple docstring"""



      _a											=						fn



 def __call__(self				,     *A				,     **A )					->  int:


      """simple docstring"""



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



 @classmethod
 def       a__						(self				,     A )					->  Union[str, Any]:


      """simple docstring"""



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








'''simple docstring'''





def lowerCAmelCase						(__A):



     """simple docstring"""



     _a											=						set()
     # To detect a back edge, keep track of vertices currently in the recursion stack
     _a											=						set()
     return any(
         node not in visited and depth_first_search(__A						,			__A						,			__A						,			__A)
         for node in graph)




def lowerCAmelCase						(__A						,			__A						,			__A						,			__A):



     """simple docstring"""



     visited.add(__A)
     rec_stk.add(__A)

     for node in graph[vertex]:
          if node not in visited:
               if depth_first_search(__A						,			__A						,			__A						,			__A):
                    return True
          elif node in rec_stk:
               return True

    # The node needs to be removed from recursion stack before function ends
     rec_stk.remove(__A)
     return False


if __name__ == "__main__":
  from doctest import testmod

  testmod()



