


'''simple docstring'''

import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode





def __snake_case (				UpperCAmelCase_						:						Any ,    **UpperCAmelCase_						:						Tuple		):
 return RunBeamCommand(
     args.dataset ,    args.name ,    args.cache_dir ,    args.beam_pipeline_options ,    args.data_dir ,    args.all_configs ,    args.save_info or args.save_infos ,    args.ignore_verifications ,    args.force_redownload ,    **UpperCAmelCase_ ,    )







class 					snake_case   (   lowercase      ):





  """simple docstring"""




  @staticmethod
  def   snake_case		(   UpperCamelCase					):







   """simple docstring"""
   lowerCamelCase_								=  parser.add_parser("run_beam"      ,  help="Run a Beam dataset processing pipeline"					)
   run_beam_parser.add_argument("dataset"      ,  type=UpperCamelCase      ,  help="Name of the dataset to download"					)
   run_beam_parser.add_argument("--name"      ,  type=UpperCamelCase      ,  default=UpperCamelCase      ,  help="Dataset config name"					)
   run_beam_parser.add_argument(
       "--cache_dir"      ,  type=UpperCamelCase      ,  default=UpperCamelCase      ,  help="Cache directory where the datasets are stored"      ,  )
   run_beam_parser.add_argument(
       "--beam_pipeline_options"      ,  type=UpperCamelCase      ,  default=""      ,  help="Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`"      ,  )
   run_beam_parser.add_argument(
       "--data_dir"      ,  type=UpperCamelCase      ,  default=UpperCamelCase      ,  help="Can be used to specify a manual directory to get the files from"      ,  )
   run_beam_parser.add_argument("--all_configs"      ,  action="store_true"      ,  help="Test all dataset configurations"					)
   run_beam_parser.add_argument("--save_info"      ,  action="store_true"      ,  help="Save the dataset infos file"					)
   run_beam_parser.add_argument(
       "--ignore_verifications"      ,  action="store_true"      ,  help="Run the test without checksums and splits checks"					)
   run_beam_parser.add_argument("--force_redownload"      ,  action="store_true"      ,  help="Force dataset redownload"					)
   # aliases
   run_beam_parser.add_argument("--save_infos"      ,  action="store_true"      ,  help="alias for save_info"					)
   run_beam_parser.set_defaults(func=UpperCamelCase					)






  def __init__(   self      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   lowerCamelCase_								=  dataset
   lowerCamelCase_								=  name
   lowerCamelCase_								=  cache_dir
   lowerCamelCase_								=  beam_pipeline_options
   lowerCamelCase_								=  data_dir
   lowerCamelCase_								=  all_configs
   lowerCamelCase_								=  save_infos
   lowerCamelCase_								=  ignore_verifications
   lowerCamelCase_								=  force_redownload
   lowerCamelCase_								=  config_kwargs







  def   snake_case		(   self					):







   """simple docstring"""
   import apache_beam as beam

   if self._name is not None and self._all_configs:
    print("Both parameters `name` and `all_configs` can't be used at once."					)
    exit(1					)
   lowerCamelCase_			,lowerCamelCase_								=  self._dataset, self._name
   lowerCamelCase_								=  dataset_module_factory(UpperCamelCase					)
   lowerCamelCase_								=  import_main_class(dataset_module.module_path					)
   lowerCamelCase_								=  []
   if self._beam_pipeline_options:
    lowerCamelCase_								=  beam.options.pipeline_options.PipelineOptions(
        flags=[f'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(","					) if opt]					)
   else:
    lowerCamelCase_								=  None
   if self._all_configs and len(builder_cls.BUILDER_CONFIGS					) > 0:
    for builder_config in builder_cls.BUILDER_CONFIGS:
     builders.append(
         builder_cls(
             config_name=builder_config.name      ,  data_dir=self._data_dir      ,  hash=dataset_module.hash      ,  beam_options=UpperCamelCase      ,  cache_dir=self._cache_dir      ,  base_path=dataset_module.builder_kwargs.get("base_path"					)      ,  )					)
   else:
    builders.append(
        builder_cls(
            config_name=UpperCamelCase      ,  data_dir=self._data_dir      ,  beam_options=UpperCamelCase      ,  cache_dir=self._cache_dir      ,  base_path=dataset_module.builder_kwargs.get("base_path"					)      ,  **self._config_kwargs      ,  )					)

   for builder in builders:
    builder.download_and_prepare(
        download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
        if not self._force_redownload
        else DownloadMode.FORCE_REDOWNLOAD      ,  download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH					)      ,  verification_mode=VerificationMode.NO_CHECKS
        if self._ignore_verifications
        else VerificationMode.ALL_CHECKS      ,  try_from_hf_gcs=UpperCamelCase      ,  )
    if self._save_infos:
     builder._save_infos()

   print("Apache beam run successful."					)

   # If save_infos=True, the dataset infos file is created next to the loaded module file.
   # Let's move it to the original directory of the dataset script, to allow the user to
   # upload them on S3 at the same time afterwards.
   if self._save_infos:
    lowerCamelCase_								=  os.path.join(builder_cls.get_imported_module_dir()      ,  config.DATASETDICT_INFOS_FILENAME					)

    lowerCamelCase_								=  Path(UpperCamelCase					).name + ".py"

    lowerCamelCase_								=  os.path.join(UpperCamelCase      ,  UpperCamelCase					)
    if os.path.isfile(UpperCamelCase					):
     lowerCamelCase_								=  os.path.dirname(UpperCamelCase					)
    elif os.path.isfile(UpperCamelCase					):
     lowerCamelCase_								=  path
    else:  # in case of a remote dataset
     print(f'''Dataset Infos file saved at {dataset_infos_path}'''					)
     exit(1					)

    # Move datasetinfo back to the user
    lowerCamelCase_								=  os.path.join(UpperCamelCase      ,  config.DATASETDICT_INFOS_FILENAME					)
    copyfile(UpperCamelCase      ,  UpperCamelCase					)
    print(f'''Dataset Infos file saved at {user_dataset_infos_path}'''					)







'''simple docstring'''

import argparse
import json
import re
import string
import sys
from collections import Counter





def __snake_case (				UpperCAmelCase_						:						str		):

 def remove_articles(UpperCAmelCase_						:						Optional[Any]		):
  return re.sub(r"\b(a|an|the)\b" ,    " " ,    UpperCAmelCase_		)

 def white_space_fix(UpperCAmelCase_						:						List[str]		):
  return " ".join(text.split()		)

 def remove_punc(UpperCAmelCase_						:						Any		):
  lowerCamelCase_								=  set(string.punctuation		)
  return "".join(ch for ch in text if ch not in exclude		)

 def lower(UpperCAmelCase_						:						List[str]		):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase_		)		)		)		)





def __snake_case (				UpperCAmelCase_						:						Tuple ,    UpperCAmelCase_						:						int		):
 lowerCamelCase_								=  normalize_answer(UpperCAmelCase_		).split()
 lowerCamelCase_								=  normalize_answer(UpperCAmelCase_		).split()
 lowerCamelCase_								=  Counter(UpperCAmelCase_		) & Counter(UpperCAmelCase_		)
 lowerCamelCase_								=  sum(common.values()		)
 if num_same == 0:
  return 0
 lowerCamelCase_								=  1.0 * num_same / len(UpperCAmelCase_		)
 lowerCamelCase_								=  1.0 * num_same / len(UpperCAmelCase_		)
 lowerCamelCase_								=  (2 * precision * recall) / (precision + recall)
 return fa





def __snake_case (				UpperCAmelCase_						:						Tuple ,    UpperCAmelCase_						:						List[Any]		):
 return normalize_answer(UpperCAmelCase_		) == normalize_answer(UpperCAmelCase_		)





def __snake_case (				UpperCAmelCase_						:						Any ,    UpperCAmelCase_						:						Dict ,    UpperCAmelCase_						:						Union[str, Any]		):
 lowerCamelCase_								=  []
 for ground_truth in ground_truths:
  lowerCamelCase_								=  metric_fn(UpperCAmelCase_ ,    UpperCAmelCase_		)
  scores_for_ground_truths.append(UpperCAmelCase_		)
 return max(UpperCAmelCase_		)





def __snake_case (				UpperCAmelCase_						:						Optional[int] ,    UpperCAmelCase_						:						Dict		):
 lowerCamelCase_								=  lowerCamelCase_								=  lowerCamelCase_								=  0
 lowerCamelCase_								=  []
 for passage in dataset:
  for qa in passage["qas"]:
   total += 1
   if qa["id"] not in predictions:
    lowerCamelCase_								=  F'''Unanswered question {qa["id"]} will receive score 0.'''
    print(UpperCAmelCase_ ,    file=sys.stderr		)
    continue

   lowerCamelCase_								=  [x["text"] for x in qa["answers"]]
   lowerCamelCase_								=  predictions[qa["id"]]

   lowerCamelCase_								=  metric_max_over_ground_truths(UpperCAmelCase_ ,    UpperCAmelCase_ ,    UpperCAmelCase_		)
   if int(_exact_match		) == 1:
    correct_ids.append(qa["id"]		)
   exact_match += _exact_match

   fa += metric_max_over_ground_truths(UpperCAmelCase_ ,    UpperCAmelCase_ ,    UpperCAmelCase_		)

 lowerCamelCase_								=  exact_match / total
 lowerCamelCase_								=  fa / total

 return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
     a_  :    Optional[int]      =			"""1.0"""
     a_  :    Dict      =			argparse.ArgumentParser("""Official evaluation script for ReCoRD v1.0.""")
     parser.add_argument("""data_file""", help="""The dataset file in JSON format.""")
     parser.add_argument("""pred_file""", help="""The model prediction file in JSON format.""")
     parser.add_argument("""--output_correct_ids""", action="""store_true""", help="""Output the correctly answered query IDs.""")
     a_  :    Optional[int]      =			parser.parse_args()

     with open(args.data_file) as data_file:
          a_  :    Any      =			json.load(data_file)
          if dataset_json["version"] != expected_version:
               print(
                   f'''Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json['version']}''',
                   file=sys.stderr,
               )
          a_  :    Any      =			dataset_json["""data"""]

     with open(args.pred_file) as pred_file:
          a_  :    List[str]      =			json.load(pred_file)

     a_     ,       a_  :    int      =			evaluate(dataset, predictions)

     if args.output_correct_ids:
          print(f'''Output {len(correct_ids)} correctly answered question IDs.''')
          with open("""correct_ids.json""", """w""") as f:
               json.dump(correct_ids, f)



