

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A__        =			logging.get_logger(__name__)

A__        =			{
    "facebook/convnext-tiny-224": "https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json",
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}







class    UpperCamelCase_			(  _snake_case		,     _snake_case			):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__				: List[str]										=      """convnext"""



  def __init__(	self    :				Optional[Any]     ,  A_    :				Any=3     ,  A_    :				Tuple=4     ,  A_    :				Optional[int]=4     ,  A_    :				Dict=None     ,  A_    :				List[Any]=None     ,  A_    :				Union[str, Any]="gelu"     ,  A_    :				Optional[Any]=0.02     ,  A_    :				Union[str, Any]=1E-1_2     ,  A_    :				Dict=1E-6     ,  A_    :				List[str]=0.0     ,  A_    :				int=224     ,  A_    :				Optional[Any]=None     ,  A_    :				List[str]=None     ,  **A_    :				Union[str, Any]     ,  )					-> Tuple:
      super().__init__(**A_	)

      SCREAMING_SNAKE_CASE__					     =						num_channels
      SCREAMING_SNAKE_CASE__					     =						patch_size
      SCREAMING_SNAKE_CASE__					     =						num_stages
      SCREAMING_SNAKE_CASE__					     =						[96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
      SCREAMING_SNAKE_CASE__					     =						[3, 3, 9, 3] if depths is None else depths
      SCREAMING_SNAKE_CASE__					     =						hidden_act
      SCREAMING_SNAKE_CASE__					     =						initializer_range
      SCREAMING_SNAKE_CASE__					     =						layer_norm_eps
      SCREAMING_SNAKE_CASE__					     =						layer_scale_init_value
      SCREAMING_SNAKE_CASE__					     =						drop_path_rate
      SCREAMING_SNAKE_CASE__					     =						image_size
      SCREAMING_SNAKE_CASE__					     =						["stem"] + [f"stage{idx}" for idx in range(1     ,  len(self.depths	) + 1	)]
      SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						get_aligned_output_features_output_indices(
          out_features=A_     ,  out_indices=A_     ,  stage_names=self.stage_names	)







class    UpperCamelCase_			(  _snake_case			):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__				: Dict										=      version.parse("""1.11"""			)



  @property
  def      _UpperCamelCase    (	self    :				str	)					-> Mapping[str, Mapping[int, str]]:
      return OrderedDict(
          [
              ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
          ]	)







  @property
  def      _UpperCamelCase    (	self    :				Tuple	)					-> float:
      return 1E-5





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





def 							__a       (					__A	):

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

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

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

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

    return white_space_fix(remove_articles(remove_punc(lower(__A	)	)	)	)





def 							__a       (					__A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						normalize_answer(__A	).split()
    SCREAMING_SNAKE_CASE__					     =						normalize_answer(__A	).split()
    SCREAMING_SNAKE_CASE__					     =						Counter(__A	) & Counter(__A	)
    SCREAMING_SNAKE_CASE__					     =						sum(common.values()	)
    if num_same == 0:
        return 0
    SCREAMING_SNAKE_CASE__					     =						1.0 * num_same / len(__A	)
    SCREAMING_SNAKE_CASE__					     =						1.0 * num_same / len(__A	)
    SCREAMING_SNAKE_CASE__					     =						(2 * precision * recall) / (precision + recall)
    return fa





def 							__a       (					__A ,       __A	):
    return normalize_answer(__A	) == normalize_answer(__A	)





def 							__a       (					__A ,       __A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						[]
    for ground_truth in ground_truths:
        SCREAMING_SNAKE_CASE__					     =						metric_fn(__A ,       __A	)
        scores_for_ground_truths.append(__A	)
    return max(__A	)





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

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

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

            fa += metric_max_over_ground_truths(__A ,       __A ,       __A	)

    SCREAMING_SNAKE_CASE__					     =						exact_match / total
    SCREAMING_SNAKE_CASE__					     =						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)


