





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






						def UpperCAmelCase_			( self : Any		)							->      int:







							"""simple docstring"""


							snake_case_						:     Any     =		_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args		)
							assert isinstance(converted_args['model_name_or_path']						,				_A		)
							assert isinstance(converted_args['do_train']						,				_A		)
							assert isinstance(converted_args['epochs']						,				_A		)
							assert isinstance(converted_args['learning_rate']						,				_A		)
							assert isinstance(converted_args['max_steps']						,				_A		)

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








from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE 		=						{
    """configuration_informer""": [
        """INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InformerConfig""",
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE 		=						[
						    """INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """InformerForPrediction""",
						    """InformerModel""",
						    """InformerPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_informer import (
												    INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    InformerForPrediction,
												    InformerModel,
												    InformerPreTrainedModel,
												)

else:
						import sys

						_SCREAMING_SNAKE_CASE 		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

