code: 
  
  
  
  
  
  '''simple docstring'''
  
  import argparse
  import json
  import re
  import string
  import sys
  
  import numpy as np
  
  
  lowercase_            =				0.5
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Any      , __lowerCamelCase				:					Optional[int]					)		->List[str]:
      _SCREAMING_SNAKE_CASE             =    ['''.''', ''',''', ''';''', ''':''']
  
      for token in remove_tokens:
          _SCREAMING_SNAKE_CASE             =    ground_truth.replace(__a      , """"""					)
          _SCREAMING_SNAKE_CASE             =    prediction.replace(__a      , """"""					)
  
      _SCREAMING_SNAKE_CASE             =    ground_truth.lower(), prediction.lower()
      _SCREAMING_SNAKE_CASE             =    ground_truth.replace("""/"""      , """ """					), prediction.replace("""/"""      , """ """					)
      _SCREAMING_SNAKE_CASE             =    set(ground_truth.split(""" """					)					), set(prediction.split(""" """					)					)
  
      _SCREAMING_SNAKE_CASE             =    ground_truth.intersection(__a					)
      _SCREAMING_SNAKE_CASE             =    ground_truth.union(__a					)
      _SCREAMING_SNAKE_CASE             =    len(__a					) / len(__a					)
      return jaccard
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Optional[int]					)		->List[str]:
  
  
  
  
  
      def remove_articles(__lowerCamelCase				:					Union[str, Any]					):
          return re.sub(R"""\b(a|an|the)\b"""      , """ """      , __a					)
  
      def white_space_fix(__lowerCamelCase				:					List[Any]					):
          return " ".join(text.split()					)
  
      def remove_punc(__lowerCamelCase				:					Optional[Any]					):
          _SCREAMING_SNAKE_CASE             =    set(string.punctuation					)
          return "".join(ch for ch in text if ch not in exclude					)
  
      def lower(__lowerCamelCase				:					Optional[Any]					):
          return text.lower()
  
      return white_space_fix(remove_articles(remove_punc(lower(__a					)					)					)					)
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Optional[Any]      , __lowerCamelCase				:					Dict      , __lowerCamelCase				:					Dict					)		->List[str]:
      _SCREAMING_SNAKE_CASE             =    0, 0, 0
  
      _SCREAMING_SNAKE_CASE             =    '''Parties''' in qa_id
  
      # first check if ground truth is empty
      if len(__a					) == 0:
          if len(__a					) > 0:
              fp += len(__a					)  # false positive for each one
      else:
          for ground_truth in ground_truths:
              assert len(__a					) > 0
              # check if there is a match
              _SCREAMING_SNAKE_CASE             =    False
              for pred in predictions:
                  if substr_ok:
                      _SCREAMING_SNAKE_CASE             =    get_jaccard(__a      , __a					) >= IOU_THRESH or ground_truth in pred
                  else:
                      _SCREAMING_SNAKE_CASE             =    get_jaccard(__a      , __a					) >= IOU_THRESH
                  if is_match:
                      _SCREAMING_SNAKE_CASE             =    True
  
              if match_found:
                  tp += 1
              else:
                  fn += 1
  
          # now also get any fps by looping through preds
          for pred in predictions:
              # Check if there's a match. if so, don't count (don't want to double count based on the above)
              # but if there's no match, then this is a false positive.
              # (Note: we get the true positives in the above loop instead of this loop so that we don't double count
              # multiple predictions that are matched with the same answer.)
              _SCREAMING_SNAKE_CASE             =    False
              for ground_truth in ground_truths:
                  assert len(__a					) > 0
                  if substr_ok:
                      _SCREAMING_SNAKE_CASE             =    get_jaccard(__a      , __a					) >= IOU_THRESH or ground_truth in pred
                  else:
                      _SCREAMING_SNAKE_CASE             =    get_jaccard(__a      , __a					) >= IOU_THRESH
                  if is_match:
                      _SCREAMING_SNAKE_CASE             =    True
  
              if not match_found:
                  fp += 1
  
      _SCREAMING_SNAKE_CASE             =    tp / (tp + fp) if tp + fp > 0 else np.nan
      _SCREAMING_SNAKE_CASE             =    tp / (tp + fn) if tp + fn > 0 else np.nan
  
      return precision, recall
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Optional[int]					)		->List[str]:
      _SCREAMING_SNAKE_CASE             =    precisions[::-1]
      for i in range(1      , len(__a					)					):
          _SCREAMING_SNAKE_CASE             =    max(precision_best[i - 1]      , precision_best[i]					)
      _SCREAMING_SNAKE_CASE             =    precision_best[::-1]
      return precisions
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Union[str, Any]      , __lowerCamelCase				:					Tuple					)		->Optional[Any]:
      _SCREAMING_SNAKE_CASE             =    process_precisions(__a					)
      _SCREAMING_SNAKE_CASE             =    np.trapz(__a      , __a					)
      if np.isnan(__a					):
          return 0
      return aupr
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Optional[int]      , __lowerCamelCase				:					Optional[Any]      , __lowerCamelCase				:					Tuple					)		->Dict:
      _SCREAMING_SNAKE_CASE             =    process_precisions(__a					)
      _SCREAMING_SNAKE_CASE             =    0
      for prec, recall in zip(__a      , __a					):
          if recall >= recall_thresh:
              _SCREAMING_SNAKE_CASE             =    prec
              break
      return prec_at_recall
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Tuple      , __lowerCamelCase				:					Optional[Any]					)		->Union[str, Any]:
      return normalize_answer(__a					) == normalize_answer(__a					)
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Optional[Any]      , __lowerCamelCase				:					Tuple      , __lowerCamelCase				:					List[str]					)		->Any:
      _SCREAMING_SNAKE_CASE             =    0
      for pred in predictions:
          for ground_truth in ground_truths:
              _SCREAMING_SNAKE_CASE             =    metric_fn(__a      , __a					)
              if score == 1:  # break the loop when one prediction matches the ground truth
                  break
          if score == 1:
              break
      return score
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Tuple      , __lowerCamelCase				:					List[Any]					)		->str:
      _SCREAMING_SNAKE_CASE             =    0
      _SCREAMING_SNAKE_CASE             =    []
      _SCREAMING_SNAKE_CASE             =    []
      for article in dataset:
          for paragraph in article["paragraphs"]:
              for qa in paragraph["qas"]:
                  total += 1
                  if qa["id"] not in predictions:
                      _SCREAMING_SNAKE_CASE             =    '''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             =    compute_precision_recall(__a      , __a      , qa["""id"""]					)
  
                  precisions.append(__a					)
                  recalls.append(__a					)
  
                  if precision == 0 and recall == 0:
                      fa += 0
                  else:
                      fa += 2 * (precision * recall) / (precision + recall)
  
                  exact_match += metric_max_over_ground_truths(__a      , __a      , __a					)
  
      _SCREAMING_SNAKE_CASE             =    [x for _, x in sorted(zip(__a      , __a					)					)]
      recalls.sort()
  
      _SCREAMING_SNAKE_CASE             =    100.0 * fa / total
      _SCREAMING_SNAKE_CASE             =    100.0 * exact_match / total
      _SCREAMING_SNAKE_CASE             =    get_aupr(__a      , __a					)
  
      _SCREAMING_SNAKE_CASE             =    get_prec_at_recall(__a      , __a      , recall_thresh=0.9					)
      _SCREAMING_SNAKE_CASE             =    get_prec_at_recall(__a      , __a      , recall_thresh=0.8					)
  
      return {
          "exact_match": exact_match,
          "f1": fa,
          "aupr": aupr,
          "prec_at_80_recall": prec_at_aa_recall,
          "prec_at_90_recall": prec_at_aa_recall,
      }
  
  
  if __name__ == "__main__":
        lowercase_            =				argparse.ArgumentParser(description="""Evaluation for CUAD""")
        parser.add_argument("""dataset_file""", help="""Dataset file""")
        parser.add_argument("""prediction_file""", help="""Prediction File""")
        lowercase_            =				parser.parse_args()
        with open(args.dataset_file) as dataset_file:
              lowercase_            =				json.load(dataset_file)
              lowercase_            =				dataset_json["""data"""]
        with open(args.prediction_file) as prediction_file:
              lowercase_            =				json.load(prediction_file)
        print(json.dumps(evaluate(dataset, predictions)))
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  # Copyright 2022 The HuggingFace Team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  import os
  from typing import List
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					List[str]      , __lowerCamelCase				:					str					)		->Tuple:
      if name != "training_function" and name != "main":
          raise ValueError(F'Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''					)
      _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    [], False
      for line in lines:
          if not found_start and F'def {name}' in line:
              _SCREAMING_SNAKE_CASE             =    True
              good_lines.append(__lowerCamelCase					)
              continue
          if found_start:
              if name == "training_function" and "def main" in line:
                  return good_lines
              if name == "main" and "if __name__" in line:
                  return good_lines
              good_lines.append(__lowerCamelCase					)
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					List[str]					)		->List[str]:
      return [line for line in lines if not line.lstrip().startswith("""#"""					) and line != "\n"]
  
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					str      , __lowerCamelCase				:					str      , __lowerCamelCase				:					bool      , __lowerCamelCase				:					str = None					)		->List[str]:
      with open(__lowerCamelCase      , """r"""					) as f:
          _SCREAMING_SNAKE_CASE             =    f.readlines()
      with open(os.path.abspath(os.path.join("""examples"""      , """nlp_example.py"""					)					)      , """r"""					) as f:
          _SCREAMING_SNAKE_CASE             =    f.readlines()
      with open(__lowerCamelCase      , """r"""					) as f:
          _SCREAMING_SNAKE_CASE             =    f.readlines()
      if secondary_filename is not None:
          with open(__lowerCamelCase      , """r"""					) as f:
              _SCREAMING_SNAKE_CASE             =    f.readlines()
  
      # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
      if parser_only:
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
          if secondary_filename is not None:
              _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
      else:
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
          if secondary_filename is not None:
              _SCREAMING_SNAKE_CASE             =    clean_lines(
                  get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
  
      _SCREAMING_SNAKE_CASE             =    """train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"""
  
      # Specific code in our script that differs from the full version, aka what is new
      _SCREAMING_SNAKE_CASE             =    []
      _SCREAMING_SNAKE_CASE             =    []  # We keep track of the idxs just in case it's a repeated statement
      _SCREAMING_SNAKE_CASE             =    iter(__lowerCamelCase					)
      for i in range(len(__lowerCamelCase					) - 1					):
          if i not in passed_idxs:
              _SCREAMING_SNAKE_CASE             =    next(__lowerCamelCase					)
              if (line not in full_file_func) and (line.lstrip() != _dl_line):
                  if "TESTING_MOCKED_DATALOADERS" not in line:
                      new_feature_code.append(__lowerCamelCase					)
                      passed_idxs.append(__lowerCamelCase					)
                  else:
                      # Skip over the `config['num_epochs'] = 2` statement
                      _SCREAMING_SNAKE_CASE             =    next(__lowerCamelCase					)
  
      # Extract out just the new parts from the full_file_training_func
      _SCREAMING_SNAKE_CASE             =    []
      _SCREAMING_SNAKE_CASE             =    []  # We keep track of the idxs just in case it's a repeated statement
      for i, line in enumerate(__lowerCamelCase					):
          if i not in passed_idxs:
              if (line not in full_file_func) and (line.lstrip() != _dl_line):
                  if "TESTING_MOCKED_DATALOADERS" not in line:
                      new_full_example_parts.append(__lowerCamelCase					)
                      passed_idxs.append(__lowerCamelCase					)
  
      # Finally, get the overall diff
      _SCREAMING_SNAKE_CASE             =    [line for line in new_feature_code if line not in new_full_example_parts]
      if secondary_filename is not None:
          _SCREAMING_SNAKE_CASE             =    [line for line in full_file_contents if line not in secondary_file_func]
          _SCREAMING_SNAKE_CASE             =    [line for line in diff_from_example if line not in diff_from_two]
  
      return diff_from_example
  
  
  
  