






"""simple docstring"""






import os

import pytest
from attr import dataclass


SCREAMING_SNAKE_CASE						:						Union[str, Any]					   =       '''us-east-1'''  # defaults region





@dataclass
class       snake_case_       :
	SCREAMING_SNAKE_CASE_:							str
	SCREAMING_SNAKE_CASE_:							Optional[Any]   			=					"""arn:aws:iam::558105141721:role/sagemaker_execution_role"""
	SCREAMING_SNAKE_CASE_:							Tuple   			=					{
	    """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""": 5_00,
	    """save_steps""": 55_00,
	}
	SCREAMING_SNAKE_CASE_:							int   			=					{**hyperparameters, """max_steps""": 10_00}


	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					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						):


					"""simple docstring"""

					return f'''{self.framework}-transfromers-test'''


	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return f'''./tests/sagemaker/scripts/{self.framework}'''






	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					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  __lowerCamelCase					(					lowerCAmelCase__				):
				A__							 =       SageMakerTestEnvironment(framework=request.cls.framework				)










"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE						:						Any					   =       logging.get_logger(__name__)





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					"""timm_backbone"""


	def __init__(      self  ,					__a=None  ,					__a=3  ,					__a=True  ,					__a=True  ,					__a=None  ,					**__a  ,					):


					"""simple docstring"""

					super().__init__(**__a						)
					A__							 =       backbone
					A__							 =       num_channels
					A__							 =       features_only
					A__							 =       use_pretrained_backbone
					A__							 =       True
					A__							 =       out_indices if out_indices is not None else (-1,)


