






"""simple docstring"""



import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Union[str, Any]    ):





  '''simple docstring'''





  return EnvironmentCommand()



class        a     (   a_	):







    @staticmethod
    def 			UpperCamelCase_      (			_lowerCamelCase       ):
      lowercase							    =       parser.add_parser('env'       )
      download_parser.set_defaults(func=_lowerCamelCase       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       huggingface_hub.__version__

      lowercase							    =       'not installed'
      lowercase							    =       'NA'
      if is_torch_available():
        import torch

        lowercase							    =       torch.__version__
        lowercase							    =       torch.cuda.is_available()

      lowercase							    =       'not installed'
      if is_transformers_available():
        import transformers

        lowercase							    =       transformers.__version__

      lowercase							    =       'not installed'
      if is_accelerate_available():
        import accelerate

        lowercase							    =       accelerate.__version__

      lowercase							    =       'not installed'
      if is_xformers_available():
        import xformers

        lowercase							    =       xformers.__version__

      lowercase							    =       {
          '`diffusers` version': version,
          'Platform': platform.platform(),
          'Python version': platform.python_version(),
          'PyTorch version (GPU?)': F'{pt_version} ({pt_cuda_available})',
          'Huggingface_hub version': hub_version,
          'Transformers version': transformers_version,
          'Accelerate version': accelerate_version,
          'xFormers version': xformers_version,
          'Using GPU in script?': '<fill in>',
          'Using distributed or parallel set-up in script?': '<fill in>',
      }

      print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'       )
      print(self.format_dict(_lowerCamelCase       )       )

      return info







    @staticmethod
    def 			UpperCamelCase_      (			_lowerCamelCase       ):
      return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]       ) + "\n"










"""simple docstring"""



import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


_UpperCamelCase		:  List[Any]						=		4
_UpperCamelCase		:  Optional[Any]						=		3

class        a     (   a_	):
    pass


def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]    ):





  '''simple docstring'''





  for shard in shards:
    for i in range(__snake_case    ):
      yield {"i": i, "shard": shard}
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       int(os.environ['RANK']    )
  lowercase							    =       int(os.environ['WORLD_SIZE']    )

  lowercase							    =       ArgumentParser()
  parser.add_argument('--streaming'		,					type=__snake_case    )
  parser.add_argument('--local_rank'		,					type=__snake_case    )
  parser.add_argument('--num_workers'		,					type=__snake_case		,					default=0    )
  lowercase							    =       parser.parse_args()
  lowercase							    =       args.streaming
  lowercase							    =       args.num_workers

  lowercase							    =       {'shards': [f'shard_{shard_idx}' for shard_idx in range(__snake_case    )]}
  lowercase							    =       IterableDataset.from_generator(__snake_case		,					gen_kwargs=__snake_case    )
  if not streaming:
    lowercase							    =       Dataset.from_list(list(__snake_case    )    )

  lowercase							    =       split_dataset_by_node(__snake_case		,					rank=__snake_case		,					world_size=__snake_case    )
  lowercase							    =       torch.utils.data.DataLoader(__snake_case		,					num_workers=__snake_case    )

  lowercase							    =       NUM_SHARDS * NUM_ITEMS_PER_SHARD
  lowercase							    =       full_size // world_size
  expected_local_size += int(rank < (full_size % world_size)    )

  lowercase							    =       sum(1 for _ in dataloader    )
  if local_size != expected_local_size:
    raise FailedTestError(f'local_size {local_size} != expected_local_size {expected_local_size}'    )


if __name__ == "__main__":
    main()


