



"""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        lowerCAmelCase_(   lowercase_     :							Optional[int]						,   lowercase_     :							Any=10							)	->       Optional[Any]:
				_lowerCamelCase        =				[]
				for _ in range(lowercase_							):
								lrs.append(scheduler.get_lr()[0]							)
								scheduler.step()
				return lrs



def        lowerCAmelCase_(   lowercase_     :							int						,   lowercase_     :							List[Any]=10							)	->       List[str]:
				_lowerCamelCase        =				[]
				for step in range(lowercase_							):
								lrs.append(scheduler.get_lr()[0]							)
								scheduler.step()
								if step == num_steps // 2:
												with tempfile.TemporaryDirectory() as tmpdirname:
																_lowerCamelCase        =				os.path.join(lowercase_						,   '''schedule.bin'''							)
																torch.save(scheduler.state_dict()						,   lowercase_							)

																_lowerCamelCase        =				torch.load(lowercase_							)
																scheduler.load_state_dict(lowercase_							)
				return lrs


@require_torch
class 							lowerCamelCase_(   unittest.TestCase    ):





					'''simple docstring'''



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

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				torch.tensor([0.1, -0.2, -0.1]				,       requires_grad=lowerCamelCase__   )
									_lowerCamelCase        =				torch.tensor([0.4, 0.2, -0.5]   )
									_lowerCamelCase        =				nn.MSELoss()
									# No warmup, constant schedule, no gradient clipping
									_lowerCamelCase        =				AdamW(params=[w]				,       lr=2e-1				,       weight_decay=0.0   )
									for _ in range(1_0_0   ):
													_lowerCamelCase        =				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 			snake_case__      (    self   ):
									_lowerCamelCase        =				torch.tensor([0.1, -0.2, -0.1]				,       requires_grad=lowerCamelCase__   )
									_lowerCamelCase        =				torch.tensor([0.4, 0.2, -0.5]   )
									_lowerCamelCase        =				nn.MSELoss()
									# No warmup, constant schedule, no gradient clipping
									_lowerCamelCase        =				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(1_0_0_0   ):
													_lowerCamelCase        =				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'''



					lowercase__   :       List[str]										= nn.Linear(50,				50    ) if is_torch_available() else None
					lowercase__   :       int										= AdamW(m.parameters(),				lr=10.0    ) if is_torch_available() else None
					lowercase__   :       List[str]										= 10

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__=None   ):
									self.assertEqual(len(lowerCamelCase__   )				,       len(lowerCamelCase__   )   )
									for a, b in zip(lowerCamelCase__				,       lowerCamelCase__   ):
													self.assertAlmostEqual(lowerCamelCase__				,       lowerCamelCase__				,       delta=lowerCamelCase__				,       msg=lowerCamelCase__   )





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				{'''num_warmup_steps''': 2, '''num_training_steps''': 1_0}
									# schedulers doct format
									# function: (sched_args_dict, expected_learning_rates)
									_lowerCamelCase        =				{
									    get_constant_schedule: ({}, [1_0.0] * self.num_steps),
									    get_constant_schedule_with_warmup: (
									        {'''num_warmup_steps''': 4},
									        [0.0, 2.5, 5.0, 7.5, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0],
									    ),
									    get_linear_schedule_with_warmup: (
									        {**common_kwargs},
									        [0.0, 5.0, 1_0.0, 8.7_5, 7.5, 6.2_5, 5.0, 3.7_5, 2.5, 1.2_5],
									    ),
									    get_cosine_schedule_with_warmup: (
									        {**common_kwargs},
									        [0.0, 5.0, 1_0.0, 9.6_1, 8.5_3, 6.9_1, 5.0, 3.0_8, 1.4_6, 0.3_8],
									    ),
									    get_cosine_with_hard_restarts_schedule_with_warmup: (
									        {**common_kwargs, '''num_cycles''': 2},
									        [0.0, 5.0, 1_0.0, 8.5_3, 5.0, 1.4_6, 1_0.0, 8.5_3, 5.0, 1.4_6],
									    ),
									    get_polynomial_decay_schedule_with_warmup: (
									        {**common_kwargs, '''power''': 2.0, '''lr_end''': 1e-7},
									        [0.0, 5.0, 1_0.0, 7.6_5_6, 5.6_2_5, 3.9_0_6, 2.5, 1.4_0_6, 0.6_2_5, 0.1_5_6],
									    ),
									    get_inverse_sqrt_schedule: (
									        {'''num_warmup_steps''': 2},
									        [0.0, 5.0, 1_0.0, 8.1_6_5, 7.0_7_1, 6.3_2_5, 5.7_7_4, 5.3_4_5, 5.0, 4.7_1_4],
									    ),
									}

									for scheduler_func, data in scheds.items():
													_lowerCamelCase  ,     _lowerCamelCase        =				data

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

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

					def __call__(    self				,       *lowerCamelCase__				,       **lowerCamelCase__   ):
									return self.fn(*lowerCamelCase__				,       **lowerCamelCase__   )





					@classmethod
					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				list(map(self				,       scheduler.lr_lambdas   )   )





"""simple docstring"""
from __future__ import annotations

from typing import Any



def        lowerCAmelCase_(   lowercase_     :							list[Any]							)	->       None:
				create_state_space_tree(lowercase_						,   []						,   0							)



def        lowerCAmelCase_(   lowercase_     :							list[Any]						,   lowercase_     :							list[Any]						,   lowercase_     :							int							)	->       None:

				if index == len(lowercase_							):
								print(lowercase_							)
								return

				create_state_space_tree(lowercase_						,   lowercase_						,   index + 1							)
				current_subsequence.append(sequence[index]							)
				create_state_space_tree(lowercase_						,   lowercase_						,   index + 1							)
				current_subsequence.pop()


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						list[Any]    							=      [3, 1, 2, 4]
			generate_all_subsequences(seq)

			seq.clear()
			seq.extend(['''A''', '''B''', '''C'''])
			generate_all_subsequences(seq)
