

'''simple docstring'''

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





def 				A_					(	snake_case	):

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

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

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

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

  return white_space_fix(remove_articles(remove_punc(lower(snake_case	)	)	)	)





def 				A_					(	snake_case ,    snake_case	):
  SCREAMING_SNAKE_CASE:int   =   normalize_answer(snake_case	).split()
  SCREAMING_SNAKE_CASE:Dict   =   normalize_answer(snake_case	).split()
  SCREAMING_SNAKE_CASE:Tuple   =   Counter(snake_case	) & Counter(snake_case	)
  SCREAMING_SNAKE_CASE:str   =   sum(common.values()	)
  if num_same == 0:
    return 0
  SCREAMING_SNAKE_CASE:str   =   1.0 * num_same / len(snake_case	)
  SCREAMING_SNAKE_CASE:Tuple   =   1.0 * num_same / len(snake_case	)
  SCREAMING_SNAKE_CASE:Dict   =   (2 * precision * recall) / (precision + recall)
  return fa





def 				A_					(	snake_case ,    snake_case	):
  return normalize_answer(snake_case	) == normalize_answer(snake_case	)





def 				A_					(	snake_case ,    snake_case ,    snake_case	):
  SCREAMING_SNAKE_CASE:Dict   =   []
  for ground_truth in ground_truths:
    SCREAMING_SNAKE_CASE:Optional[int]   =   metric_fn(snake_case ,    snake_case	)
    scores_for_ground_truths.append(snake_case	)
  return max(snake_case	)





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

      SCREAMING_SNAKE_CASE:Tuple   =   [x["text"] for x in qa["answers"]]
      SCREAMING_SNAKE_CASE:Optional[int]   =   predictions[qa["id"]]

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

      fa += metric_max_over_ground_truths(snake_case ,    snake_case ,    snake_case	)

  SCREAMING_SNAKE_CASE:List[Any]   =   exact_match / total
  SCREAMING_SNAKE_CASE:Tuple   =   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)









'''simple docstring'''

import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
A_      =							logging.get_logger(__name__)

A_      =							["UtteranceLevel"]





@torch.no_grad()
def 				A_					(	snake_case ,    snake_case ,    snake_case ,    snake_case	):
  SCREAMING_SNAKE_CASE:Optional[int]   =   torch.load(snake_case ,    map_location="cpu"	)
  if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
    raise NotImplementedError(F'''The supported s3prl models are {SUPPORTED_MODELS}'''	)

  SCREAMING_SNAKE_CASE:Tuple   =   checkpoint["Downstream"]

  SCREAMING_SNAKE_CASE:Union[str, Any]   =   HubertConfig.from_pretrained(snake_case	)
  SCREAMING_SNAKE_CASE:int   =   HubertForSequenceClassification.from_pretrained(snake_case ,    config=snake_case	)
  SCREAMING_SNAKE_CASE:Union[str, Any]   =   WavaVecaFeatureExtractor.from_pretrained(
      snake_case ,    return_attention_mask=snake_case ,    do_normalize=snake_case	)

  if hf_congfig.use_weighted_layer_sum:
    SCREAMING_SNAKE_CASE:Tuple   =   checkpoint["Featurizer"]["weights"]

  SCREAMING_SNAKE_CASE:List[Any]   =   downstream_dict["projector.weight"]
  SCREAMING_SNAKE_CASE:Optional[Any]   =   downstream_dict["projector.bias"]
  SCREAMING_SNAKE_CASE:List[Any]   =   downstream_dict["model.post_net.linear.weight"]
  SCREAMING_SNAKE_CASE:Tuple   =   downstream_dict["model.post_net.linear.bias"]

  hf_feature_extractor.save_pretrained(snake_case	)
  hf_model.save_pretrained(snake_case	)


if __name__ == "__main__":
    A_      =							argparse.ArgumentParser()
    parser.add_argument(
        "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
    )
    parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
    parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
    parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
    A_      =							parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






