






"""simple docstring"""



import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_UpperCamelCase		:  Optional[int]						=		5_0_0_0_0
_UpperCamelCase		:  Any						=		5_0_0_0

_UpperCamelCase,		_UpperCamelCase		:  List[str]						=		os.path.split(__file__)
_UpperCamelCase		:  List[str]						=		os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  datasets.Dataset		,					__snake_case			:  str    ):





  '''simple docstring'''





  for i in range(__snake_case    ):
    lowercase							    =       dataset[i]
@get_duration
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  datasets.Dataset		,					__snake_case			:  Union[str, Any]		,					__snake_case			:  List[Any]    ):





  '''simple docstring'''





  for i in range(0		,					len(__snake_case    )		,					__snake_case    ):
    lowercase							    =       dataset[i : i + batch_size]
@get_duration
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  datasets.Dataset		,					__snake_case			:  List[str]		,					__snake_case			:  Any    ):





  '''simple docstring'''





  with dataset.formatted_as(type=__snake_case    ):
    for i in range(__snake_case    ):
      lowercase							    =       dataset[i]
@get_duration
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  datasets.Dataset		,					__snake_case			:  Optional[int]		,					__snake_case			:  Union[str, Any]		,					__snake_case			:  Union[str, Any]    ):





  '''simple docstring'''





  with dataset.formatted_as(type=__snake_case    ):
    for i in range(0		,					__snake_case		,					__snake_case    ):
      lowercase							    =       dataset[i : i + batch_size]
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       {'num examples': SPEED_TEST_N_EXAMPLES}
  lowercase							    =       [
      (read, {'length': SMALL_TEST}),
      (read, {'length': SPEED_TEST_N_EXAMPLES}),
      (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
      (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
      (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
      (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
      (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
      (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
      (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
      (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
      (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
  ]

  lowercase							    =       [
      (read, {'length': SMALL_TEST}),
      (read, {'length': SPEED_TEST_N_EXAMPLES}),
      (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
      (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
      (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
      (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
      (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
      (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
  ]
  with tempfile.TemporaryDirectory() as tmp_dir:
    print('generating dataset'    )
    lowercase							    =       datasets.Features(
        {'list': datasets.Sequence(datasets.Value('float32'    )    ), 'numbers': datasets.Value('float32'    )}    )
    lowercase							    =       generate_example_dataset(
        os.path.join(__snake_case		,					'dataset.arrow'    )		,					__snake_case		,					num_examples=__snake_case		,					seq_shapes={'list': (1_00,)}		,					)
    print('first set of iterations'    )
    for func, kwargs in functions:
      print(func.__name__		,					str(__snake_case    )    )
      lowercase							    =       func(__snake_case		,					**__snake_case    )

    print('shuffling dataset'    )
    lowercase							    =       dataset.shuffle()
    print('Second set of iterations (after shuffling'    )
    for func, kwargs in functions_shuffled:
      print('shuffled '		,					func.__name__		,					str(__snake_case    )    )
      lowercase							    =       func(
          __snake_case		,					**__snake_case    )

  with open(__snake_case		,					'wb'    ) as f:
    f.write(json.dumps(__snake_case    ).encode('utf-8'    )    )


if __name__ == "__main__":  # useful to run the profiler
    benchmark_iterating()










"""simple docstring"""



import argparse
import json

from tqdm import tqdm
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       argparse.ArgumentParser()

  # Required parameters
  parser.add_argument(
      '--src_path'		,					type=__snake_case		,					default='biencoder-nq-dev.json'		,					help='Path to raw DPR training data'		,					)
  parser.add_argument(
      '--evaluation_set'		,					type=__snake_case		,					help='where to store parsed evaluation_set file'		,					)
  parser.add_argument(
      '--gold_data_path'		,					type=__snake_case		,					help='where to store parsed gold_data_path file'		,					)
  lowercase							    =       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:
    lowercase							    =       json.load(__snake_case    )
    for dpr_record in tqdm(__snake_case    ):
      lowercase							    =       dpr_record['question']
      lowercase							    =       [context['title'] for context in dpr_record['positive_ctxs']]
      eval_file.write(question + '\n'    )
      gold_file.write('\t'.join(__snake_case    ) + '\n'    )


if __name__ == "__main__":
    main()


