


# 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      ,						snake_case      ,						snake_case      ,						snake_case = True      ,						snake_case = False     ):

						'''simple docstring'''


						UpperCamelCase__					=       scheduler
						UpperCamelCase__					=       optimizers if isinstance(snake_case      ,						(list, tuple)     ) else [optimizers]
						UpperCamelCase__					=       split_batches
						UpperCamelCase__					=       step_with_optimizer
						UpperCamelCase__					=       GradientState()




			def 				snake_case__				(							self      ,						*snake_case      ,						**snake_case     ):

						'''simple docstring'''


						if not self.step_with_optimizer:
									# No link between scheduler and optimizer -> just step
									self.scheduler.step(*snake_case      ,						**snake_case     )
									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(*snake_case      ,						**snake_case     )
						else:
									# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
									# num_processes steps per training step
									UpperCamelCase__					=       AcceleratorState().num_processes
									for _ in range(snake_case     ):
												# 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(*snake_case      ,						**snake_case     )
												else:
															self.scheduler.step(*snake_case      ,						**snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return self.scheduler.get_last_lr()




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return self.scheduler.state_dict()




			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						self.scheduler.load_state_dict(snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return self.scheduler.get_lr()







			def 				snake_case__				(							self      ,						*snake_case      ,						**snake_case     ):

						'''simple docstring'''


						return self.scheduler.print_lr(*snake_case      ,						**snake_case     )






import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property







@unittest.skipUnless(os.path.exists(UpperCAmelCase      )					,	'Tatoeba directory does not exist.'      )
class        lowerCamelCase__	(							unittest.TestCase      ):






			"""simple docstring"""






			@cached_property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       tempfile.mkdtemp()
						return TatoebaConverter(save_dir=snake_case     )




			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						self.resolver.convert_models(["heb-eng"]     )







			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__,      UpperCamelCase__					=       self.resolver.write_model_card("opus-mt-he-en"      ,						dry_run=snake_case     )
						assert mmeta["long_pair"] == "heb-eng"


