

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 						__magic_name__			(							A__							):

   '''simple docstring'''




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

   '''simple docstring'''




   def  _UpperCAmelCase (				self       )							->    List[Any]:






     """simple docstring"""






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

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




'''simple docstring'''
from typing import TYPE_CHECKING

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


__snake_case							      =     {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							      =     ['''DPTFeatureExtractor''']
		__snake_case							      =     ['''DPTImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							      =     [
		    '''DPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''DPTForDepthEstimation''',
		    '''DPTForSemanticSegmentation''',
		    '''DPTModel''',
		    '''DPTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_dpt import DPTFeatureExtractor
				from .image_processing_dpt import DPTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_dpt import (
				    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DPTForDepthEstimation,
				    DPTForSemanticSegmentation,
				    DPTModel,
				    DPTPreTrainedModel,
				)


else:
		import sys

		__snake_case							      =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)