





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  __magic_name__  (				lowercase				,       lowercase=10				)				-> List[Any]:

							"""simple docstring"""



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





def  __magic_name__  (				lowercase				,       lowercase=10				)				-> str:

							"""simple docstring"""



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

																												lowercase_	:   str  				=     torch.load(lowercase				)
																												scheduler.load_state_dict(lowercase				)
							return lrs
@require_torch
class     UpperCamelCase__					(     unittest.TestCase				):






		'''simple docstring'''






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__  )       -> Dict:






									"""simple docstring"""
									self.assertEqual(len(snake_case__  ),   len(snake_case__  )  )
									for a, b in zip(snake_case__,   snake_case__  ):
																self.assertAlmostEqual(snake_case__,   snake_case__,   delta=snake_case__  )






		def 						snake_case__				(							self  )       -> Union[str, Any]:






									"""simple docstring"""
									lowercase_	:   str  				=     torch.tensor([0.1, -0.2, -0.1],   requires_grad=snake_case__  )
									lowercase_	:   Optional[int]  				=     torch.tensor([0.4, 0.2, -0.5]  )
									lowercase_	:   Dict  				=     nn.MSELoss()
									# No warmup, constant schedule, no gradient clipping
									lowercase_	:   Union[str, Any]  				=     AdamW(params=[w],   lr=2E-1,   weight_decay=0.0  )
									for _ in range(1_00  ):
																lowercase_	:   Dict  				=     criterion(snake_case__,   snake_case__  )
																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  )       -> Union[str, Any]:






									"""simple docstring"""
									lowercase_	:   Dict  				=     torch.tensor([0.1, -0.2, -0.1],   requires_grad=snake_case__  )
									lowercase_	:   Union[str, Any]  				=     torch.tensor([0.4, 0.2, -0.5]  )
									lowercase_	:   Optional[int]  				=     nn.MSELoss()
									# No warmup, constant schedule, no gradient clipping
									lowercase_	:   List[str]  				=     Adafactor(
									    params=[w],   lr=1E-2,   eps=(1E-30, 1E-3),   clip_threshold=1.0,   decay_rate=-0.8,   betaa=snake_case__,   weight_decay=0.0,   relative_step=snake_case__,   scale_parameter=snake_case__,   warmup_init=snake_case__,   )
									for _ in range(10_00  ):
																lowercase_	:   int  				=     criterion(snake_case__,   snake_case__  )
																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     UpperCamelCase__					(     unittest.TestCase				):






		'''simple docstring'''






		__a       :       Any       =	nn.Linear(50				, 50				) if is_torch_available() else None
		__a       :       Optional[int]       =	AdamW(m.parameters()				, lr=1_0.0				) if is_torch_available() else None
		__a       :       Tuple       =	10






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__=None  )       -> List[str]:






									"""simple docstring"""
									self.assertEqual(len(snake_case__  ),   len(snake_case__  )  )
									for a, b in zip(snake_case__,   snake_case__  ):
																self.assertAlmostEqual(snake_case__,   snake_case__,   delta=snake_case__,   msg=snake_case__  )






		def 						snake_case__				(							self  )       -> List[str]:






									"""simple docstring"""
									lowercase_	:   Dict  				=     {"""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, 7.656, 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_	:   int  				=     data

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

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




class     UpperCamelCase__					:






		'''simple docstring'''






		def __init__(							self,   snake_case__  )       -> List[Any]:






									"""simple docstring"""
									lowercase_	:   Optional[Any]  				=     fn






		def __call__(							self,   *snake_case__,   **snake_case__  )       -> str:






									"""simple docstring"""
									return self.fn(*snake_case__,   **snake_case__  )






		@classmethod
		def 						snake_case__				(							self,   snake_case__  )       -> str:






									"""simple docstring"""
									lowercase_	:   Dict  				=     list(map(self,   scheduler.lr_lambdas  )  )






import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp





def  __magic_name__  (				)				-> str:

							"""simple docstring"""



							lowercase_	:   Optional[int]  				=     ArgumentParser(
							    description=(
							        """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
							    )				)

							# Optional arguments for the launch helper
							parser.add_argument("""--num_cores"""				,       type=lowercase				,       default=1				,       help="""Number of TPU cores to use (1 or 8)."""				)

							# positional
							parser.add_argument(
							    """training_script"""				,       type=lowercase				,       help=(
							        """The full path to the single TPU training """
							        """program/script to be launched in parallel, """
							        """followed by all the arguments for the """
							        """training script"""
							    )				,       )

							# rest from the training program
							parser.add_argument("""training_script_args"""				,       nargs=lowercase				)

							return parser.parse_args()





def  __magic_name__  (				)				-> List[Any]:

							"""simple docstring"""



							lowercase_	:   Union[str, Any]  				=     parse_args()

							# Import training_script as a module.
							lowercase_	:   List[Any]  				=     Path(args.training_script				)
							sys.path.append(str(script_fpath.parent.resolve()				)				)
							lowercase_	:   Tuple  				=     script_fpath.stem
							lowercase_	:   List[str]  				=     importlib.import_module(lowercase				)

							# Patch sys.argv
							lowercase_	:   Optional[int]  				=     [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores				)]

							xmp.spawn(mod._mp_fn				,       args=()				,       nprocs=args.num_cores				)


if __name__ == "__main__":
							main()