






'''simple docstring'''

import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available
def     __lowercase  (*_lowercase     )   ->     int:


     """simple docstring"""
     if not isinstance(_lowercase,						_lowercase     ):
          __lowerCamelCase						:       Optional[Any]				   =				list(_lowercase     )
     for i in range(len(_lowercase     )     ):
          __lowerCamelCase						:       Optional[int]				   =				None
     gc.collect()
     if is_xpu_available():
          torch.xpu.empty_cache()
     elif is_npu_available():
          torch.npu.empty_cache()
     else:
          torch.cuda.empty_cache()
     return objects
def     __lowercase  (_lowercase     )   ->     bool:


     """simple docstring"""
     __lowerCamelCase						:       Tuple				   =				[
         """CUDA out of memory.""",  # CUDA OOM
         """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
         """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
     ]
     if isinstance(_lowercase,						_lowercase     ) and len(exception.args     ) == 1:
          return any(err in exception.args[0] for err in _statements     )
     return False
def     __lowercase  (_lowercase = None,						_lowercase = 128     )   ->     Dict:


     """simple docstring"""
     if function is None:
          return functools.partial(_lowercase,						starting_batch_size=_lowercase     )

     __lowerCamelCase						:       Optional[Any]				   =				starting_batch_size

     def decorator(*_lowercase,						**_lowercase     ):
          nonlocal batch_size
          gc.collect()
          if is_xpu_available():
               torch.xpu.empty_cache()
          elif is_npu_available():
               torch.npu.empty_cache()
          else:
               torch.cuda.empty_cache()
          __lowerCamelCase						:       Union[str, Any]				   =				list(inspect.signature(_lowercase     ).parameters.keys()     )
          # Guard against user error
          if len(_lowercase     ) < (len(_lowercase     ) + 1):
               __lowerCamelCase						:       Dict				   =				""", """.join([f"{arg}={value}" for arg, value in zip(params[1:],						args[1:]     )]     )
               raise TypeError(
                   f"Batch size was passed into `{function.__name__}` as the first argument when called."
                   f"Remove this as the decorator already does so: `{function.__name__}({arg_str})`"     )
          while True:
               if batch_size == 0:
                    raise RuntimeError("""No executable batch size found, reached zero."""     )
               try:
                    return function(_lowercase,						*_lowercase,						**_lowercase     )
               except Exception as e:
                    if should_reduce_batch_size(_lowercase     ):
                         gc.collect()
                         if is_xpu_available():
                              torch.xpu.empty_cache()
                         elif is_npu_available():
                              torch.npu.empty_cache()
                         else:
                              torch.cuda.empty_cache()
                         batch_size //= 2
                    else:
                         raise

     return decorator











'''simple docstring'''

import argparse
import json

from tqdm import tqdm
def     __lowercase  ()   ->     Tuple:


     """simple docstring"""
     __lowerCamelCase						:       Union[str, Any]				   =				argparse.ArgumentParser()

     # Required parameters
     parser.add_argument(
         """--src_path""",						type=_lowercase,						default="""biencoder-nq-dev.json""",						help="""Path to raw DPR training data""",						)
     parser.add_argument(
         """--evaluation_set""",						type=_lowercase,						help="""where to store parsed evaluation_set file""",						)
     parser.add_argument(
         """--gold_data_path""",						type=_lowercase,						help="""where to store parsed gold_data_path file""",						)
     __lowerCamelCase						:       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:
          __lowerCamelCase						:       int				   =				json.load(_lowercase     )
          for dpr_record in tqdm(_lowercase     ):
               __lowerCamelCase						:       List[Any]				   =				dpr_record["""question"""]
               __lowerCamelCase						:       List[str]				   =				[context["""title"""] for context in dpr_record["""positive_ctxs"""]]
               eval_file.write(question + """\n"""     )
               gold_file.write("""\t""".join(_lowercase     ) + """\n"""     )


if __name__ == "__main__":
   main()



