





import os

import pytest
from attr import dataclass


UpperCAmelCase						:   List[Any]			  =     '''us-east-1'''  # defaults region





@dataclass
class 						_A:


				"""simple docstring"""


				UpperCamelCase    :  str
				UpperCamelCase    :  Tuple					    =	'''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
				UpperCamelCase    :  str					    =	{
				    '''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,
				}
				UpperCamelCase    :  Optional[int]					    =	{**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  _SCREAMING_SNAKE_CASE				(   a  )      ->      Dict:
					__A	:						Optional[int]       =				SageMakerTestEnvironment(framework=request.cls.framework  )











import argparse
import json

from tqdm import tqdm







def  _SCREAMING_SNAKE_CASE				(   )      ->      List[Any]:
					__A	:						Tuple       =				argparse.ArgumentParser()

					# Required parameters
					parser.add_argument(
					    '--src_path'					,						type=a					,						default='biencoder-nq-dev.json'					,						help='Path to raw DPR training data'					,						)
					parser.add_argument(
					    '--evaluation_set'					,						type=a					,						help='where to store parsed evaluation_set file'					,						)
					parser.add_argument(
					    '--gold_data_path'					,						type=a					,						help='where to store parsed gold_data_path file'					,						)
					__A	:						Optional[int]       =				parser.parse_args()

					with open(args.src_path					,						'r'  ) as src_file, open(args.evaluation_set					,						'w'  ) as eval_file, open(
					    args.gold_data_path					,						'w'  ) as gold_file:
										__A	:						List[Any]       =				json.load(a  )
										for dpr_record in tqdm(a  ):
															__A	:						Dict       =				dpr_record['question']
															__A	:						Any       =				[context['title'] for context in dpr_record['positive_ctxs']]
															eval_file.write(question + '\n'  )
															gold_file.write('\t'.join(a  ) + '\n'  )


if __name__ == "__main__":
				main()




