






"""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       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=10    ):




		"""simple docstring"""


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


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=10    ):




		"""simple docstring"""


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

								UpperCamelCase   =	torch.load(lowerCAmelCase_    )
								scheduler.load_state_dict(lowerCAmelCase_    )
		return lrs





@require_torch
class        _lowerCamelCase      (       unittest.TestCase ):


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


						def     snake_case_   (self		) ->   Union[str, Any]:
								UpperCamelCase   =	torch.tensor([0.1, -0.2, -0.1]   ,							requires_grad=lowercase__		)
								UpperCamelCase   =	torch.tensor([0.4, 0.2, -0.5]		)
								UpperCamelCase   =	nn.MSELoss()
								# No warmup, constant schedule, no gradient clipping
								UpperCamelCase   =	AdamW(params=[w]   ,							lr=2e-1   ,							weight_decay=0.0		)
								for _ in range(1_00		):
										UpperCamelCase   =	criterion(lowercase__   ,							lowercase__		)
										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     snake_case_   (self		) ->   str:
								UpperCamelCase   =	torch.tensor([0.1, -0.2, -0.1]   ,							requires_grad=lowercase__		)
								UpperCamelCase   =	torch.tensor([0.4, 0.2, -0.5]		)
								UpperCamelCase   =	nn.MSELoss()
								# No warmup, constant schedule, no gradient clipping
								UpperCamelCase   =	Adafactor(
								    params=[w]   ,							lr=1e-2   ,							eps=(1e-3_0, 1e-3)   ,							clip_threshold=1.0   ,							decay_rate=-0.8   ,							betaa=lowercase__   ,							weight_decay=0.0   ,							relative_step=lowercase__   ,							scale_parameter=lowercase__   ,							warmup_init=lowercase__   ,							)
								for _ in range(10_00		):
										UpperCamelCase   =	criterion(lowercase__   ,							lowercase__		)
										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 ):
						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     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							__a=None		) ->   Optional[int]:
								self.assertEqual(len(lowercase__		)   ,							len(lowercase__		)		)
								for a, b in zip(lowercase__   ,							lowercase__		):
										self.assertAlmostEqual(lowercase__   ,							lowercase__   ,							delta=lowercase__   ,							msg=lowercase__		)




						def     snake_case_   (self		) ->   Dict:
								UpperCamelCase   =	{'''num_warmup_steps''': 2, '''num_training_steps''': 10}
								# schedulers doct format
								# function: (sched_args_dict, expected_learning_rates)
								UpperCamelCase   =	{
								    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():
										UpperCamelCase   =	data

										UpperCamelCase   =	scheduler_func(self.optimizer   ,							**lowercase__		)
										self.assertEqual(len([scheduler.get_lr()[0]]		)   ,							1		)
										UpperCamelCase   =	unwrap_schedule(lowercase__   ,							self.num_steps		)
										self.assertListAlmostEqual(
										    lowercase__   ,							lowercase__   ,							tol=1e-2   ,							msg=F"failed for {scheduler_func} in normal scheduler"   ,							)

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





class        _lowerCamelCase      :


						def __init__(self   ,							__a		) ->   str:
								UpperCamelCase   =	fn


						def __call__(self   ,							*__a   ,							**__a		) ->   str:
								return self.fn(*lowercase__   ,							**lowercase__		)




						@classmethod
						def     snake_case_   (self   ,							__a		) ->   Dict:
								UpperCamelCase   =	list(map(self   ,							scheduler.lr_lambdas		)		)













"""simple docstring"""





def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		return number & 1 == 0


if __name__ == "__main__":
						import doctest

						doctest.testmod()






