





import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand



def A_	(		A__				)  ->		Any:
       return EnvironmentCommand()



def A_	(		A__				)  ->		str:
       return EnvironmentCommand(args.accelerate_config_file				)





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   @staticmethod
   def 							__lowercase		(					lowercase)			->						Optional[int]:







          '''simple docstring'''

          a__						:	Optional[Any] =				parser.add_parser('env')
          download_parser.set_defaults(func=lowercase)
          download_parser.add_argument(
              '--accelerate-config_file'					,  default=lowercase					,  help='The accelerate config file to use for the default values in the launching script.'					,  )
          download_parser.set_defaults(func=lowercase)

   def __init__(					self					,  lowercase					,  *lowercase)			->						None:







          '''simple docstring'''

          a__						:	Optional[int] =				accelerate_config_file

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__						:	List[Any] =				'not installed'
          if is_safetensors_available():
                 import safetensors

                 a__						:	Optional[int] =				safetensors.__version__
          elif importlib.util.find_spec('safetensors') is not None:
                 import safetensors

                 a__						:	Optional[Any] =				F'{safetensors.__version__} but is ignored because of PyTorch version too old.'

          a__						:	Any =				'not installed'
          a__						:	Optional[int] =				'not found'
          if is_accelerate_available():
                 import accelerate
                 from accelerate.commands.config import default_config_file, load_config_from_file

                 a__						:	Tuple =				accelerate.__version__
                 # Get the default from the config file.
                 if self._accelerate_config_file is not None or os.path.isfile(lowercase):
                        a__						:	Dict =				load_config_from_file(self._accelerate_config_file).to_dict()

                 a__						:	int =				(
                     '\n'.join([F'\t- {prop}: {val}' for prop, val in accelerate_config.items()])
                     if isinstance(lowercase					,  lowercase)
                     else F'\t{accelerate_config}'
                 )

          a__						:	List[Any] =				'not installed'
          a__						:	Optional[Any] =				'NA'
          if is_torch_available():
                 import torch

                 a__						:	Union[str, Any] =				torch.__version__
                 a__						:	Optional[int] =				torch.cuda.is_available()

          a__						:	Any =				'not installed'
          a__						:	Dict =				'NA'
          if is_tf_available():
                 import tensorflow as tf

                 a__						:	Optional[int] =				tf.__version__
                 try:
                        # deprecated in v2.1
                        a__						:	str =				tf.test.is_gpu_available()
                 except AttributeError:
                        # returns list of devices, convert to bool
                        a__						:	str =				bool(tf.config.list_physical_devices('GPU'))

          a__						:	List[str] =				'not installed'
          a__						:	Optional[Any] =				'not installed'
          a__						:	Optional[Any] =				'not installed'
          a__						:	Tuple =				'NA'
          if is_flax_available():
                 import flax
                 import jax
                 import jaxlib

                 a__						:	Any =				flax.__version__
                 a__						:	Union[str, Any] =				jax.__version__
                 a__						:	List[Any] =				jaxlib.__version__
                 a__						:	Dict =				jax.lib.xla_bridge.get_backend().platform

          a__						:	List[str] =				{
              '`transformers` version': version,
              'Platform': platform.platform(),
              'Python version': platform.python_version(),
              'Huggingface_hub version': huggingface_hub.__version__,
              'Safetensors version': F'{safetensors_version}',
              'Accelerate version': F'{accelerate_version}',
              'Accelerate config': F'{accelerate_config_str}',
              'PyTorch version (GPU?)': F'{pt_version} ({pt_cuda_available})',
              'Tensorflow version (GPU?)': F'{tf_version} ({tf_cuda_available})',
              'Flax version (CPU?/GPU?/TPU?)': F'{flax_version} ({jax_backend})',
              'Jax version': F'{jax_version}',
              'JaxLib version': F'{jaxlib_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(lowercase))

          return info

   @staticmethod
   def 							__lowercase		(					lowercase)			->						Optional[Any]:







          '''simple docstring'''

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









def A_	(		A__				)  ->		int:
       stooge(A__		,      0		,      len(A__				) - 1				)
       return arr



def A_	(		A__		,      A__		,      A__				)  ->		List[Any]:
       if i >= h:
              return

       # If first element is smaller than the last then swap them
       if arr[i] > arr[h]:
              a__ ,							a__						:	List[str] =				arr[h], arr[i]

       # If there are more than 2 elements in the array
       if h - i + 1 > 2:
              a__						:	Dict =				(int)((h - i + 1) / 3				)

              # Recursively sort first 2/3 elements
              stooge(A__		,      A__		,      (h - t)				)

              # Recursively sort last 2/3 elements
              stooge(A__		,      i + t		,      (A__)				)

              # Recursively sort first 2/3 elements
              stooge(A__		,      A__		,      (h - t)				)


if __name__ == "__main__":
    lowercase      :       Dict									=  input("""Enter numbers separated by a comma:\n""").strip()
    lowercase      :       Dict									=  [int(item) for item in user_input.split(""",""")]
    print(stooge_sort(unsorted))


