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





def    _a (						lowerCamelCase							):

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

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

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

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

 return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase							)							)							)							)





def    _a (						lowerCamelCase,     lowerCamelCase							):
 lowerCamelCase		:     Optional[Any]          =				normalize_answer(lowerCamelCase							).split()
 lowerCamelCase		:     Dict          =				normalize_answer(lowerCamelCase							).split()
 lowerCamelCase		:     str          =				Counter(lowerCamelCase							) & Counter(lowerCamelCase							)
 lowerCamelCase		:     List[Any]          =				sum(common.values()							)
 if num_same == 0:
  return 0
 lowerCamelCase		:     Optional[Any]          =				1.0 * num_same / len(lowerCamelCase							)
 lowerCamelCase		:     Dict          =				1.0 * num_same / len(lowerCamelCase							)
 lowerCamelCase		:     int          =				(2 * precision * recall) / (precision + recall)
 return fa





def    _a (						lowerCamelCase,     lowerCamelCase							):
 return normalize_answer(lowerCamelCase							) == normalize_answer(lowerCamelCase							)





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 lowerCamelCase		:     Any          =				[]
 for ground_truth in ground_truths:
  lowerCamelCase		:     Any          =				metric_fn(lowerCamelCase,     lowerCamelCase							)
  scores_for_ground_truths.append(lowerCamelCase							)
 return max(lowerCamelCase							)





def    _a (						lowerCamelCase,     lowerCamelCase							):
 lowerCamelCase		:     List[Any]          =				0
 for article in dataset:
  for paragraph in article["paragraphs"]:
   for qa in paragraph["qas"]:
    total += 1
    if qa["id"] not in predictions:
     lowerCamelCase		:     str          =				"""Unanswered question """ + qa["""id"""] + """ will receive score 0."""
     print(lowerCamelCase,     file=sys.stderr							)
     continue
    lowerCamelCase		:     Any          =				[x["""text"""] for x in qa["""answers"""]]
    lowerCamelCase		:     Dict          =				predictions[qa["""id"""]]
    exact_match += metric_max_over_ground_truths(lowerCamelCase,     lowerCamelCase,     lowerCamelCase							)
    fa += metric_max_over_ground_truths(lowerCamelCase,     lowerCamelCase,     lowerCamelCase							)

 lowerCamelCase		:     int          =				1_0_0.0 * exact_match / total
 lowerCamelCase		:     Dict          =				1_0_0.0 * fa / total

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


if __name__ == "__main__":
      _lowerCamelCase        ="""1.1"""
      _lowerCamelCase        =argparse.ArgumentParser(description="""Evaluation for SQuAD """ + expected_version)
      parser.add_argument("""dataset_file""", help="""Dataset file""")
      parser.add_argument("""prediction_file""", help="""Prediction File""")
      _lowerCamelCase        =parser.parse_args()
      with open(args.dataset_file) as dataset_file:
            _lowerCamelCase        =json.load(dataset_file)
            if dataset_json["version"] != expected_version:
                  print(
                      """Evaluation expects v-""" + expected_version + """, but got dataset with v-""" + dataset_json["""version"""],
                      file=sys.stderr,
                  )
            _lowerCamelCase        =dataset_json["""data"""]
      with open(args.prediction_file) as prediction_file:
            _lowerCamelCase        =json.load(prediction_file)
      print(json.dumps(evaluate(dataset, predictions)))






import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer





def    _a (						lowerCamelCase							):
 lowerCamelCase		:     Dict          =				args.serialization_dir
 lowerCamelCase		:     List[Any]          =				args.pruning_method
 lowerCamelCase		:     List[Any]          =				args.threshold

 lowerCamelCase		:     str          =				torch.load(os.path.join(lowerCamelCase,     """pytorch_model.bin"""							),     map_location="""cpu"""							)

 lowerCamelCase		:     Optional[int]          =				0  # Number of remaining (not pruned) params in the encoder
 lowerCamelCase		:     Dict          =				0  # Number of params in the encoder

 print("""name""".ljust(60,     """ """							),     """Remaining Weights %""",     """Remaining Weight"""							)
 for name, param in st.items():
  if "encoder" not in name:
   continue

  if "mask_scores" in name:
   if pruning_method == "topK":
    lowerCamelCase		:     List[str]          =				TopKBinarizer.apply(lowerCamelCase,     lowerCamelCase							).sum().item()
   elif pruning_method == "sigmoied_threshold":
    lowerCamelCase		:     Optional[int]          =				ThresholdBinarizer.apply(lowerCamelCase,     lowerCamelCase,     lowerCamelCase							).sum().item()
   elif pruning_method == "l0":
    lowerCamelCase      ,   lowerCamelCase		:     Tuple          =				-0.1, 1.1
    lowerCamelCase		:     Any          =				torch.sigmoid(lowerCamelCase							)
    lowerCamelCase		:     str          =				s * (r - l) + l
    lowerCamelCase		:     str          =				s_bar.clamp(min=0.0,     max=1.0							)
    lowerCamelCase		:     Union[str, Any]          =				(mask > 0.0).sum().item()
   else:
    raise ValueError("""Unknown pruning method"""							)
   remaining_count += mask_ones
   print(name.ljust(60,     """ """							),     str(round(100 * mask_ones / param.numel(),     3							)							).ljust(20,     """ """							),     str(lowerCamelCase							)							)
  else:
   encoder_count += param.numel()
   if "bias" in name or "LayerNorm" in name:
    remaining_count += param.numel()

 print(""""""							)
 print("""Remaining Weights (global) %: """,     100 * remaining_count / encoder_count							)


if __name__ == "__main__":
      _lowerCamelCase        =argparse.ArgumentParser()

      parser.add_argument(
          """--pruning_method""",
          choices=["""l0""", """topK""", """sigmoied_threshold"""],
          type=str,
          required=True,
          help=(
              """Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement"""
              """ pruning)"""
          ),
      )
      parser.add_argument(
          """--threshold""",
          type=float,
          required=False,
          help=(
              """For `topK`, it is the level of remaining weights (in %) in the fine-pruned model."""
              """For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."""
              """Not needed for `l0`"""
          ),
      )
      parser.add_argument(
          """--serialization_dir""",
          type=str,
          required=True,
          help="""Folder containing the model that was previously fine-pruned""",
      )

      _lowerCamelCase        =parser.parse_args()

      main(args)




