






from __future__ import annotations

from typing import Any







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							list[Any]       ):
	create_state_space_tree(snake_case_					,  []					,  0       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							list[Any]					,  snake_case_ :							list[Any]					,  snake_case_ :							int       ):

	if index == len(snake_case_       ):
		print(snake_case_       )
		return

	create_state_space_tree(snake_case_					,  snake_case_					,  index + 1       )
	current_subsequence.append(sequence[index]       )
	create_state_space_tree(snake_case_					,  snake_case_					,  index + 1       )
	current_subsequence.pop()


if __name__ == "__main__":
	__lowerCamelCase :      list[Any]										=				[3, 1, 2, 4]
	generate_all_subsequences(seq)

	seq.clear()
	seq.extend(["""A""", """B""", """C"""])
	generate_all_subsequences(seq)











import os

import pytest
from attr import dataclass


__lowerCamelCase :      Any										=				"""us-east-1"""  # defaults region



@dataclass
class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					a_     =		42
					a_     =		"arn:aws:iam::558105141721:role/sagemaker_execution_role"
					a_     =		{
					    "task_name": "mnli",
					    "per_device_train_batch_size": 1_6,
					    "per_device_eval_batch_size": 1_6,
					    "do_train": True,
					    "do_eval": True,
					    "do_predict": True,
					    "output_dir": "/opt/ml/model",
					    "overwrite_output_dir": True,
					    "max_steps": 5_0_0,
					    "save_steps": 5_5_0_0,
					}
					a_     =		{**hyperparameters, "max_steps": 1_0_0_0}



					@property
					def 	_lowercase					( self					:  List[Any]	):
						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 	_lowercase					( self					:  Any	):
						return f'''{self.framework}-transfromers-test'''



					@property
					def 	_lowercase					( self					:  Optional[Any]	):
						return f'''./tests/sagemaker/scripts/{self.framework}'''






					@property
					def 	_lowercase					( self					:  Union[str, Any]	):
						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 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[Any]       ):
	snake_case__			:				Optional[int]       =  SageMakerTestEnvironment(framework=request.cls.framework       )



