






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__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		ComputeEnvironment.AMAZON_SAGEMAKER
					a_     =		True
					a_     =		"ml.p3.2xlarge"
					a_     =		"accelerate_sagemaker_execution_role"
					a_     =		"hf-sm"
					a_     =		"us-east-1"
					a_     =		1
					a_     =		"accelerate-sagemaker-1"
					a_     =		"1.6"
					a_     =		"4.4"
					a_     =		"train.py"
					a_     =		[
					    "--model_name_or_path",
					    "bert",
					    "--do_train",
					    "False",
					    "--epochs",
					    "3",
					    "--learning_rate",
					    "5e-5",
					    "--max_steps",
					    "50.5",
					]
					a_     =		[
					    "--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						):





					"""simple docstring"""






					def 	_lowercase					( self					:  List[Any]	):
						# If no defaults are changed, `to_kwargs` returns an empty dict.
						snake_case__			:				Optional[int]       =  _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

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase :      Union[str, Any]										=				{
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase :      Tuple										=				[
	    """TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TimeSeriesTransformerForPrediction""",
	    """TimeSeriesTransformerModel""",
	    """TimeSeriesTransformerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_time_series_transformer import (
	    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TimeSeriesTransformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimeSeriesTransformerForPrediction,
		    TimeSeriesTransformerModel,
		    TimeSeriesTransformerPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase :      Tuple										=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



