



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__	(					lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Optional[int]=10):





				'''simple docstring'''

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






def 			lowerCAmelCase__	(					lowerCamelCase_	:	str ,lowerCamelCase_	:	Optional[Any]=10):





				'''simple docstring'''

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

																lowerCAmelCase__  :       int				=  torch.load(lowerCamelCase_)
																scheduler.load_state_dict(lowerCamelCase_)
				return lrs



@require_torch
class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    Optional[int]:

						"""simple docstring"""






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



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

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  torch.tensor([0.1, -0.2, -0.1]	,requires_grad=__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  torch.tensor([0.4, 0.2, -0.5]				)
						lowerCAmelCase__  :       List[str]				=  nn.MSELoss()
						# No warmup, constant schedule, no gradient clipping
						lowerCAmelCase__  :       Dict				=  AdamW(params=[w]	,lr=2e-1	,weight_decay=0.0				)
						for _ in range(1_00				):
										lowerCAmelCase__  :       Optional[int]				=  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:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  torch.tensor([0.1, -0.2, -0.1]	,requires_grad=__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[Any]				=  torch.tensor([0.4, 0.2, -0.5]				)
						lowerCAmelCase__  :       List[str]				=  nn.MSELoss()
						# No warmup, constant schedule, no gradient clipping
						lowerCAmelCase__  :       List[str]				=  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(10_00				):
										lowerCAmelCase__  :       Tuple				=  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'''



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



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase=None				)  ->    Any:

						"""simple docstring"""






						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:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  {'''num_warmup_steps''': 2, '''num_training_steps''': 10}
						# schedulers doct format
						# function: (sched_args_dict, expected_learning_rates)
						lowerCAmelCase__  :       Optional[Any]				=  {
						    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__  :       List[Any]				=  data

										lowerCAmelCase__  :       str				=  scheduler_func(self.optimizer	,**__lowerCamelCase				)
										self.assertEqual(len([scheduler.get_lr()[0]]				)	,1				)
										lowerCAmelCase__  :       Union[str, Any]				=  unwrap_schedule(__lowerCamelCase	,self.num_steps				)
										self.assertListAlmostEqual(
										    __lowerCamelCase	,__lowerCamelCase	,tol=1e-2	,msg=f"""failed for {scheduler_func} in normal scheduler"""	,)

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

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  fn



		def __call__(self	,*__lowerCamelCase	,**__lowerCamelCase				)  ->    Tuple:

						"""simple docstring"""






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



		@classmethod
		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    List[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  list(map(self	,scheduler.lr_lambdas				)				)






from __future__ import annotations






def 			lowerCAmelCase__	(					lowerCamelCase_	:	list[int]):





				'''simple docstring'''

				return len(set(lowerCamelCase_)) == len(lowerCamelCase_)


if __name__ == "__main__":
							import doctest

							doctest.testmod()

