def  snake_case						(__lowercase						)	->	list:

						'''simple docstring'''



						_snake_case						:					str    =   len(__lowercase						)
						for i in range(1 ,      __lowercase						):
												_snake_case						:					List[str]    =   collection[i]
												_snake_case						:					Union[str, Any]    =   0
												_snake_case						:					Optional[int]    =   i - 1

												while low <= high:
																		_snake_case						:					int    =   (low + high) // 2
																		if val < collection[mid]:
																								_snake_case						:					Dict    =   mid - 1
																		else:
																								_snake_case						:					Union[str, Any]    =   mid + 1
												for j in range(__lowercase ,      __lowercase ,      -1						):
																		_snake_case						:					Optional[int]    =   collection[j - 1]
												_snake_case						:					Dict    =   val
						return collection


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:   Optional[int]         =			input('Enter numbers separated by a comma:\n').strip()
				__SCREAMING_SNAKE_CASE						:   List[Any]         =			[int(item) for item in user_input.split(',')]
				print(binary_insertion_sort(unsorted))
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						)