
"""simple docstring"""







import os

import pytest
from attr import dataclass


_SCREAMING_SNAKE_CASE	       =      """us-east-1"""  # defaults region







@dataclass
class 							__magic_name__     :
					_SCREAMING_SNAKE_CASE       :					str
					_SCREAMING_SNAKE_CASE       :					Optional[Any]     							=   'arn:aws:iam::558105141721:role/sagemaker_execution_role'
					_SCREAMING_SNAKE_CASE       :					Union[str, Any]     							=   {
					    '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': 5500,
					}
					_SCREAMING_SNAKE_CASE       :					Dict     							=   {**hyperparameters, 'max_steps': 1000}





					@property
					def 							lowerCAmelCase      (				self   :							Optional[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 							lowerCAmelCase      (				self   :							str    ):
												return F'''{self.framework}-transfromers-test'''





					@property
					def 							lowerCAmelCase      (				self   :							List[str]    ):
												return F'''./tests/sagemaker/scripts/{self.framework}'''





					@property
					def 							lowerCAmelCase      (				self   :							Optional[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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Any:







							"""simple docstring"""


							__snake_case     			=       SageMakerTestEnvironment(framework=request.cls.framework )


"""simple docstring"""







from collections.abc import Sequence
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE = None )  ->     int:







							"""simple docstring"""


							if nums is None or not nums:
														raise ValueError("Input sequence should not be empty" )

							__snake_case     			=       nums[0]
							for i in range(1							,    len(SCREAMING_SNAKE_CASE ) ):
														__snake_case     			=       nums[i]
														__snake_case     			=       max(SCREAMING_SNAKE_CASE							,    ans + num							,    SCREAMING_SNAKE_CASE )

							return ans


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		# Try on a sample input from the user
		_SCREAMING_SNAKE_CASE	       =      int(input("""Enter number of elements : """).strip())
		_SCREAMING_SNAKE_CASE	       =      list(map(int, input("""\nEnter the numbers : """).strip().split()))[:n]
		print(max_subsequence_sum(array))
