
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 						UpperCAmelCase			(	a_						)		->	List[str]:







				"""simple docstring"""



				return EnvironmentCommand()


def 						UpperCAmelCase			(	a_						)		->	Union[str, Any]:







				"""simple docstring"""



				return EnvironmentCommand(args.accelerate_config_file						)





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	@staticmethod
	def 				UpperCamelCase_  (       A					: ArgumentParser ):
					__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					: str							,A					: str							,*A					: List[Any] ):
					__A   			= accelerate_config_file






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					__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 				UpperCamelCase_  (       A					: Any ):
					return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()] ) + "\n"





_lowercase: Dict      			=		[
    (1000, "M"),
    (900, "CM"),
    (500, "D"),
    (400, "CD"),
    (100, "C"),
    (90, "XC"),
    (50, "L"),
    (40, "XL"),
    (10, "X"),
    (9, "IX"),
    (5, "V"),
    (4, "IV"),
    (1, "I"),
]






def     a(      A       :    str       )		->       int:
					"""simple docstring"""





					a					     =   {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
					a					     =   0
					a					     =   0
					while place < len(A       ):
										if (place + 1 < len(A       )) and (vals[roman[place]] < vals[roman[place + 1]]):
															total += vals[roman[place + 1]] - vals[roman[place]]
															place += 2
										else:
															total += vals[roman[place]]
															place += 1
					return total






def     a(      A       :    int       )		->       str:
					"""simple docstring"""





					a					     =   []
					for arabic, roman in ROMAN:
										((a)     ,				(a))					     =   divmod(A ,     A       )
										result.append(roman * factor       )
										if number == 0:
															break
					return "".join(A       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
