






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 					lowercase_ (    __snake_case     :							Optional[int]	,	__snake_case     :							List[str]=10      )     -> Optional[Any]:





				'''simple docstring'''


				snake_case__      :Any         =   []
				for _ in range(__snake_case      ):
								lrs.append(scheduler.get_lr()[0]      )
								scheduler.step()
				return lrs





def 					lowercase_ (    __snake_case     :							str	,	__snake_case     :							Union[str, Any]=10      )     -> str:





				'''simple docstring'''


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

																snake_case__      :List[Any]         =   torch.load(__snake_case      )
																scheduler.load_state_dict(__snake_case      )
				return lrs


@require_torch
class 					_snake_case						(			unittest.TestCase ):






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    List[str]:
					self.assertEqual(len(UpperCamelCase       )					,len(UpperCamelCase       )       )
					for a, b in zip(UpperCamelCase					,UpperCamelCase       ):
									self.assertAlmostEqual(UpperCamelCase					,UpperCamelCase					,delta=UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :Union[str, Any]         =   torch.tensor([0.1, -0.2, -0.1]					,requires_grad=UpperCamelCase       )
					snake_case__      :List[Any]         =   torch.tensor([0.4, 0.2, -0.5]       )
					snake_case__      :int         =   nn.MSELoss()
					# No warmup, constant schedule, no gradient clipping
					snake_case__      :List[str]         =   AdamW(params=[w]					,lr=2E-1					,weight_decay=0.0       )
					for _ in range(100       ):
									snake_case__      :str         =   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    lowerCAmelCase_							(		self       )						->    int:
					snake_case__      :int         =   torch.tensor([0.1, -0.2, -0.1]					,requires_grad=UpperCamelCase       )
					snake_case__      :Optional[Any]         =   torch.tensor([0.4, 0.2, -0.5]       )
					snake_case__      :Optional[int]         =   nn.MSELoss()
					# No warmup, constant schedule, no gradient clipping
					snake_case__      :Any         =   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(1_000       ):
									snake_case__      :Any         =   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 					_snake_case						(			unittest.TestCase ):
	_A          =   nn.Linear(50       ,    50 ) if is_torch_available() else None
	_A          =   AdamW(m.parameters()       ,    lr=1_0.0 ) if is_torch_available() else None
	_A          =   10






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase=None       )						->    int:
					self.assertEqual(len(UpperCamelCase       )					,len(UpperCamelCase       )       )
					for a, b in zip(UpperCamelCase					,UpperCamelCase       ):
									self.assertAlmostEqual(UpperCamelCase					,UpperCamelCase					,delta=UpperCamelCase					,msg=UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					snake_case__      :Dict         =   {"num_warmup_steps": 2, "num_training_steps": 10}
					# schedulers doct format
					# function: (sched_args_dict, expected_learning_rates)
					snake_case__      :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, 7.656, 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():
									snake_case__    ,						snake_case__      :List[Any]         =   data

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

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




class 					_snake_case						:






	def __init__(		self					,UpperCamelCase       )						->    List[str]:
					snake_case__      :Any         =   fn






	def __call__(		self					,*UpperCamelCase					,**UpperCamelCase       )						->    Dict:
					return self.fn(*UpperCamelCase					,**UpperCamelCase       )






	@classmethod
	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    Any:
					snake_case__      :Dict         =   list(map(self					,scheduler.lr_lambdas       )       )







from __future__ import annotations

import math





def 					lowercase_ (    __snake_case     :							int	,	__snake_case     :							int	,	__snake_case     :							bool	,	__snake_case     :							list[int]	,	__snake_case     :							float      )     -> int:





				'''simple docstring'''


				if depth < 0:
								raise ValueError("Depth cannot be less than 0"      )

				if len(__snake_case      ) == 0:
								raise ValueError("Scores cannot be empty"      )

				if depth == height:
								return scores[node_index]

				if is_max:
								return max(
								    minimax(depth + 1	,	node_index * 2	,	__snake_case	,	__snake_case	,	__snake_case      )	,	minimax(depth + 1	,	node_index * 2 + 1	,	__snake_case	,	__snake_case	,	__snake_case      )	,	)

				return min(
				    minimax(depth + 1	,	node_index * 2	,	__snake_case	,	__snake_case	,	__snake_case      )	,	minimax(depth + 1	,	node_index * 2 + 1	,	__snake_case	,	__snake_case	,	__snake_case      )	,	)





def 					lowercase_ (    )     -> None:





				'''simple docstring'''


				snake_case__      :List[Any]         =   [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
				snake_case__      :int         =   math.log(len(__snake_case      )	,	2      )
				print("Optimal value : "	,	end=""      )
				print(minimax(0	,	0	,	__snake_case	,	__snake_case	,	__snake_case      )      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()