
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


lowercase						:      Optional[Any]									= 'scheduler_config.json'




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  1
	_A			=  2
	_A			=  3
	_A			=  4
	_A			=  5
	_A			=  6
	_A			=  7
	_A			=  8
	_A			=  9
	_A			=  1_0
	_A			=  1_1
	_A			=  1_2
	_A			=  1_3
	_A			=  1_4




@dataclass
class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  42




class 				lowerCamelCase__							:




	'''simple docstring'''




	_A			=  SCHEDULER_CONFIG_NAME
	_A			=  []
	_A			=  True







	@classmethod
	def      _lowerCamelCase       (      cls	:int  ,     a	:Dict[str, Any] = None  ,     a	:Optional[str] = None  ,     a	:Tuple=False  ,     **a	:List[Any]  ,     )	->				str:
								__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Tuple         =      cls.load_config(
								    pretrained_model_name_or_path=a  ,     subfolder=a  ,     return_unused_kwargs=a  ,     return_commit_hash=a  ,     **a  ,     )
								return cls.from_config(a  ,     return_unused_kwargs=a  ,     **a		)







	def      _lowerCamelCase       (      self	:Dict  ,     a	:Union[str, os.PathLike]  ,     a	:bool = False  ,     **a	:List[Any]		)	->				Optional[Any]:
								self.save_config(save_directory=a  ,     push_to_hub=a  ,     **a		)







	@property
	def      _lowerCamelCase       (      self	:Optional[int]		)	->				Dict:
								return self._get_compatibles()







	@classmethod
	def      _lowerCamelCase       (      cls	:List[Any]		)	->				Optional[Any]:
								__UpperCamelCase			:     str         =      list(set([cls.__name__] + cls._compatibles		)		)
								__UpperCamelCase			:     Union[str, Any]         =      importlib.import_module(__name__.split("."		)[0]		)
								__UpperCamelCase			:     Any         =      [
								    getattr(a  ,     a		) for c in compatible_classes_str if hasattr(a  ,     a		)
								]
								return compatible_classes

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__							(       __lowercase):




	'''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 				lowerCamelCase__							(       unittest.TestCase):




	'''simple docstring'''




	def      _lowerCamelCase       (      self	:Tuple		)	->				str:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								__UpperCamelCase			:     str         =      _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		)