






'''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    (  a		:	Tuple						,			a		:	Union[str, Any]=1_0							)			->			List[str]:





						"""simple docstring"""
						lowercase_      :   int        =			[]
						for _ in range(a							):
												lrs.append(scheduler.get_lr()[0]							)
												scheduler.step()
						return lrs






def 	_UpperCAmelCase    (  a		:	List[Any]						,			a		:	Union[str, Any]=1_0							)			->			Optional[int]:





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

																								lowercase_      :   Tuple        =			torch.load(a							)
																								scheduler.load_state_dict(a							)
						return lrs






@require_torch
class 							__magic_name__     (	unittest.TestCase				):







	"""simple docstring"""



	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  Optional[Any]:
							self.assertEqual(len(_lowercase       )  ,      len(_lowercase       )       )
							for a, b in zip(_lowercase  ,      _lowercase       ):
													self.assertAlmostEqual(_lowercase  ,      _lowercase  ,      delta=_lowercase       )
	def        lowerCamelCase__		(							self       )						->  int:
							lowercase_      :   int        =			torch.tensor([0.1, -0.2, -0.1]  ,      requires_grad=_lowercase       )
							lowercase_      :   List[Any]        =			torch.tensor([0.4, 0.2, -0.5]       )
							lowercase_      :   List[Any]        =			nn.MSELoss()
							# No warmup, constant schedule, no gradient clipping
							lowercase_      :   int        =			AdamW(params=[w]  ,      lr=2E-1  ,      weight_decay=0.0       )
							for _ in range(100       ):
													lowercase_      :   Optional[Any]        =			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        lowerCamelCase__		(							self       )						->  Dict:
							lowercase_      :   List[Any]        =			torch.tensor([0.1, -0.2, -0.1]  ,      requires_grad=_lowercase       )
							lowercase_      :   Optional[Any]        =			torch.tensor([0.4, 0.2, -0.5]       )
							lowercase_      :   Dict        =			nn.MSELoss()
							# No warmup, constant schedule, no gradient clipping
							lowercase_      :   Optional[Any]        =			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(1000       ):
													lowercase_      :   str        =			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 							__magic_name__     (	unittest.TestCase				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Optional[int]        =  nn.Linear(5_0,						5_0				) if is_torch_available() else None
	SCREAMING_SNAKE_CASE_					:      Optional[Any]        =  AdamW(m.parameters(),						lr=10.0				) if is_torch_available() else None
	SCREAMING_SNAKE_CASE_					:      Optional[Any]        =  1_0
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase=None       )						->  Union[str, Any]:
							self.assertEqual(len(_lowercase       )  ,      len(_lowercase       )       )
							for a, b in zip(_lowercase  ,      _lowercase       ):
													self.assertAlmostEqual(_lowercase  ,      _lowercase  ,      delta=_lowercase  ,      msg=_lowercase       )







	def        lowerCamelCase__		(							self       )						->  int:
							lowercase_      :   int        =			{'num_warmup_steps': 2, 'num_training_steps': 10}
							# schedulers doct format
							# function: (sched_args_dict, expected_learning_rates)
							lowercase_      :   Dict        =			{
							    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():
													lowercase_  ,      lowercase_      :   Union[str, Any]        =			data

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

													lowercase_      :   int        =			scheduler_func(self.optimizer  ,      **_lowercase       )
													if scheduler_func.__name__ != "get_constant_schedule":
																			LambdaScheduleWrapper.wrap_scheduler(_lowercase       )  # wrap to test picklability of the schedule
													lowercase_      :   List[Any]        =			unwrap_and_save_reload_schedule(_lowercase  ,      self.num_steps       )
													self.assertListEqual(_lowercase  ,      _lowercase  ,      msg=f"failed for {scheduler_func} in save and reload"       )






class 							__magic_name__     :







	"""simple docstring"""



	def __init__(							self  ,      _lowercase       )						->  int:
							lowercase_      :   Dict        =			fn
	def __call__(							self  ,      *_lowercase  ,      **_lowercase       )						->  List[str]:
							return self.fn(*_lowercase  ,      **_lowercase       )







	@classmethod
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Union[str, Any]:
							lowercase_      :   Union[str, Any]        =			list(map(self  ,      scheduler.lr_lambdas       )       )








'''simple docstring'''

def 	_UpperCAmelCase    (  a		:	str							)			->			str:





						"""simple docstring"""
						lowercase_      :   Dict        =			0

						# if input_string is "aba" than new_input_string become "a|b|a"
						lowercase_      :   Dict        =			''
						lowercase_      :   Any        =			''

						# append each character + "|" in new_string for range(0, length-1)
						for i in input_string[: len(a							) - 1]:
												new_input_string += i + "|"
						# append last character
						new_input_string += input_string[-1]

						# we will store the starting and ending of previous furthest ending palindromic
						# substring
						lowercase_  ,      lowercase_      :   Dict        =			0, 0

						# length[i] shows the length of palindromic substring with center i
						lowercase_      :   List[Any]        =			[1 for i in range(len(a							)							)]

						# for each character in new_string find corresponding palindromic string
						lowercase_      :   Dict        =			0
						for j in range(len(a							)							):
												lowercase_      :   Tuple        =			1 if j > r else min(length[l + r - j] // 2						,			r - j + 1							)
												while (
												    j - k >= 0
												    and j + k < len(a							)
												    and new_input_string[k + j] == new_input_string[j - k]
												):
																		k += 1

												lowercase_      :   int        =			2 * k - 1

												# does this string is ending after the previously explored end (that is r) ?
												# if yes the update the new r to the last index of this
												if j + k - 1 > r:
																		lowercase_      :   Tuple        =			j - k + 1  # noqa: E741
																		lowercase_      :   Tuple        =			j + k - 1

												# update max_length and start position
												if max_length < length[j]:
																		lowercase_      :   Tuple        =			length[j]
																		lowercase_      :   List[Any]        =			j

    # create that string
						lowercase_      :   str        =			new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
						for i in s:
												if i != "|":
																		output_string += i

						return output_string


if __name__ == "__main__":
						import doctest

						doctest.testmod()
