


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 lowerCamelCase      (      lowerCAmelCase    :     Optional[int]=None		):





	"""simple docstring"""







	if subparsers is not None:
		__magic_name__	:   Tuple					     =					subparsers.add_parser('env'		)
	else:
		__magic_name__	:   Union[str, Any]					     =					argparse.ArgumentParser('Accelerate env command'		)

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

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





def lowerCamelCase      (      lowerCAmelCase    :     List[Any]		):





	"""simple docstring"""







	__magic_name__	:   Union[str, Any]					     =					torch.__version__
	__magic_name__	:   str					     =					torch.cuda.is_available()
	__magic_name__	:   Dict					     =					is_xpu_available()
	__magic_name__	:   Optional[Any]					     =					is_npu_available()

	__magic_name__	:   List[Any]					     =					'Not found'
	# Get the default from the config file.
	if args.config_file is not None or os.path.isfile(lowerCAmelCase		):
		__magic_name__	:   Dict					     =					load_config_from_file(args.config_file		).to_dict()

	__magic_name__	:   List[Any]					     =					{
	    '`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(lowerCAmelCase		),
	    'PyTorch NPU available': str(lowerCAmelCase		),
	    'System RAM': f'{psutil.virtual_memory().total / 1024 ** 3:.2f} GB',
	}
	if pt_cuda_available:
		__magic_name__	:   List[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:'		)
	__magic_name__	:   Any					     =					(
	    '\n'.join([f'\t- {prop}: {val}' for prop, val in accelerate_config.items()]		)
	    if isinstance(lowerCAmelCase		,		lowerCAmelCase		)
	    else f'\t{accelerate_config}'
	)
	print(lowerCAmelCase		)

	__magic_name__	:   str					     =					accelerate_config

	return info





def lowerCamelCase      (      ):





	"""simple docstring"""







	__magic_name__	:   Optional[int]					     =					env_command_parser()
	__magic_name__	:   Tuple					     =					parser.parse_args()
	env_command(lowerCAmelCase		)
	return 0


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



'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase				:Any        =      logging.get_logger(__name__)

lowerCAmelCase				:int        =      {
    '''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json''',
}
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      List[str]				   =    """lxmert"""
		A_     :      int				   =    {}







		def __init__( self		:						int  ,					_A		:						List[Any]=30522  ,					_A		:						int=768  ,					_A		:						Any=12  ,					_A		:						Optional[Any]=9500  ,					_A		:						Any=1600  ,					_A		:						Tuple=400  ,					_A		:						str=3072  ,					_A		:						Tuple="gelu"  ,					_A		:						int=0.1  ,					_A		:						List[Any]=0.1  ,					_A		:						Dict=512  ,					_A		:						Dict=2  ,					_A		:						Tuple=0.02  ,					_A		:						List[Any]=1E-12  ,					_A		:						Optional[Any]=9  ,					_A		:						List[Any]=5  ,					_A		:						str=5  ,					_A		:						int=2048  ,					_A		:						Tuple=4  ,					_A		:						Tuple=6.67  ,					_A		:						Dict=True  ,					_A		:						str=True  ,					_A		:						str=True  ,					_A		:						Dict=True  ,					_A		:						str=True  ,					_A		:						int=True  ,					_A		:						Tuple=True  ,					**_A		:						int  ,					)  ->			str:
			__magic_name__	:   List[Any]					     =					vocab_size
			__magic_name__	:   List[Any]					     =					hidden_size
			__magic_name__	:   Optional[int]					     =					num_attention_heads
			__magic_name__	:   Dict					     =					hidden_act
			__magic_name__	:   Optional[Any]					     =					intermediate_size
			__magic_name__	:   str					     =					hidden_dropout_prob
			__magic_name__	:   List[str]					     =					attention_probs_dropout_prob
			__magic_name__	:   Tuple					     =					max_position_embeddings
			__magic_name__	:   Dict					     =					type_vocab_size
			__magic_name__	:   str					     =					initializer_range
			__magic_name__	:   str					     =					layer_norm_eps
			__magic_name__	:   Union[str, Any]					     =					num_qa_labels
			__magic_name__	:   str					     =					num_object_labels
			__magic_name__	:   List[str]					     =					num_attr_labels
			__magic_name__	:   Tuple					     =					l_layers
			__magic_name__	:   List[Any]					     =					x_layers
			__magic_name__	:   Optional[int]					     =					r_layers
			__magic_name__	:   Dict					     =					visual_feat_dim
			__magic_name__	:   Optional[int]					     =					visual_pos_dim
			__magic_name__	:   Optional[int]					     =					visual_loss_normalizer
			__magic_name__	:   int					     =					task_matched
			__magic_name__	:   Dict					     =					task_mask_lm
			__magic_name__	:   List[Any]					     =					task_obj_predict
			__magic_name__	:   str					     =					task_qa
			__magic_name__	:   List[Any]					     =					visual_obj_loss
			__magic_name__	:   Dict					     =					visual_attr_loss
			__magic_name__	:   int					     =					visual_feat_loss
			__magic_name__	:   Tuple					     =					{'vision': r_layers, 'cross_encoder': x_layers, 'language': l_layers}
			super().__init__(**_A      )