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      __A					(     _lowercase					,			_lowercase=10 ):

					'''simple docstring'''







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




def      __A					(     _lowercase					,			_lowercase=10 ):

					'''simple docstring'''







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

																				_A								= torch.load(_lowercase )
																				scheduler.load_state_dict(_lowercase )
					return lrs





@require_torch
class 						SCREAMING_SNAKE_CASE				( unittest.TestCase					):





							"""simple docstring"""

							def       __A (  self:   str		,     __A:   Optional[int]		,     __A:   Optional[int]		,     __A:   Optional[Any]   )    ->				Optional[int]:
												self.assertEqual(len(__A   )		,     len(__A   )   )
												for a, b in zip(__A		,     __A   ):
																	self.assertAlmostEqual(__A		,     __A		,     delta=__A   )

							def       __A (  self:   Union[str, Any]   )    ->				Union[str, Any]:
												_A								= torch.tensor([0.1, -0.2, -0.1]		,     requires_grad=__A   )
												_A								= torch.tensor([0.4, 0.2, -0.5]   )
												_A								= nn.MSELoss()
												# No warmup, constant schedule, no gradient clipping
												_A								= AdamW(params=[w]		,     lr=2e-1		,     weight_decay=0.0   )
												for _ in range(1_00   ):
																	_A								= criterion(__A		,     __A   )
																	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:   Optional[Any]   )    ->				List[Any]:
												_A								= torch.tensor([0.1, -0.2, -0.1]		,     requires_grad=__A   )
												_A								= torch.tensor([0.4, 0.2, -0.5]   )
												_A								= nn.MSELoss()
												# No warmup, constant schedule, no gradient clipping
												_A								= Adafactor(
												    params=[w]		,     lr=1e-2		,     eps=(1e-30, 1e-3)		,     clip_threshold=1.0		,     decay_rate=-0.8		,     betaa=__A		,     weight_decay=0.0		,     relative_step=__A		,     scale_parameter=__A		,     warmup_init=__A		,     )
												for _ in range(10_00   ):
																	_A								= criterion(__A		,     __A   )
																	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 						SCREAMING_SNAKE_CASE				( unittest.TestCase					):





							"""simple docstring"""

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

							def       __A (  self:   Optional[Any]		,     __A:   List[str]		,     __A:   Optional[Any]		,     __A:   Optional[int]		,     __A:   List[Any]=None   )    ->				List[Any]:
												self.assertEqual(len(__A   )		,     len(__A   )   )
												for a, b in zip(__A		,     __A   ):
																	self.assertAlmostEqual(__A		,     __A		,     delta=__A		,     msg=__A   )



							def       __A (  self:   Tuple   )    ->				List[Any]:
												_A								= {'''num_warmup_steps''': 2, '''num_training_steps''': 10}
												# schedulers doct format
												# function: (sched_args_dict, expected_learning_rates)
												_A								= {
												    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():
																	_A							,_A								= data

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

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





class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							def __init__(  self:   Optional[int]		,     __A:   Tuple   )    ->				Optional[Any]:
												_A								= fn

							def __call__(  self:   int		,     *__A:   Tuple		,     **__A:   Optional[Any]   )    ->				Optional[int]:
												return self.fn(*__A		,     **__A   )



							@classmethod
							def       __A (  self:   Tuple		,     __A:   Dict   )    ->				List[str]:
												_A								= list(map(self		,     scheduler.lr_lambdas   )   )











def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A							,_A								= len(_lowercase ), len(grid[0] )
					if (
					    min(_lowercase					,			_lowercase ) < 0
					    or row == row_length
					    or col == col_length
					    or (row, col) in visit
					    or grid[row][col] == 1
					):
										return 0
					if row == row_length - 1 and col == col_length - 1:
										return 1

					visit.add((row, col) )

					_A								= 0
					count += depth_first_search(_lowercase					,			row + 1					,			_lowercase					,			_lowercase )
					count += depth_first_search(_lowercase					,			row - 1					,			_lowercase					,			_lowercase )
					count += depth_first_search(_lowercase					,			_lowercase					,			col + 1					,			_lowercase )
					count += depth_first_search(_lowercase					,			_lowercase					,			col - 1					,			_lowercase )

					visit.remove((row, col) )
					return count


if __name__ == "__main__":
				import doctest

				doctest.testmod()



