


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



class 						__a (							unittest.TestCase     ):
		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args		)
									assert isinstance(converted_args['model_name_or_path']     ,       _SCREAMING_SNAKE_CASE		)
									assert isinstance(converted_args['do_train']     ,       _SCREAMING_SNAKE_CASE		)
									assert isinstance(converted_args['epochs']     ,       _SCREAMING_SNAKE_CASE		)
									assert isinstance(converted_args['learning_rate']     ,       _SCREAMING_SNAKE_CASE		)
									assert isinstance(converted_args['max_steps']     ,       _SCREAMING_SNAKE_CASE		)

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




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase__    :Tuple       =				{'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnnxConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :List[Any]       =				['''DeiTFeatureExtractor''']
		lowerCAmelCase__    :Union[str, Any]       =				['''DeiTImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :Tuple       =				[
		    '''DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''DeiTForImageClassification''',
		    '''DeiTForImageClassificationWithTeacher''',
		    '''DeiTForMaskedImageModeling''',
		    '''DeiTModel''',
		    '''DeiTPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :Union[str, Any]       =				[
		    '''TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFDeiTForImageClassification''',
		    '''TFDeiTForImageClassificationWithTeacher''',
		    '''TFDeiTForMaskedImageModeling''',
		    '''TFDeiTModel''',
		    '''TFDeiTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

		lowerCAmelCase__    :List[str]       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
