


'''simple docstring'''







import warnings

from .state import AcceleratorState, GradientState


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



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__      ,	snake_case__ = True      ,	snake_case__ = False   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       scheduler
            _lowerCAmelCase    :							Optional[int]   			=       optimizers if isinstance(snake_case__      ,	(list, tuple)   ) else [optimizers]
            _lowerCAmelCase    :							Optional[int]   			=       split_batches
            _lowerCAmelCase    :							Any   			=       step_with_optimizer
            _lowerCAmelCase    :							Tuple   			=       GradientState()







     def a	( 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
                   _lowerCAmelCase    :							Dict   			=       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 a	( self   ):

            '''simple docstring'''

            return self.scheduler.get_last_lr()







     def a	( self   ):

            '''simple docstring'''

            return self.scheduler.state_dict()







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            self.scheduler.load_state_dict(snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            return self.scheduler.get_lr()







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

            '''simple docstring'''

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





'''simple docstring'''







import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
       import jax.numpy as jnp

       from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     @slow
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base'   )
            _lowerCAmelCase    :							Optional[Any]   			=       AutoTokenizer.from_pretrained('xlm-roberta-base'   )
            _lowerCAmelCase    :							Dict   			=       'The dog is cute and lives in the garden house'
            _lowerCAmelCase    :							List[str]   			=       jnp.array([tokenizer.encode(snake_case__   )]   )

            _lowerCAmelCase    :							Optional[int]   			=       (1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
            _lowerCAmelCase    :							Tuple   			=       jnp.array(
                [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]   )

            _lowerCAmelCase    :							Union[str, Any]   			=       model(snake_case__   )['last_hidden_state']
            self.assertEqual(output.shape      ,	snake_case__   )
            # compare the actual values for a slice of last dim
            self.assertTrue(jnp.allclose(output[:, :, -1]      ,	snake_case__      ,	atol=1E-3   )   )

