

'''simple docstring'''
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		(     snake_case       )      ->     Dict:
   return EnvironmentCommand()







def  _A		(     snake_case       )      ->     Any:
   return EnvironmentCommand(args.accelerate_config_file       )


class        a__  (							a__  ):


   @staticmethod
   def 						_lowerCamelCase       (			_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				str      					=       parser.add_parser("env"  )
      download_parser.set_defaults(func=_lowerCamelCase  )
      download_parser.add_argument(
          "--accelerate-config_file"      ,    default=_lowerCamelCase      ,    help="The accelerate config file to use for the default values in the launching script."      ,    )
      download_parser.set_defaults(func=_lowerCamelCase  )


   def __init__(			self      ,    _UpperCamelCase      ,    *_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       accelerate_config_file


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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

         _lowercase      :				Any      					=       safetensors.__version__
      elif importlib.util.find_spec("safetensors"  ) is not None:
         import safetensors

         _lowercase      :				int      					=       f'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

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

         _lowercase      :				List[Any]      					=       accelerate.__version__
         # Get the default from the config file.
         if self._accelerate_config_file is not None or os.path.isfile(_lowerCamelCase  ):
            _lowercase      :				Union[str, Any]      					=       load_config_from_file(self._accelerate_config_file  ).to_dict()

         _lowercase      :				Tuple      					=       (
             '''\n'''.join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]  )
             if isinstance(_lowerCamelCase      ,    _lowerCamelCase  )
             else f'''\t{accelerate_config}'''
         )

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

         _lowercase      :				List[str]      					=       torch.__version__
         _lowercase      :				Optional[Any]      					=       torch.cuda.is_available()

      _lowercase      :				str      					=       '''not installed'''
      _lowercase      :				Any      					=       '''NA'''
      if is_tf_available():
         import tensorflow as tf

         _lowercase      :				int      					=       tf.__version__
         try:
            # deprecated in v2.1
            _lowercase      :				Dict      					=       tf.test.is_gpu_available()
         except AttributeError:
            # returns list of devices, convert to bool
            _lowercase      :				Any      					=       bool(tf.config.list_physical_devices("GPU"  )  )

      _lowercase      :				Any      					=       '''not installed'''
      _lowercase      :				Union[str, Any]      					=       '''not installed'''
      _lowercase      :				int      					=       '''not installed'''
      _lowercase      :				Union[str, Any]      					=       '''NA'''
      if is_flax_available():
         import flax
         import jax
         import jaxlib

         _lowercase      :				Optional[int]      					=       flax.__version__
         _lowercase      :				Tuple      					=       jax.__version__
         _lowercase      :				Optional[int]      					=       jaxlib.__version__
         _lowercase      :				List[Any]      					=       jax.lib.xla_bridge.get_backend().platform

      _lowercase      :				Any      					=       {
          '''`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(_lowerCamelCase  )  )

      return info






   @staticmethod
   def 						_lowerCamelCase       (			_UpperCamelCase  ):





      """simple docstring"""





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









'''simple docstring'''
def  _A		(     snake_case		,							snake_case					)      ->     int:
   return int((input_a, input_a).count(0					) != 0					)







def  _A		(     )      ->     None:
   assert nand_gate(0		,							0					) == 1
   assert nand_gate(0		,							1					) == 1
   assert nand_gate(1		,							0					) == 1
   assert nand_gate(1		,							1					) == 0


if __name__ == "__main__":
    print(nand_gate(0, 0))
    print(nand_gate(0, 1))
    print(nand_gate(1, 0))
    print(nand_gate(1, 1))






