"""simple docstring"""




import os
import sys

__SCREAMING_SNAKE_CASE						:   int         =			os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


__SCREAMING_SNAKE_CASE						:   Dict         =			[
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]
@add_start_docstrings(AutoConfig.__doc__						)
def  snake_case						(*__lowercase ,      **__lowercase						)	->	Dict:

						'''simple docstring'''



						return AutoConfig.from_pretrained(*__lowercase ,      **__lowercase						)
@add_start_docstrings(AutoTokenizer.__doc__						)
def  snake_case						(*__lowercase ,      **__lowercase						)	->	int:

						'''simple docstring'''



						return AutoTokenizer.from_pretrained(*__lowercase ,      **__lowercase						)
@add_start_docstrings(AutoModel.__doc__						)
def  snake_case						(*__lowercase ,      **__lowercase						)	->	int:

						'''simple docstring'''



						return AutoModel.from_pretrained(*__lowercase ,      **__lowercase						)
@add_start_docstrings(AutoModelForCausalLM.__doc__						)
def  snake_case						(*__lowercase ,      **__lowercase						)	->	List[Any]:

						'''simple docstring'''



						return AutoModelForCausalLM.from_pretrained(*__lowercase ,      **__lowercase						)
@add_start_docstrings(AutoModelForMaskedLM.__doc__						)
def  snake_case						(*__lowercase ,      **__lowercase						)	->	Optional[int]:

						'''simple docstring'''



						return AutoModelForMaskedLM.from_pretrained(*__lowercase ,      **__lowercase						)
@add_start_docstrings(AutoModelForSequenceClassification.__doc__						)
def  snake_case						(*__lowercase ,      **__lowercase						)	->	Tuple:

						'''simple docstring'''



						return AutoModelForSequenceClassification.from_pretrained(*__lowercase ,      **__lowercase						)

@add_start_docstrings(AutoModelForQuestionAnswering.__doc__						)
def  snake_case						(*__lowercase ,      **__lowercase						)	->	Tuple:

						'''simple docstring'''



						return AutoModelForQuestionAnswering.from_pretrained(*__lowercase ,      **__lowercase						)
import os

import pytest
from attr import dataclass


__SCREAMING_SNAKE_CASE						:   int         =			'us-east-1'  # defaults region



@dataclass
class        lowercase_   :
							_lowerCamelCase						=		42
							_lowerCamelCase						=		'arn:aws:iam::558105141721:role/sagemaker_execution_role'
							_lowerCamelCase						=		{
							    'task_name': 'mnli',
							    'per_device_train_batch_size': 16,
							    'per_device_eval_batch_size': 16,
							    'do_train': True,
							    'do_eval': True,
							    'do_predict': True,
							    'output_dir': '/opt/ml/model',
							    'overwrite_output_dir': True,
							    'max_steps': 500,
							    'save_steps': 5_500,
							}
							_lowerCamelCase						=		{**hyperparameters, 'max_steps': 1_000}



							@property
							def 	UpperCamelCase	(						self    ):
													if self.framework == "pytorch":
																			return [
																			    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
																			    {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
																			    {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
																			]
													else:
																			return [
																			    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
																			    {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
																			    {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
																			]



							@property
							def 	UpperCamelCase	(						self    ):
													return f"""{self.framework}-transfromers-test"""



							@property
							def 	UpperCamelCase	(						self    ):
													return f"""./tests/sagemaker/scripts/{self.framework}"""



							@property
							def 	UpperCamelCase	(						self    ):
													if self.framework == "pytorch":
																			return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
													else:
																			return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"




@pytest.fixture(scope="class"						)
def  snake_case						(__lowercase						)	->	str:

						'''simple docstring'''



						_snake_case						:					List[str]    =   SageMakerTestEnvironment(framework=request.cls.framework						)