


"""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  __snake_case       (   UpperCamelCase__	)      ->		Optional[int]:







		"""simple docstring"""





		return EnvironmentCommand()







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		return EnvironmentCommand(args.accelerate_config_file	)



class 			lowerCamelCase__						(   UpperCAmelCase_			):







						@staticmethod
						def __a						(							_lowercase   :    ArgumentParser		):
								A					       = 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   :    Tuple			,			_lowercase   :    Optional[int]			,			*_lowercase   :    Tuple		):
								A					       = accelerate_config_file







						def __a						(							self   :    Union[str, Any]		):
								A					       = 'not installed'
								if is_safetensors_available():
										import safetensors

										A					       = safetensors.__version__
								elif importlib.util.find_spec('safetensors'		) is not None:
										import safetensors

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

								A					       = 'not installed'
								A					       = A					       = 'not found'
								if is_accelerate_available():
										import accelerate
										from accelerate.commands.config import default_config_file, load_config_from_file

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

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

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

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

								A					       = 'not installed'
								A					       = 'NA'
								if is_tf_available():
										import tensorflow as tf

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

								A					       = 'not installed'
								A					       = 'not installed'
								A					       = 'not installed'
								A					       = 'NA'
								if is_flax_available():
										import flax
										import jax
										import jaxlib

										A					       = flax.__version__
										A					       = jax.__version__
										A					       = jaxlib.__version__
										A					       = jax.lib.xla_bridge.get_backend().platform

								A					       = {
								    '`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 __a						(							_lowercase   :    Optional[Any]		):
								return "\n".join([f'- {prop}: {val}' for prop, val in d.items()]		) + "\n"









"""simple docstring"""


def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ = " "	)      ->		list:







		"""simple docstring"""





		A					       = []

		A					       = 0
		for index, char in enumerate(UpperCamelCase__	):
				if char == separator:
						split_words.append(string[last_index:index]	)
						A					       = index + 1
				elif index + 1 == len(UpperCamelCase__	):
						split_words.append(string[last_index : index + 1]	)
		return split_words


if __name__ == "__main__":
	from doctest import testmod

	testmod()





