



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 __lowercase    (							_a=None					):
    if subparsers is not None:
        snake_case_				:  Any       =			subparsers.add_parser('''env'''					)
    else:
        snake_case_				:  str       =			argparse.ArgumentParser('''Accelerate env command'''					)

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

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




def __lowercase    (							_a					):
    snake_case_				:  Optional[Any]       =			torch.__version__
    snake_case_				:  int       =			torch.cuda.is_available()
    snake_case_				:  str       =			is_xpu_available()
    snake_case_				:  int       =			is_npu_available()

    snake_case_				:  Union[str, Any]       =			'''Not found'''
    # Get the default from the config file.
    if args.config_file is not None or os.path.isfile(_a					):
        snake_case_				:  Optional[int]       =			load_config_from_file(args.config_file					).to_dict()

    snake_case_				:  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(_a					),
        '''PyTorch NPU available''': str(_a					),
        '''System RAM''': f"{psutil.virtual_memory().total / 1_024 ** 3:.2f} GB",
    }
    if pt_cuda_available:
        snake_case_				:  List[str]       =			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:'''					)
    snake_case_				:  str       =			(
        '''\n'''.join([f"\t- {prop}: {val}" for prop, val in accelerate_config.items()]					)
        if isinstance(_a	,    _a					)
        else f"\t{accelerate_config}"
    )
    print(_a					)

    snake_case_				:  List[Any]       =			accelerate_config

    return info




def __lowercase    (							):
    snake_case_				:  Dict       =			env_command_parser()
    snake_case_				:  Optional[int]       =			parser.parse_args()
    env_command(_a					)
    return 0


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










"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__					:						Any												=			logging.get_logger(__name__)

lowercase__					:						Tuple												=			{
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Dict	      =	"""roberta"""

 def __init__(  self	: int						,  lowercase_	: Any=50265						,  lowercase_	: Union[str, Any]=768						,  lowercase_	: Optional[int]=12						,  lowercase_	: List[str]=12						,  lowercase_	: Dict=3072						,  lowercase_	: Tuple="gelu"						,  lowercase_	: Optional[int]=0.1						,  lowercase_	: Tuple=0.1						,  lowercase_	: List[str]=512						,  lowercase_	: List[Any]=2						,  lowercase_	: Union[str, Any]=0.02						,  lowercase_	: List[Any]=1E-12						,  lowercase_	: Optional[int]=1						,  lowercase_	: Tuple=0						,  lowercase_	: str=2						,  lowercase_	: int="absolute"						,  lowercase_	: str=True						,  lowercase_	: Tuple=None						,  **lowercase_	: str						,  ):
     super().__init__(pad_token_id=lowercase_						,  bos_token_id=lowercase_						,  eos_token_id=lowercase_						,  **lowercase_       )

     snake_case_				:  str       =			vocab_size
     snake_case_				:  Optional[Any]       =			hidden_size
     snake_case_				:  Any       =			num_hidden_layers
     snake_case_				:  List[str]       =			num_attention_heads
     snake_case_				:  Dict       =			hidden_act
     snake_case_				:  List[Any]       =			intermediate_size
     snake_case_				:  Optional[int]       =			hidden_dropout_prob
     snake_case_				:  List[str]       =			attention_probs_dropout_prob
     snake_case_				:  Any       =			max_position_embeddings
     snake_case_				:  Optional[Any]       =			type_vocab_size
     snake_case_				:  Optional[int]       =			initializer_range
     snake_case_				:  Dict       =			layer_norm_eps
     snake_case_				:  Optional[int]       =			position_embedding_type
     snake_case_				:  str       =			use_cache
     snake_case_				:  Tuple       =			classifier_dropout





class  _UpperCAmelCase (    lowerCAmelCase__):

 @property
 def 		_snake_case      (  self	: Any       ):
     if self.task == "multiple-choice":
         snake_case_				:  List[Any]       =			{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
         snake_case_				:  Union[str, Any]       =			{0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
         ]       )





