



"""simple docstring"""

import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
							import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin
@require_torch
class     lowerCAmelCase__						(	unittest.TestCase							,							_lowerCamelCase ):


	'''simple docstring'''







	def 			_lowerCAmelCase     (      self				:       Dict    )						->	Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     load_tool('text-to-speech'    )
					self.tool.setup()





	def 			_lowerCAmelCase     (      self				:       Dict    )						->	int:





					"""simple docstring"""


					torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							int							=     self.tool('hey'    )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     result.to_raw()
					self.assertTrue(
					    torch.allclose(
					        resulting_tensor[:3]     ,       torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]    )     ,       )    )







	def 			_lowerCAmelCase     (      self				:       Optional[int]    )						->	List[str]:





					"""simple docstring"""


					torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     self.tool('hey'    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     result.to_raw()
					self.assertTrue(
					    torch.allclose(
					        resulting_tensor[:3]     ,       torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]    )     ,       )    )










"""simple docstring"""

import argparse
import json

from tqdm import tqdm
def  __snake_case							(	)     ->       Any:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							str							=     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'							,		)
				SCREAMING_SNAKE_CASE						:							Tuple							=     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:
								SCREAMING_SNAKE_CASE						:							Tuple							=     json.load(__A     )
								for dpr_record in tqdm(__A     ):
												SCREAMING_SNAKE_CASE						:							Tuple							=     dpr_record['question']
												SCREAMING_SNAKE_CASE						:							Optional[int]							=     [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()





