

import argparse
import os
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available

def 						UpperCamelCase (lowercase_:			Tuple=None )					->				int:
 if subparsers is not None:
  A__							:   str					       =		subparsers.add_parser("""env""" )
 else:
  A__							:   Dict					       =		argparse.ArgumentParser("""Accelerate env command""" )

 parser.add_argument(
     """--config_file"""   ,				default=lowercase__   ,				help="""The config file to use for the default values in the launching script.""" )

 if subparsers is not None:
  parser.set_defaults(func=lowercase__ )
 return parser

def 						UpperCamelCase (lowercase_:			Optional[int] )					->				Optional[Any]:
 A__							:   str					       =		torch.__version__
 A__							:   Tuple					       =		torch.cuda.is_available()
 A__							:   Union[str, Any]					       =		is_xpu_available()
 A__							:   int					       =		is_npu_available()

 A__							:   Union[str, Any]					       =		"""Not found"""
 # Get the default from the config file.
 if args.config_file is not None or os.path.isfile(lowercase__ ):
  A__							:   Tuple					       =		load_config_from_file(args.config_file ).to_dict()

 A__							:   str					       =		{
     """`Accelerate` version""": version,
     """Platform""": platform.platform(),
     """Python version""": platform.python_version(),
     """Numpy version""": np.__version__,
     """PyTorch version (GPU?)""": f"""{pt_version} ({pt_cuda_available})""",
     """PyTorch XPU available""": str(lowercase__ ),
     """PyTorch NPU available""": str(lowercase__ ),
     """System RAM""": f"""{psutil.virtual_memory().total / 1024 ** 3:.2f} GB""",
 }
 if pt_cuda_available:
  A__							:   Any					       =		torch.cuda.get_device_name()

 print("""\nCopy-and-paste the text below in your GitHub issue\n""" )
 print("""\n""".join([f"""- {prop}: {val}""" for prop, val in info.items()] ) )

 print("""- `Accelerate` default config:""" if args.config_file is None else """- `Accelerate` config passed:""" )
 A__							:   int					       =		(
     """\n""".join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()] )
     if isinstance(lowercase__   ,				lowercase__ )
     else f"""\t{accelerate_config}"""
 )
 print(lowercase__ )

 A__							:   List[Any]					       =		accelerate_config

 return info

def 						UpperCamelCase ()					->				str:
 A__							:   Tuple					       =		env_command_parser()
 A__							:   Any					       =		parser.parse_args()
 env_command(lowercase__ )
 return 0


if __name__ == "__main__":
 raise SystemExit(main())






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


A_	:			List[Any]  	=      {'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[Any]  	=      ['GPTNeoXTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[int]  	=      [
	    'GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GPTNeoXForCausalLM',
	    'GPTNeoXForQuestionAnswering',
	    'GPTNeoXForSequenceClassification',
	    'GPTNeoXForTokenClassification',
	    'GPTNeoXLayer',
	    'GPTNeoXModel',
	    'GPTNeoXPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	A_	:			Any  	=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



