




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     (				lowercase      :			Any      ,				lowercase      :			Optional[int]=10	):


      '''simple docstring'''



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

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Union[str, Any]      ,				lowercase      :			Any=10	):


      '''simple docstring'''



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

                        lowerCamelCase_							=       torch.load(lowercase	)
                        scheduler.load_state_dict(lowercase	)
      return lrs







@require_torch
class     A(		unittest.TestCase  ):



 '''simple docstring'''


 def 							a__					(							self   :    str		,				A_   :    List[str]		,				A_   :    str		,				A_   :    Any    )  ->      Dict:

       """simple docstring"""






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

 def 							a__					(							self   :    List[Any]    )  ->      Any:

       """simple docstring"""






       lowerCamelCase_							=       torch.tensor([0.1, -0.2, -0.1]		,				requires_grad=A_    )
       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(100    ):
             lowerCamelCase_							=       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   :    Optional[int]    )  ->      int:

       """simple docstring"""






       lowerCamelCase_							=       torch.tensor([0.1, -0.2, -0.1]		,				requires_grad=A_    )
       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=A_		,				weight_decay=0.0		,				relative_step=A_		,				scale_parameter=A_		,				warmup_init=A_		,				)
       for _ in range(1000    ):
             lowerCamelCase_							=       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'''


 UpperCamelCase		  =		nn.Linear(50				, 50  ) if is_torch_available() else None
 UpperCamelCase		  =		AdamW(m.parameters()				, lr=10.0  ) if is_torch_available() else None
 UpperCamelCase		  =		10

 def 							a__					(							self   :    Optional[int]		,				A_   :    List[str]		,				A_   :    Tuple		,				A_   :    Optional[int]		,				A_   :    Dict=None    )  ->      List[str]:

       """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   :    Dict    )  ->      Union[str, Any]:

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

             lowerCamelCase_							=       scheduler_func(self.optimizer		,				**A_    )
             if scheduler_func.__name__ != "get_constant_schedule":
                   LambdaScheduleWrapper.wrap_scheduler(A_    )  # wrap to test picklability of the schedule
             lowerCamelCase_							=       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   :    Optional[Any]		,				A_   :    List[str]    )  ->      Any:

       """simple docstring"""






       lowerCamelCase_							=       fn

 def __call__(							self   :    List[str]		,				*A_   :    List[Any]		,				**A_   :    Tuple    )  ->      Any:

       """simple docstring"""






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





 @classmethod
 def 							a__					(							self   :    Dict		,				A_   :    Optional[Any]    )  ->      int:

       """simple docstring"""






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








def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:list				)  ->     bool:
   if not isinstance(_snake_case	,   _snake_case				):
      raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''				)
   if len(_snake_case				) == 0:
      raise ValueError('''Input list must be a non empty list'''				)
   if len(_snake_case				) == 1:
      return True
   _A      =       series[1] - series[0]
   for index in range(len(_snake_case				) - 1				):
      if series[index + 1] - series[index] != common_diff:
         return False
   return True

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:list				)  ->     float:
   if not isinstance(_snake_case	,   _snake_case				):
      raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''				)
   if len(_snake_case				) == 0:
      raise ValueError('''Input list must be a non empty list'''				)
   _A      =       0
   for val in series:
      answer += val
   return answer / len(_snake_case				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



