




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  UpperCAmelCase_							(				__UpperCAmelCase						:     Tuple      ,      __UpperCAmelCase						:     List[Any]=10							) ->			Tuple:
      SCREAMING_SNAKE_CASE_     						=      []
      for _ in range(__UpperCAmelCase							):
            lrs.append(scheduler.get_lr()[0]							)
            scheduler.step()
      return lrs

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Tuple      ,      __UpperCAmelCase						:     Tuple=10							) ->			Tuple:
      SCREAMING_SNAKE_CASE_     						=      []
      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_     						=      os.path.join(__UpperCAmelCase      ,      'schedule.bin'							)
                        torch.save(scheduler.state_dict()      ,      __UpperCAmelCase							)

                        SCREAMING_SNAKE_CASE_     						=      torch.load(__UpperCAmelCase							)
                        scheduler.load_state_dict(__UpperCAmelCase							)
      return lrs





@require_torch
class 	lowerCamelCase_							(				unittest.TestCase ):


 '''simple docstring'''







 def 				lowerCAmelCase_  (							self  :		List[Any]    ,    _lowerCAmelCase  :		Optional[int]    ,    _lowerCAmelCase  :		List[Any]    ,    _lowerCAmelCase  :		str						):
       self.assertEqual(len(_lowerCAmelCase						)    ,    len(_lowerCAmelCase						)						)
       for a, b in zip(_lowerCAmelCase    ,    _lowerCAmelCase						):
             self.assertAlmostEqual(_lowerCAmelCase    ,    _lowerCAmelCase    ,    delta=_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Dict						):
       SCREAMING_SNAKE_CASE_     						=      torch.tensor([0.1, -0.2, -0.1]    ,    requires_grad=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      torch.tensor([0.4, 0.2, -0.5]						)
       SCREAMING_SNAKE_CASE_     						=      nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       SCREAMING_SNAKE_CASE_     						=      AdamW(params=[w]    ,    lr=2E-1    ,    weight_decay=0.0						)
       for _ in range(100						):
             SCREAMING_SNAKE_CASE_     						=      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 				lowerCAmelCase_  (							self  :		Tuple						):
       SCREAMING_SNAKE_CASE_     						=      torch.tensor([0.1, -0.2, -0.1]    ,    requires_grad=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      torch.tensor([0.4, 0.2, -0.5]						)
       SCREAMING_SNAKE_CASE_     						=      nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       SCREAMING_SNAKE_CASE_     						=      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(1_000						):
             SCREAMING_SNAKE_CASE_     						=      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 	lowerCamelCase_							(				unittest.TestCase ):


 '''simple docstring'''







 lowercase_       =   nn.Linear(50	,       50 ) if is_torch_available() else None
 lowercase_       =   AdamW(m.parameters()	,       lr=1_0.0 ) if is_torch_available() else None
 lowercase_       =   10



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




 def 				lowerCAmelCase_  (							self  :		int						):
       SCREAMING_SNAKE_CASE_     						=      {'num_warmup_steps': 2, 'num_training_steps': 10}
       # schedulers doct format
       # function: (sched_args_dict, expected_learning_rates)
       SCREAMING_SNAKE_CASE_     						=      {
           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_     						=      data

             SCREAMING_SNAKE_CASE_     						=      scheduler_func(self.optimizer    ,    **_lowerCAmelCase						)
             self.assertEqual(len([scheduler.get_lr()[0]]						)    ,    1						)
             SCREAMING_SNAKE_CASE_     						=      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_     						=      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_     						=      unwrap_and_save_reload_schedule(_lowerCAmelCase    ,    self.num_steps						)
             self.assertListEqual(_lowerCAmelCase    ,    _lowerCAmelCase    ,    msg=F"failed for {scheduler_func} in save and reload"						)





class 	lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		Tuple    ,    _lowerCAmelCase  :		List[str]						):
       SCREAMING_SNAKE_CASE_     						=      fn



 def __call__(							self  :		Union[str, Any]    ,    *_lowerCAmelCase  :		Optional[int]    ,    **_lowerCAmelCase  :		str						):
       return self.fn(*_lowerCAmelCase    ,    **_lowerCAmelCase						)




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





def  UpperCAmelCase_							(				__UpperCAmelCase						:     str							) ->			list:
      return [
          txt[:a] + txt[a].upper() + txt[a + 1 :]
          for a in range(len(__UpperCAmelCase							)							)
          if txt[a].isalpha()
      ]


if __name__ == "__main__":
   __import__('doctest').testmod()