


from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream






class      UpperCamelCase(	UpperCamelCase__ ):


					def __init__(    self						:		str  ,     SCREAMING_SNAKE_CASE						:		int  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any] = None  ,     SCREAMING_SNAKE_CASE						:		str = None  ,     SCREAMING_SNAKE_CASE						:		Optional[Any] = False  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any] = False  ,     SCREAMING_SNAKE_CASE						:		Tuple = None  ,     SCREAMING_SNAKE_CASE						:		Optional[int] = None  ,     **SCREAMING_SNAKE_CASE						:		Any  ,     )      ->						List[str]:




											'''simple docstring'''

											super().__init__(
											    features=__lowerCamelCase  ,     cache_dir=__lowerCamelCase  ,     keep_in_memory=__lowerCamelCase  ,     streaming=__lowerCamelCase  ,     num_proc=__lowerCamelCase  ,     **__lowerCamelCase  ,     )
											__snake_case				=		Generator(
											    cache_dir=__lowerCamelCase  ,     features=__lowerCamelCase  ,     generator=__lowerCamelCase  ,     gen_kwargs=__lowerCamelCase  ,     **__lowerCamelCase  ,     )






					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]    )      ->						Optional[Any]:




											'''simple docstring'''

											if self.streaming:
																	__snake_case				=		self.builder.as_streaming_dataset(split="train"    )
											# Build regular (map-style) dataset
											else:
																	__snake_case				=		None
																	__snake_case				=		None
																	__snake_case				=		None
																	__snake_case				=		None

																	self.builder.download_and_prepare(
																	    download_config=__lowerCamelCase  ,     download_mode=__lowerCamelCase  ,     verification_mode=__lowerCamelCase  ,     base_path=__lowerCamelCase  ,     num_proc=self.num_proc  ,     )
																	__snake_case				=		self.builder.as_dataset(
																	    split="train"  ,     verification_mode=__lowerCamelCase  ,     in_memory=self.keep_in_memory    )
											return dataset












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



 def 							UpperCamelCase     (				self					):
        # If no defaults are changed, `to_kwargs` returns an empty dict.
        A__		   =		_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args					)
        assert isinstance(converted_args['''model_name_or_path'''],__lowerCamelCase					)
        assert isinstance(converted_args['''do_train'''],__lowerCamelCase					)
        assert isinstance(converted_args['''epochs'''],__lowerCamelCase					)
        assert isinstance(converted_args['''learning_rate'''],__lowerCamelCase					)
        assert isinstance(converted_args['''max_steps'''],__lowerCamelCase					)

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





