def  snake_case						(__lowercase						)	->	list[list[float]]:

						'''simple docstring'''



						_snake_case						:					list[list[float]]    =   []
						for data in source_data:
												for i, el in enumerate(__lowercase						):
																		if len(__lowercase						) < i + 1:
																								data_lists.append([]						)
																		data_lists[i].append(float(__lowercase						)						)
						return data_lists
def  snake_case						(__lowercase ,      __lowercase						)	->	list[list[float]]:

						'''simple docstring'''



						_snake_case						:					list[list[float]]    =   []
						for dlist, weight in zip(__lowercase ,      __lowercase						):
												_snake_case						:					List[str]    =   min(__lowercase						)
												_snake_case						:					Union[str, Any]    =   max(__lowercase						)

												_snake_case						:					list[float]    =   []
												# for weight 0 score is 1 - actual score
												if weight == 0:
																		for item in dlist:
																								try:
																														score.append(1 - ((item - mind) / (maxd - mind))						)
																								except ZeroDivisionError:
																														score.append(1						)

												elif weight == 1:
																		for item in dlist:
																								try:
																														score.append((item - mind) / (maxd - mind)						)
																								except ZeroDivisionError:
																														score.append(0						)

        # weight not 0 or 1
												else:
																		_snake_case						:					List[str]    =   F"""Invalid weight of {weight:f} provided"""
																		raise ValueError(__lowercase						)

												score_lists.append(__lowercase						)

						return score_lists
def  snake_case						(__lowercase						)	->	list[float]:

						'''simple docstring'''



						_snake_case						:					list[float]    =   [0 for i in range(len(score_lists[0]						)						)]

						for slist in score_lists:
												for j, ele in enumerate(__lowercase						):
																		_snake_case						:					Any    =   final_scores[j] + ele

						return final_scores

def  snake_case						(__lowercase ,      __lowercase						)	->	list[list[float]]:

						'''simple docstring'''



						_snake_case						:					Optional[Any]    =   get_data(__lowercase						)
						_snake_case						:					Optional[Any]    =   calculate_each_score(__lowercase ,      __lowercase						)
						_snake_case						:					int    =   generate_final_scores(__lowercase						)

						# append scores to source data
						for i, ele in enumerate(__lowercase						):
												source_data[i].append(__lowercase						)

						return source_data
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						)