


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					( UpperCamelCase__:  Optional[int]				,       UpperCamelCase__:  Optional[Any]=10    )			-> Optional[int]:





	"""simple docstring"""







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





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[Any]				,       UpperCamelCase__:  Union[str, Any]=10    )			-> List[str]:





	"""simple docstring"""







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

				A             =					torch.load(_snake_case    )
				scheduler.load_state_dict(_snake_case    )
	return lrs






@require_torch
class    _UpperCamelCase     (       unittest.TestCase					):

				"""simple docstring"""







				def   _UpperCAmelCase       (    self					, a__					, a__					, a__ )						->					Union[str, Any]:
					self.assertEqual(len(_UpperCAmelCase )					, len(_UpperCAmelCase ) )
					for a, b in zip(_UpperCAmelCase					, _UpperCAmelCase ):
						self.assertAlmostEqual(_UpperCAmelCase					, _UpperCAmelCase					, delta=_UpperCAmelCase )

				def   _UpperCAmelCase       (    self )						->					str:
					A             =					torch.tensor([0.1, -0.2, -0.1]					, requires_grad=_UpperCAmelCase )
					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(_UpperCAmelCase					, _UpperCAmelCase )
						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 )						->					Optional[Any]:
					A             =					torch.tensor([0.1, -0.2, -0.1]					, requires_grad=_UpperCAmelCase )
					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=_UpperCAmelCase					, weight_decay=0.0					, relative_step=_UpperCAmelCase					, scale_parameter=_UpperCAmelCase					, warmup_init=_UpperCAmelCase					, )
					for _ in range(1000 ):
						A             =					criterion(_UpperCAmelCase					, _UpperCAmelCase )
						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    _UpperCamelCase     (       unittest.TestCase					):

				"""simple docstring"""







				lowerCAmelCase					=			nn.Linear(5_0     ,		5_0					) if is_torch_available() else None
				lowerCAmelCase					=			AdamW(m.parameters()     ,		lr=10.0					) if is_torch_available() else None
				lowerCAmelCase					=			1_0

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__					, a__=None )						->					List[str]:
					self.assertEqual(len(_UpperCAmelCase )					, len(_UpperCAmelCase ) )
					for a, b in zip(_UpperCAmelCase					, _UpperCAmelCase ):
						self.assertAlmostEqual(_UpperCAmelCase					, _UpperCAmelCase					, delta=_UpperCAmelCase					, msg=_UpperCAmelCase )

				def   _UpperCAmelCase       (    self )						->					str:
					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, 7.6_56, 5.6_25, 3.9_06, 2.5, 1.4_06, 0.6_25, 0.1_56],
					    ),
					    get_inverse_sqrt_schedule: (
					        {"""num_warmup_steps""": 2},
					        [0.0, 5.0, 10.0, 8.1_65, 7.0_71, 6.3_25, 5.7_74, 5.3_45, 5.0, 4.7_14],
					    ),
					}

					for scheduler_func, data in scheds.items():
						A       ,      A             =					data

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

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






class    _UpperCamelCase     :

				"""simple docstring"""







				def __init__(    self					, a__ )						->					int:
					A             =					fn

				def __call__(    self					, *a__					, **a__ )						->					Any:
					return self.fn(*_UpperCAmelCase					, **_UpperCAmelCase )

				@classmethod
				def   _UpperCAmelCase       (    self					, a__ )						->					Tuple:
					A             =					list(map(self					, scheduler.lr_lambdas ) )









"""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 		_snake_case	(   _snake_case				:   Optional[int]							,    _snake_case				:   Optional[Any]=10				)       ->   Optional[int]:






      '''simple docstring'''


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






def 		_snake_case	(   _snake_case				:   Optional[Any]							,    _snake_case				:   Union[str, Any]=10				)       ->   List[str]:






      '''simple docstring'''


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

                        _A     =  torch.load(_snake_case				)
                        scheduler.load_state_dict(_snake_case				)
      return lrs





@require_torch
class 							lowercase_			(	unittest.TestCase				):



 '''simple docstring'''

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

 def      lowerCAmelCase_				(					self			:			Any						):
       _A     =  torch.tensor([0.1, -0.2, -0.1]       ,				requires_grad=_UpperCAmelCase						)
       _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(_UpperCAmelCase       ,				_UpperCAmelCase						)
             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			:			int						):
       _A     =  torch.tensor([0.1, -0.2, -0.1]       ,				requires_grad=_UpperCAmelCase						)
       _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-3_0, 1E-3)       ,				clip_threshold=1.0       ,				decay_rate=-0.8       ,				betaa=_UpperCAmelCase       ,				weight_decay=0.0       ,				relative_step=_UpperCAmelCase       ,				scale_parameter=_UpperCAmelCase       ,				warmup_init=_UpperCAmelCase       ,				)
       for _ in range(1_000						):
             _A     =  criterion(_UpperCAmelCase       ,				_UpperCAmelCase						)
             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 							lowercase_			(	unittest.TestCase				):



 '''simple docstring'''
 UpperCAmelCase       :	List[str]     =      nn.Linear(50     ,      50				) if is_torch_available() else None
 UpperCAmelCase       :	Tuple     =      AdamW(m.parameters()     ,      lr=10.0				) if is_torch_available() else None
 UpperCAmelCase       :	Dict     =      10

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



 def      lowerCAmelCase_				(					self			:			List[Any]						):
       _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       ,				**_UpperCAmelCase						)
             self.assertEqual(len([scheduler.get_lr()[0]]						)       ,				1						)
             _A     =  unwrap_schedule(_UpperCAmelCase       ,				self.num_steps						)
             self.assertListAlmostEqual(
                 _UpperCAmelCase       ,				_UpperCAmelCase       ,				tol=1E-2       ,				msg=F'''failed for {scheduler_func} in normal scheduler'''       ,				)

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





class 							lowercase_			:



 '''simple docstring'''

 def __init__(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			Optional[int]						):
       _A     =  fn

 def __call__(					self			:			Tuple       ,				*_UpperCAmelCase			:			List[str]       ,				**_UpperCAmelCase			:			List[str]						):
       return self.fn(*_UpperCAmelCase       ,				**_UpperCAmelCase						)



 @classmethod
 def      lowerCAmelCase_				(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			Any						):
       _A     =  list(map(self       ,				scheduler.lr_lambdas						)						)

