



'''simple docstring'''





# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")

class 	lowerCamelCase_    :






						'''simple docstring'''







						def __init__(	self      :			Optional[Any]	,	__lowerCamelCase      :			str	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			bool = True	,	__lowerCamelCase      :			bool = False )      ->				List[str]:
									A							:					List[Any]              =						scheduler
									A							:					int              =						optimizers if isinstance(__lowerCamelCase	,	(list, tuple) ) else [optimizers]
									A							:					Any              =						split_batches
									A							:					List[str]              =						step_with_optimizer
									A							:					Tuple              =						GradientState()







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	*__lowerCamelCase      :			int	,	**__lowerCamelCase      :			str )      ->				List[Any]:
									if not self.step_with_optimizer:
												# No link between scheduler and optimizer -> just step
												self.scheduler.step(*__lowerCamelCase	,	**__lowerCamelCase )
												return

									# Otherwise, first make sure the optimizer was stepped.
									if not self.gradient_state.sync_gradients:
												if self.gradient_state.adjust_scheduler:
															self.scheduler._step_count += 1
												return

									for opt in self.optimizers:
												if opt.step_was_skipped:
															return
									if self.split_batches:
												# Split batches -> the training dataloader batch size is not changed so one step per training step
												self.scheduler.step(*__lowerCamelCase	,	**__lowerCamelCase )
									else:
												# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
												# num_processes steps per training step
												A							:					str              =						AcceleratorState().num_processes
												for _ in range(__lowerCamelCase ):
															# Special case when using OneCycle and `drop_last` was not used
															if hasattr(self.scheduler	,	"total_steps" ):
																		if self.scheduler._step_count <= self.scheduler.total_steps:
																					self.scheduler.step(*__lowerCamelCase	,	**__lowerCamelCase )
															else:
																		self.scheduler.step(*__lowerCamelCase	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Any:
									return self.scheduler.get_last_lr()







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Union[str, Any]:
									return self.scheduler.state_dict()







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	__lowerCamelCase      :			Optional[Any] )      ->				str:
									self.scheduler.load_state_dict(__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				Any:
									return self.scheduler.get_lr()







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	*__lowerCamelCase      :			str	,	**__lowerCamelCase      :			str )      ->				Tuple:
									return self.scheduler.print_lr(*__lowerCamelCase	,	**__lowerCamelCase )




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
							import torch

							from transformers import XLMRobertaModel

@require_sentencepiece
@require_tokenizers
@require_torch
class 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Union[str, Any]:
									A							:					Union[str, Any]              =						XLMRobertaModel.from_pretrained("xlm-roberta-base" )
									A							:					Tuple              =						torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]] )
									# The dog is cute and lives in the garden house

									A							:					Tuple              =						torch.Size((1, 12, 7_68) )  # batch_size, sequence_length, embedding_vector_dim
									A							:					List[str]              =						torch.tensor(
									    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
									#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
									#  xlmr.eval()
									#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
									with torch.no_grad():
												A							:					Tuple              =						model(__lowerCamelCase )["last_hidden_state"].detach()
									self.assertEqual(output.shape	,	__lowerCamelCase )
									# compare the actual values for a slice of last dim
									self.assertTrue(torch.allclose(output[:, :, -1]	,	__lowerCamelCase	,	atol=1e-3 ) )







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				int:
									A							:					str              =						XLMRobertaModel.from_pretrained("xlm-roberta-large" )
									A							:					List[Any]              =						torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]] )
									# The dog is cute and lives in the garden house

									A							:					Optional[Any]              =						torch.Size((1, 12, 10_24) )  # batch_size, sequence_length, embedding_vector_dim
									A							:					List[Any]              =						torch.tensor(
									    [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]] )
									#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
									#  xlmr.eval()
									#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
									with torch.no_grad():
												A							:					Optional[int]              =						model(__lowerCamelCase )["last_hidden_state"].detach()
									self.assertEqual(output.shape	,	__lowerCamelCase )
									# compare the actual values for a slice of last dim
									self.assertTrue(torch.allclose(output[:, :, -1]	,	__lowerCamelCase	,	atol=1e-3 ) )