






'''simple docstring'''

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


UpperCamelCase							=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 							snake_case					(     a_							: Any    ,   a_							: Optional[Any]	)       ->				Tuple:





      """simple docstring"""

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



@pytest.mark.integration
def 							snake_case					(     a_							: List[str]	)       ->				Optional[int]:





      """simple docstring"""

      UpperCamelCase_				:   int					    =							_TestCommandArgs(dataset=a_    ,   all_configs=a_    ,   save_infos=a_	)
      UpperCamelCase_				:   Tuple					    =							TestCommand(*a_	)
      test_command.run()
      UpperCamelCase_				:   Optional[Any]					    =							os.path.join(a_    ,   """README.md"""	)
      assert os.path.exists(a_	)
      UpperCamelCase_				:   Union[str, Any]					    =							DatasetInfosDict.from_directory(a_	)
      UpperCamelCase_				:   Any					    =							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_351_563,
                          """num_examples""": 10_000,
                      },
                      {
                          """name""": """validation""",
                          """num_bytes""": 238_418,
                          """num_examples""": 1_000,
                      },
                  ]    ,   download_size=3_940_680    ,   dataset_size=2_589_981    ,   )
          }	)
      assert dataset_infos.keys() == expected_dataset_infos.keys()
      for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
            UpperCamelCase_    ,			UpperCamelCase_				:   Dict					    =							getattr(dataset_infos["""default"""]    ,   a_	), getattr(expected_dataset_infos["""default"""]    ,   a_	)
            if key == "num_bytes":
                  assert is_apercent_close(a_    ,   a_	)
            elif key == "splits":
                  assert list(a_	) == list(a_	)
                  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














'''simple docstring'''

import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler




@dataclass
class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						int      =						0
 __a			:						bool      =						False
 __a			:						float      =						3.0






class     A    (      unittest.TestCase						):






 """simple docstring"""





 def   _UpperCAmelCase       (							self ):
       # If no defaults are changed, `to_kwargs` returns an empty dict.
       self.assertDictEqual(MockClass().to_kwargs()			,     {} )
       self.assertDictEqual(MockClass(a=2 ).to_kwargs()			,     {"""a""": 2} )
       self.assertDictEqual(MockClass(a=2			,     b=__lowerCAmelCase ).to_kwargs()			,     {"""a""": 2, """b""": True} )
       self.assertDictEqual(MockClass(a=2			,     c=2.25 ).to_kwargs()			,     {"""a""": 2, """c""": 2.25} )
 @require_cuda
 def   _UpperCAmelCase       (							self ):
       # If no defaults are changed, `to_kwargs` returns an empty dict.
       UpperCamelCase_				:   int					    =							GradScalerKwargs(init_scale=10_24			,     growth_factor=2 )
       AcceleratorState._reset_state()
       UpperCamelCase_				:   Dict					    =							Accelerator(mixed_precision="""fp16"""			,     kwargs_handlers=[scaler_handler] )
       print(accelerator.use_fpaa )
       UpperCamelCase_				:   Any					    =							accelerator.scaler

       # Check the kwargs have been applied
       self.assertEqual(scaler._init_scale			,     10_24.0 )
       self.assertEqual(scaler._growth_factor			,     2.0 )

       # Check the other values are at the default
       self.assertEqual(scaler._backoff_factor			,     0.5 )
       self.assertEqual(scaler._growth_interval			,     20_00 )
       self.assertEqual(scaler._enabled			,     __lowerCAmelCase )





 @require_multi_gpu
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							["""torchrun""", F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__ )]
       execute_subprocess_async(__lowerCAmelCase			,     env=os.environ.copy() )


if __name__ == "__main__":
      UpperCamelCase							=DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
      UpperCamelCase							=Accelerator(kwargs_handlers=[ddp_scaler])
      UpperCamelCase							=torch.nn.Linear(100, 200)
      UpperCamelCase							=accelerator.prepare(model)

      # Check the values changed in kwargs
      UpperCamelCase							=""
      UpperCamelCase							=model.bucket_bytes_cap // (1024 * 1024)
      if observed_bucket_cap_map != 15:
            error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
      if model.find_unused_parameters is not True:
            error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

      # Check the values of the defaults
      if model.dim != 0:
            error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
      if model.broadcast_buffers is not True:
            error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
      if model.gradient_as_bucket_view is not False:
            error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

      # Raise error at the end to make sure we don't stop at the first failure.
      if len(error_msg) > 0:
            raise ValueError(error_msg)






