





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   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						Optional[int]     		=				ComputeEnvironment.AMAZON_SAGEMAKER
   _UpperCamelCase :						str     		=				True
   _UpperCamelCase :						str     		=				"""ml.p3.2xlarge"""
   _UpperCamelCase :						str     		=				"""accelerate_sagemaker_execution_role"""
   _UpperCamelCase :						Tuple     		=				"""hf-sm"""
   _UpperCamelCase :						List[str]     		=				"""us-east-1"""
   _UpperCamelCase :						Union[str, Any]     		=				1
   _UpperCamelCase :						Dict     		=				"""accelerate-sagemaker-1"""
   _UpperCamelCase :						List[Any]     		=				"""1.6"""
   _UpperCamelCase :						int     		=				"""4.4"""
   _UpperCamelCase :						Optional[Any]     		=				"""train.py"""
   _UpperCamelCase :						List[str]     		=				[
       """--model_name_or_path""",
       """bert""",
       """--do_train""",
       """False""",
       """--epochs""",
       """3""",
       """--learning_rate""",
       """5e-5""",
       """--max_steps""",
       """50.5""",
   ]
   _UpperCamelCase :						int     		=				[
       """--model_name_or_path""",
       """bert""",
       """--do_train""",
       """--do_test""",
       """False""",
       """--do_predict""",
       """--epochs""",
       """3""",
       """--learning_rate""",
       """5e-5""",
       """--max_steps""",
       """50.5""",
   ]







class   __lowerCAmelCase			(					unittest.TestCase  ):





   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      a__	:						Dict      		=  _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args      )
      assert isinstance(converted_args["model_name_or_path"]					,			snake_case      )
      assert isinstance(converted_args["do_train"]					,			snake_case      )
      assert isinstance(converted_args["epochs"]					,			snake_case      )
      assert isinstance(converted_args["learning_rate"]					,			snake_case      )
      assert isinstance(converted_args["max_steps"]					,			snake_case      )

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








from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE__       :			List[str]			      =						{"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						[
      """VAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """VanForImageClassification""",
      """VanModel""",
      """VanPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_van import (
        VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
        VanForImageClassification,
        VanModel,
        VanPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE__       :			List[str]			      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure)

