
"""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					(		snake_case__ :  Tuple  ):
	return EnvironmentCommand()






def    _snake_case					(		snake_case__ :  List[str]  ):
	return EnvironmentCommand(args.accelerate_config_file  )







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						@staticmethod
						def     _SCREAMING_SNAKE_CASE   (			A_  :   ArgumentParser   )  -> Any:
							A     = parser.add_parser('env'   )
							download_parser.set_defaults(func=A_   )
							download_parser.add_argument(
							    '--accelerate-config_file'	,default=A_	,help='The accelerate config file to use for the default values in the launching script.'	,)
							download_parser.set_defaults(func=A_   )


						def __init__(			self  :   List[Any]	,A_  :   Optional[int]	,*A_  :   Tuple   )  -> None:
							A     = accelerate_config_file


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> 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(A_   ):
									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(A_	,A_   )
								    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(A_   )   )

							return info




						@staticmethod
						def     _SCREAMING_SNAKE_CASE   (			A_  :   Union[str, Any]   )  -> Dict:
							return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]   ) + "\n"



'''simple docstring'''

from math import sqrt

def _SCREAMING_SNAKE_CASE (A = 1_000_000			)			->			int:






       """simple docstring"""







       lowercase__      =					0
       lowercase__      =					0
       lowercase__      =					42

       while num_cuboids <= limit:
              max_cuboid_size += 1
              for sum_shortest_sides in range(2    ,					2 * max_cuboid_size + 1			):
                     if sqrt(sum_shortest_sides**2 + max_cuboid_size**2			).is_integer():
                            num_cuboids += (
                                min(A    ,					sum_shortest_sides // 2			)
                                - max(1    ,					sum_shortest_sides - max_cuboid_size			)
                                + 1
                            )

       return max_cuboid_size


if __name__ == "__main__":
 print(f"""{solution() = }""")




