"""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 			UpperCAmelCase__       (				lowerCAmelCase__     :List[str]					,		lowerCAmelCase__     :List[Any]=1_0		) ->    str:
      '''simple docstring'''


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


def 			UpperCAmelCase__       (				lowerCAmelCase__     :str					,		lowerCAmelCase__     :Dict=1_0		) ->    Any:
      '''simple docstring'''


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

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





@require_torch
class  _A						(       unittest.TestCase	):

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



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

      def 				A__       (						self						):







            """simple docstring"""



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







            """simple docstring"""



            lowercase														=	torch.tensor([0.1, -0.2, -0.1]		,       requires_grad=__lowerCAmelCase						)
            lowercase														=	torch.tensor([0.4, 0.2, -0.5]						)
            lowercase														=	nn.MSELoss()
            # No warmup, constant schedule, no gradient clipping
            lowercase														=	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(1000						):
                  lowercase														=	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  _A						(       unittest.TestCase	):
      snake_case__			:   Union[str, Any]							  =	nn.Linear(50  ,    50	) if is_torch_available() else None
      snake_case__			:   Union[str, Any]							  =	AdamW(m.parameters()  ,    lr=10.0	) if is_torch_available() else None
      snake_case__			:   Dict							  =	10

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=None						):







            """simple docstring"""



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







      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	{"""num_warmup_steps""": 2, """num_training_steps""": 10}
            # schedulers doct format
            # function: (sched_args_dict, expected_learning_rates)
            lowercase														=	{
                get_constant_schedule: ({}, [1_0.0] * self.num_steps),
                get_constant_schedule_with_warmup: (
                    {"""num_warmup_steps""": 4},
                    [0.0, 2.5, 5.0, 7.5, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0],
                ),
                get_linear_schedule_with_warmup: (
                    {**common_kwargs},
                    [0.0, 5.0, 1_0.0, 8.7_5, 7.5, 6.2_5, 5.0, 3.7_5, 2.5, 1.2_5],
                ),
                get_cosine_schedule_with_warmup: (
                    {**common_kwargs},
                    [0.0, 5.0, 1_0.0, 9.6_1, 8.5_3, 6.9_1, 5.0, 3.0_8, 1.4_6, 0.3_8],
                ),
                get_cosine_with_hard_restarts_schedule_with_warmup: (
                    {**common_kwargs, """num_cycles""": 2},
                    [0.0, 5.0, 1_0.0, 8.5_3, 5.0, 1.4_6, 1_0.0, 8.5_3, 5.0, 1.4_6],
                ),
                get_polynomial_decay_schedule_with_warmup: (
                    {**common_kwargs, """power""": 2.0, """lr_end""": 1E-7},
                    [0.0, 5.0, 1_0.0, 7.6_5_6, 5.6_2_5, 3.9_0_6, 2.5, 1.4_0_6, 0.6_2_5, 0.1_5_6],
                ),
                get_inverse_sqrt_schedule: (
                    {"""num_warmup_steps""": 2},
                    [0.0, 5.0, 1_0.0, 8.1_6_5, 7.0_7_1, 6.3_2_5, 5.7_7_4, 5.3_4_5, 5.0, 4.7_1_4],
                ),
            }

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

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

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






class  _A						:

      def __init__(						self		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	fn

      def __call__(						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



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







      @classmethod
      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



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




"""simple docstring"""






from ..utils import DummyObject, requires_backends





class  _A						(       metaclass=lowerCAmelCase	):
      snake_case__			:   Optional[int]							  =	['torch', 'torchsde']

      def __init__(						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(self		,       ["""torch""", """torchsde"""]						)

      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """torchsde"""]						)







      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """torchsde"""]						)



