





import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


SCREAMING_SNAKE_CASE_		     = namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     Any:


      '''simple docstring'''


      return (abs(source - target			) / target) < 0.01



@pytest.mark.integration
def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		_TestCommandArgs(dataset=__lowercase				,		all_configs=__lowercase				,		save_infos=__lowercase			)
      SCREAMING_SNAKE_CASE	=		TestCommand(*__lowercase			)
      test_command.run()
      SCREAMING_SNAKE_CASE	=		os.path.join(__lowercase				,		"""README.md"""			)
      assert os.path.exists(__lowercase			)
      SCREAMING_SNAKE_CASE	=		DatasetInfosDict.from_directory(__lowercase			)
      SCREAMING_SNAKE_CASE	=		DatasetInfosDict(
          {
              """default""": DatasetInfo(
                  features=Features(
                      {
                          """tokens""": Sequence(Value("""string"""			)			),
                          """ner_tags""": Sequence(
                              ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""]			)			),
                          """langs""": Sequence(Value("""string"""			)			),
                          """spans""": Sequence(Value("""string"""			)			),
                      }			)				,		splits=[
                      {
                          """name""": """train""",
                          """num_bytes""": 2_35_15_63,
                          """num_examples""": 1_00_00,
                      },
                      {
                          """name""": """validation""",
                          """num_bytes""": 23_84_18,
                          """num_examples""": 10_00,
                      },
                  ]				,		download_size=3_94_06_80				,		dataset_size=2_58_99_81				,		)
          }			)
      assert dataset_infos.keys() == expected_dataset_infos.keys()
      for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
            SCREAMING_SNAKE_CASE	=		getattr(dataset_infos["""default"""]				,		__lowercase			), getattr(expected_dataset_infos["""default"""]				,		__lowercase			)
            if key == "num_bytes":
                  assert is_apercent_close(__lowercase				,		__lowercase			)
            elif key == "splits":
                  assert list(__lowercase			) == list(__lowercase			)
                  for split in result:
                        assert result[split].name == expected[split].name
                        assert result[split].num_examples == expected[split].num_examples
                        assert is_apercent_close(result[split].num_bytes				,		expected[split].num_bytes			)
            else:
                  result == expected









# 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 	UpperCamelCase__				:



     '''simple docstring'''

     def __init__(							self		:					Optional[Any]		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					bool = True		,lowerCamelCase__		:					bool = False )  ->	Tuple:




           '''simple docstring'''


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






     def        SCREAMING_SNAKE_CASE__						(							self		:					str		,*lowerCamelCase__		:					Union[str, Any]		,**lowerCamelCase__		:					Optional[int] )  ->	Dict:




           '''simple docstring'''


           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
                 SCREAMING_SNAKE_CASE	=		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		:					int )  ->	Optional[Any]:




           '''simple docstring'''


           return self.scheduler.get_last_lr()






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	Optional[Any]:




           '''simple docstring'''


           return self.scheduler.state_dict()






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					str )  ->	int:




           '''simple docstring'''


           self.scheduler.load_state_dict(lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	Optional[Any]:




           '''simple docstring'''


           return self.scheduler.get_lr()






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict		,*lowerCamelCase__		:					int		,**lowerCamelCase__		:					Tuple )  ->	Optional[int]:




           '''simple docstring'''


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


