



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



			def     _lowerCamelCase       (						self   :  Optional[Any])   ->    Dict:



						"""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 Dict

from .base import GenericTensor, Pipeline






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       **_SCREAMING_SNAKE_CASE		)->List[Any]:




         '''simple docstring'''





         if tokenize_kwargs is None:
           A_      :				Optional[int]		     =					{}

         if truncation is not None:
           if "truncation" in tokenize_kwargs:
             raise ValueError(
                 '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''		)
           A_      :				List[str]		     =					truncation

         A_      :				str		     =					tokenize_kwargs

         A_      :				Optional[Any]		     =					{}
         if return_tensors is not None:
           A_      :				Union[str, Any]		     =					return_tensors

         return preprocess_params, {}, postprocess_params



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->Dict[str, GenericTensor]:




         '''simple docstring'''





         A_      :				str		     =					self.framework
         A_      :				Any		     =					self.tokenizer(_SCREAMING_SNAKE_CASE				,       return_tensors=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)
         return model_inputs



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Optional[Any]:




         '''simple docstring'''





         A_      :				Optional[int]		     =					self.model(**_SCREAMING_SNAKE_CASE		)
         return model_outputs



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=False		)->List[Any]:




         '''simple docstring'''





         if return_tensors:
           return model_outputs[0]
         if self.framework == "pt":
           return model_outputs[0].tolist()
         elif self.framework == "tf":
           return model_outputs[0].numpy().tolist()







       def __call__(      self				,       *_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->Union[str, Any]:




         '''simple docstring'''





         return super().__call__(*_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)




