






import os
import sys

_a      	=  os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


_a      	=  [
    '''torch''',
    '''numpy''',
    '''tokenizers''',
    '''filelock''',
    '''requests''',
    '''tqdm''',
    '''regex''',
    '''sentencepiece''',
    '''sacremoses''',
    '''importlib_metadata''',
    '''huggingface_hub''',
]


@add_start_docstrings(AutoConfig.__doc__	)
def __A    (      *__lowerCAmelCase ,		**__lowerCAmelCase	)->  str:





						"""simple docstring"""



						return AutoConfig.from_pretrained(*__lowerCAmelCase ,		**__lowerCAmelCase	)


@add_start_docstrings(AutoTokenizer.__doc__	)
def __A    (      *__lowerCAmelCase ,		**__lowerCAmelCase	)->  Optional[int]:





						"""simple docstring"""



						return AutoTokenizer.from_pretrained(*__lowerCAmelCase ,		**__lowerCAmelCase	)


@add_start_docstrings(AutoModel.__doc__	)
def __A    (      *__lowerCAmelCase ,		**__lowerCAmelCase	)->  int:





						"""simple docstring"""



						return AutoModel.from_pretrained(*__lowerCAmelCase ,		**__lowerCAmelCase	)


@add_start_docstrings(AutoModelForCausalLM.__doc__	)
def __A    (      *__lowerCAmelCase ,		**__lowerCAmelCase	)->  Union[str, Any]:





						"""simple docstring"""



						return AutoModelForCausalLM.from_pretrained(*__lowerCAmelCase ,		**__lowerCAmelCase	)


@add_start_docstrings(AutoModelForMaskedLM.__doc__	)
def __A    (      *__lowerCAmelCase ,		**__lowerCAmelCase	)->  List[Any]:





						"""simple docstring"""



						return AutoModelForMaskedLM.from_pretrained(*__lowerCAmelCase ,		**__lowerCAmelCase	)


@add_start_docstrings(AutoModelForSequenceClassification.__doc__	)
def __A    (      *__lowerCAmelCase ,		**__lowerCAmelCase	)->  Optional[Any]:





						"""simple docstring"""



						return AutoModelForSequenceClassification.from_pretrained(*__lowerCAmelCase ,		**__lowerCAmelCase	)


@add_start_docstrings(AutoModelForQuestionAnswering.__doc__	)
def __A    (      *__lowerCAmelCase ,		**__lowerCAmelCase	)->  Tuple:





						"""simple docstring"""



						return AutoModelForQuestionAnswering.from_pretrained(*__lowerCAmelCase ,		**__lowerCAmelCase	)

import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict







@dataclass
class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							ComputeEnvironment.AMAZON_SAGEMAKER
   lowerCAmelCase_														=							True
   lowerCAmelCase_														=							"ml.p3.2xlarge"
   lowerCAmelCase_														=							"accelerate_sagemaker_execution_role"
   lowerCAmelCase_														=							"hf-sm"
   lowerCAmelCase_														=							"us-east-1"
   lowerCAmelCase_														=							1
   lowerCAmelCase_														=							"accelerate-sagemaker-1"
   lowerCAmelCase_														=							"1.6"
   lowerCAmelCase_														=							"4.4"
   lowerCAmelCase_														=							"train.py"
   lowerCAmelCase_														=							[
       "--model_name_or_path",
       "bert",
       "--do_train",
       "False",
       "--epochs",
       "3",
       "--learning_rate",
       "5e-5",
       "--max_steps",
       "50.5",
   ]
   lowerCAmelCase_														=							[
       "--model_name_or_path",
       "bert",
       "--do_train",
       "--do_test",
       "False",
       "--do_predict",
       "--epochs",
       "3",
       "--learning_rate",
       "5e-5",
       "--max_steps",
       "50.5",
   ]







class __snake_case				(						unittest.TestCase		):




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args      )
     assert isinstance(converted_args["""model_name_or_path"""]       ,	_lowercase      )
     assert isinstance(converted_args["""do_train"""]       ,	_lowercase      )
     assert isinstance(converted_args["""epochs"""]       ,	_lowercase      )
     assert isinstance(converted_args["""learning_rate"""]       ,	_lowercase      )
     assert isinstance(converted_args["""max_steps"""]       ,	_lowercase      )

     with pytest.raises(_lowercase      ):
       _convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args      )
