



'''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      SCREAMING_SNAKE_CASE				(				lowercase_    :      Dict ,  lowercase_    :      List[Any]=10						):
						lowercase        =							[]
						for _ in range(_lowerCamelCase						):
												lrs.append(scheduler.get_lr()[0]						)
												scheduler.step()
						return lrs







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

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




@require_torch
class      __UpperCamelCase       (unittest.TestCase			):







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    Any:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							lowercase        =							torch.tensor([0.1, -0.2, -0.1] ,				requires_grad=_A						)
							lowercase        =							torch.tensor([0.4, 0.2, -0.5]						)
							lowercase        =							nn.MSELoss()
							# No warmup, constant schedule, no gradient clipping
							lowercase        =							AdamW(params=[w] ,				lr=2E-1 ,				weight_decay=0.0						)
							for _ in range(100						):
													lowercase        =							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]:
							'''simple docstring'''






							lowercase        =							torch.tensor([0.1, -0.2, -0.1] ,				requires_grad=_A						)
							lowercase        =							torch.tensor([0.4, 0.2, -0.5]						)
							lowercase        =							nn.MSELoss()
							# No warmup, constant schedule, no gradient clipping
							lowercase        =							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(1000						):
													lowercase        =							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      __UpperCamelCase       (unittest.TestCase			):
	__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 ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase=None						)    ->    List[Any]:
							'''simple docstring'''






							self.assertEqual(len(_A						) ,				len(_A						)						)
							for a, b in zip(_A ,				_A						):
													self.assertAlmostEqual(_A ,				_A ,				delta=_A ,				msg=_A						)







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							lowercase        =							{'num_warmup_steps': 2, 'num_training_steps': 10}
							# schedulers doct format
							# function: (sched_args_dict, expected_learning_rates)
							lowercase        =							{
							    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        =							data

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

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




class      __UpperCamelCase       :







	def __init__(    self ,				_lowerCAmelCase						)    ->    List[Any]:
							'''simple docstring'''






							lowercase        =							fn







	def __call__(    self ,				*_lowerCAmelCase ,				**_lowerCAmelCase						)    ->    str:
							'''simple docstring'''






							return self.fn(*_A ,				**_A						)







	@classmethod
	def   _a   (    self ,				_lowerCAmelCase						)    ->    Tuple:
							'''simple docstring'''






							lowercase        =							list(map(self ,				scheduler.lr_lambdas						)						)







'''simple docstring'''


from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							HfArgumentParser(lowercase_						)
						lowercase        =							parser.parse_args_into_dataclasses()[0]
						lowercase        =							TensorFlowBenchmark(args=lowercase_						)
						try:
												lowercase        =							parser.parse_args_into_dataclasses()[0]
						except ValueError as e:
												lowercase        =							"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
												lowercase        =							""" """.join(str(lowercase_						).split(""" """						)[:-1]						)
												lowercase        =							""""""
												lowercase        =							eval(str(lowercase_						).split(""" """						)[-1]						)
												lowercase        =							[]
												for arg in depreciated_args:
																		# arg[2:] removes '--'
																		if arg[2:] in TensorFlowBenchmark.deprecated_args:
																								# arg[5:] removes '--no_'
																								full_error_msg += arg_error_msg.format(arg[5:]						)
																		else:
																								wrong_args.append(lowercase_						)
												if len(lowercase_						) > 0:
																		lowercase        =							full_error_msg + begin_error_msg + str(lowercase_						)
												raise ValueError(lowercase_						)
						benchmark.run()


if __name__ == "__main__":
						main()


