




'''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    __lowercase						(  __magic_name__ ):
   _a							=      0
   _a							=      False
   _a							=      3.0





class    __lowercase						(  unittest.TestCase ):







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     # 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=UpperCamelCase      ).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      )				->    Any:
     # If no defaults are changed, `to_kwargs` returns an empty dict.
     __a			=					GradScalerKwargs(init_scale=1024      ,							growth_factor=2      )
     AcceleratorState._reset_state()
     __a			=					Accelerator(mixed_precision='fp16'      ,							kwargs_handlers=[scaler_handler]      )
     print(accelerator.use_fpaa      )
     __a			=					accelerator.scaler

     # Check the kwargs have been applied
     self.assertEqual(scaler._init_scale      ,							1024.0      )
     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      ,							2000      )
     self.assertEqual(scaler._enabled      ,							UpperCamelCase      )







   @require_multi_gpu
   def 		UpperCamelCase__			(	self      )				->    str:
     __a			=					['torchrun', f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__      )]
     execute_subprocess_async(UpperCamelCase      ,							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(1_00, 2_00)
  UpperCAmelCase_ 		=      accelerator.prepare(model)

  # Check the values changed in kwargs
  UpperCAmelCase_ 		=      ""
  UpperCAmelCase_ 		=      model.bucket_bytes_cap // (10_24 * 10_24)
  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)








'''simple docstring'''


from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


UpperCAmelCase_ 		=      logging.get_logger(__name__)


UpperCAmelCase_ 		=      TypeVar("DatasetType", Dataset, IterableDataset)


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[DatasetType]				,					a_	:							Optional[List[float]] = None				,					a_	:							Optional[int] = None				,					a_	:							Optional[DatasetInfo] = None				,					a_	:							Optional[NamedSplit] = None				,					a_	:							Literal["first_exhausted", "all_exhausted"] = "first_exhausted"				,					):
  from .arrow_dataset import Dataset
  from .iterable_dataset import IterableDataset

  if not datasets:
    raise ValueError('Unable to interleave an empty list of datasets.'			)
  for i, dataset in enumerate(a_			):
    if not isinstance(a_				,					(Dataset, IterableDataset)			):
      if isinstance(a_				,					(DatasetDict, IterableDatasetDict)			):
        if not dataset:
          raise ValueError(
              f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
              'is an empty dataset dictionary.'			)
        raise ValueError(
            f"Dataset at position {i} has at least one split: {list(a_			)}\n"
            f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(a_			)			)}']"			)
      raise ValueError(
          f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(a_			).__name__}."			)
    if i == 0:
      __a	,       __a			=					(
          (Dataset, IterableDataset) if isinstance(a_				,					a_			) else (IterableDataset, Dataset)
      )
    elif not isinstance(a_				,					a_			):
      raise ValueError(
          f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."			)
  if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
    raise ValueError(f"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."			)
  if dataset_type is Dataset:
    return _interleave_map_style_datasets(
        a_				,					a_				,					a_				,					info=a_				,					split=a_				,					stopping_strategy=a_			)
  else:
    return _interleave_iterable_datasets(
        a_				,					a_				,					a_				,					info=a_				,					split=a_				,					stopping_strategy=a_			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[DatasetType]				,					a_	:							Optional[DatasetInfo] = None				,					a_	:							Optional[NamedSplit] = None				,					a_	:							int = 0				,					):

  if not dsets:
    raise ValueError('Unable to concatenate an empty list of datasets.'			)
  for i, dataset in enumerate(a_			):
    if not isinstance(a_				,					(Dataset, IterableDataset)			):
      if isinstance(a_				,					(DatasetDict, IterableDatasetDict)			):
        if not dataset:
          raise ValueError(
              f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
              'is an empty dataset dictionary.'			)
        raise ValueError(
            f"Dataset at position {i} has at least one split: {list(a_			)}\n"
            f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(a_			)			)}']"			)
      raise ValueError(
          f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(a_			).__name__}."			)
    if i == 0:
      __a	,       __a			=					(
          (Dataset, IterableDataset) if isinstance(a_				,					a_			) else (IterableDataset, Dataset)
      )
    elif not isinstance(a_				,					a_			):
      raise ValueError(
          f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."			)
  if dataset_type is Dataset:
    return _concatenate_map_style_datasets(a_				,					info=a_				,					split=a_				,					axis=a_			)
  else:
    return _concatenate_iterable_datasets(a_				,					info=a_				,					split=a_				,					axis=a_			)



