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



			"""simple docstring"""







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






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase=10       )->       int:



			"""simple docstring"""







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

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






@require_torch
class      __lowercase					(    unittest.TestCase    ):

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      str   )							->		Optional[Any]:


									'''simple docstring'''

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

						def        __a		(	self				:      Optional[Any]   )							->		Dict:


									'''simple docstring'''

									lowercase							=		torch.tensor([0.1, -0.2, -0.1]							,							requires_grad=UpperCAmelCase_   )
									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(1_00   ):
												lowercase							=		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        __a		(	self				:      Dict   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		torch.tensor([0.1, -0.2, -0.1]							,							requires_grad=UpperCAmelCase_   )
									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=UpperCAmelCase_							,							weight_decay=0.0							,							relative_step=UpperCAmelCase_							,							scale_parameter=UpperCAmelCase_							,							warmup_init=UpperCAmelCase_							,							)
									for _ in range(10_00   ):
												lowercase							=		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    ):
						lowercase					 =     nn.Linear(50       ,     50    ) if is_torch_available() else None
						lowercase					 =     AdamW(m.parameters()       ,     lr=10.0    ) if is_torch_available() else None
						lowercase					 =     10

						def        __a		(	self				:      int							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Any=None   )							->		Union[str, Any]:


									'''simple docstring'''

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



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


									'''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: ({}, [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():
												lowercase		,lowercase							=		data

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

												lowercase							=		scheduler_func(self.optimizer							,							**UpperCAmelCase_   )
												if scheduler_func.__name__ != "get_constant_schedule":
															LambdaScheduleWrapper.wrap_scheduler(UpperCAmelCase_   )  # wrap to test picklability of the schedule
												lowercase							=		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					:

						def __init__(	self				:      str							,							__lowerCamelCase				:      List[str]   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		fn

						def __call__(	self				:      Optional[Any]							,							*__lowerCamelCase				:      Dict							,							**__lowerCamelCase				:      int   )							->		str:


									'''simple docstring'''

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



						@classmethod
						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      Dict   )							->		List[Any]:


									'''simple docstring'''

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






import cmath
import math






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       complex:



			"""simple docstring"""







			lowercase							=		math.radians(UpperCAmelCase       )
			lowercase							=		math.radians(UpperCAmelCase       )

			# Convert voltage and current to rectangular form
			lowercase							=		cmath.rect(UpperCAmelCase,		UpperCAmelCase       )
			lowercase							=		cmath.rect(UpperCAmelCase,		UpperCAmelCase       )

			# Calculate apparent power
			return voltage_rect * current_rect


if __name__ == "__main__":
						import doctest

						doctest.testmod()





