




import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


a_										=       get_logger(__name__)


if TYPE_CHECKING:
 from .builder import DatasetBuilder




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ = None )    ->		Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase											=  importlib.import_module(lowercase_ )

    # TODO(QL): always update the module to add subsequent new authentication
    if hasattr(lowercase_   ,       '''_patched_for_streaming''' ) and module._patched_for_streaming:
        return

    def wrap_auth(lowercase_ ):
        @wraps(lowercase_ )
        def wrapper(*lowercase_   ,       **lowercase_ ):
            return function(*lowercase_   ,       use_auth_token=lowercase_   ,       **lowercase_ )

        __UpperCamelCase											=  '''wrap_auth'''
        return wrapper

    # open files in a streaming fashion
    patch_submodule(lowercase_   ,       '''open'''   ,       wrap_auth(lowercase_ ) ).start()
    patch_submodule(lowercase_   ,       '''os.listdir'''   ,       wrap_auth(lowercase_ ) ).start()
    patch_submodule(lowercase_   ,       '''os.walk'''   ,       wrap_auth(lowercase_ ) ).start()
    patch_submodule(lowercase_   ,       '''glob.glob'''   ,       wrap_auth(lowercase_ ) ).start()
    # allow to navigate in remote zip files
    patch_submodule(lowercase_   ,       '''os.path.join'''   ,       lowercase_ ).start()
    patch_submodule(lowercase_   ,       '''os.path.dirname'''   ,       lowercase_ ).start()
    patch_submodule(lowercase_   ,       '''os.path.basename'''   ,       lowercase_ ).start()
    patch_submodule(lowercase_   ,       '''os.path.relpath'''   ,       lowercase_ ).start()
    patch_submodule(lowercase_   ,       '''os.path.split'''   ,       lowercase_ ).start()
    patch_submodule(lowercase_   ,       '''os.path.splitext'''   ,       lowercase_ ).start()
    # allow checks on paths
    patch_submodule(lowercase_   ,       '''os.path.exists'''   ,       wrap_auth(lowercase_ ) ).start()
    patch_submodule(lowercase_   ,       '''os.path.isdir'''   ,       wrap_auth(lowercase_ ) ).start()
    patch_submodule(lowercase_   ,       '''os.path.isfile'''   ,       wrap_auth(lowercase_ ) ).start()
    patch_submodule(lowercase_   ,       '''os.path.getsize'''   ,       wrap_auth(lowercase_ ) ).start()
    patch_submodule(lowercase_   ,       '''pathlib.Path'''   ,       lowercase_ ).start()
    # file readers
    patch_submodule(lowercase_   ,       '''gzip.open'''   ,       wrap_auth(lowercase_ ) ).start()
    patch_submodule(lowercase_   ,       '''numpy.load'''   ,       wrap_auth(lowercase_ ) ).start()
    patch_submodule(lowercase_   ,       '''pandas.read_csv'''   ,       wrap_auth(lowercase_ )   ,       attrs=['''__version__'''] ).start()
    patch_submodule(lowercase_   ,       '''pandas.read_excel'''   ,       wrap_auth(lowercase_ )   ,       attrs=['''__version__'''] ).start()
    patch_submodule(lowercase_   ,       '''scipy.io.loadmat'''   ,       wrap_auth(lowercase_ )   ,       attrs=['''__version__'''] ).start()
    patch_submodule(lowercase_   ,       '''xml.etree.ElementTree.parse'''   ,       wrap_auth(lowercase_ ) ).start()
    patch_submodule(lowercase_   ,       '''xml.dom.minidom.parse'''   ,       wrap_auth(lowercase_ ) ).start()
    __UpperCamelCase											=  True




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		List[Any]:

    """simple docstring"""


    extend_module_for_streaming(builder.__module__   ,       use_auth_token=builder.use_auth_token )
    # if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
    if not builder.__module__.startswith('''datasets.''' ):  # check that it's not a packaged builder like csv
        for imports in get_imports(inspect.getfile(builder.__class__ ) ):
            if imports[0] == "internal":
                __UpperCamelCase											=  imports[1]
                __UpperCamelCase											=  '''.'''.join(builder.__module__.split('''.''' )[:-1] + [internal_import_name] )
                extend_module_for_streaming(lowercase_   ,       use_auth_token=builder.use_auth_token )

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
    from .builder import DatasetBuilder

    __UpperCamelCase											=  [
        cls.__module__
        for cls in type(lowercase_ ).__mro__[1:]  # make sure it's not the same module we've already patched
        if issubclass(cls   ,       lowercase_ ) and cls.__module__ != DatasetBuilder.__module__
    ]  # check it's not a standard builder from datasets.builder
    for module in parent_builder_modules:
        extend_module_for_streaming(lowercase_   ,       use_auth_token=builder.use_auth_token )












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




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		Dict:

    """simple docstring"""


    def remove_articles(lowercase_ ):
        return re.sub(r'''\b(a|an|the)\b'''   ,       ''' '''   ,       lowercase_ )

    def white_space_fix(lowercase_ ):
        return " ".join(text.split() )

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

    def lower(lowercase_ ):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(lowercase_ ) ) ) )




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		List[str]:

    """simple docstring"""


    __UpperCamelCase											=  normalize_answer(lowercase_ ).split()
    __UpperCamelCase											=  normalize_answer(lowercase_ ).split()
    __UpperCamelCase											=  Counter(lowercase_ ) & Counter(lowercase_ )
    __UpperCamelCase											=  sum(common.values() )
    if num_same == 0:
        return 0
    __UpperCamelCase											=  1.0 * num_same / len(lowercase_ )
    __UpperCamelCase											=  1.0 * num_same / len(lowercase_ )
    __UpperCamelCase											=  (2 * precision * recall) / (precision + recall)
    return fa




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		Any:

    """simple docstring"""


    return normalize_answer(lowercase_ ) == normalize_answer(lowercase_ )




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		int:

    """simple docstring"""


    __UpperCamelCase											=  []
    for ground_truth in ground_truths:
        __UpperCamelCase											=  metric_fn(lowercase_   ,       lowercase_ )
        scores_for_ground_truths.append(lowercase_ )
    return max(lowercase_ )




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		Optional[int]:

    """simple docstring"""


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

            __UpperCamelCase											=  [x['''text'''] for x in qa['''answers''']]
            __UpperCamelCase											=  predictions[qa['''id''']]

            __UpperCamelCase											=  metric_max_over_ground_truths(lowercase_   ,       lowercase_   ,       lowercase_ )
            if int(_exact_match ) == 1:
                correct_ids.append(qa['''id'''] )
            exact_match += _exact_match

            fa += metric_max_over_ground_truths(lowercase_   ,       lowercase_   ,       lowercase_ )

    __UpperCamelCase											=  exact_match / total
    __UpperCamelCase											=  fa / total

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


if __name__ == "__main__":
 a_										=       "1.0"
 a_										=       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_										=       parser.parse_args()

 with open(args.data_file) as data_file:
  a_										=       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_										=       dataset_json["data"]

 with open(args.pred_file) as pred_file:
  a_										=       json.load(pred_file)

 a_						,      a_										=       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)






