





import argparse
import json
import re
import string
import sys

import numpy as np


__UpperCAmelCase			:  Optional[Any]										=				0.5







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Tuple:
  __snake_case:		Optional[Any]					    =		[""".""", """,""", """;""", """:"""]

  for token in remove_tokens:
    __snake_case:		str					    =		ground_truth.replace(SCREAMING_SNAKE_CASE__   ,   """""")
    __snake_case:		Union[str, Any]					    =		prediction.replace(SCREAMING_SNAKE_CASE__   ,   """""")

  __snake_case:		List[str]					    =		ground_truth.lower(), prediction.lower()
  __snake_case:		int					    =		ground_truth.replace("""/"""   ,   """ """), prediction.replace("""/"""   ,   """ """)
  __snake_case:		Optional[int]					    =		set(ground_truth.split(""" """)), set(prediction.split(""" """))

  __snake_case:		List[str]					    =		ground_truth.intersection(SCREAMING_SNAKE_CASE__)
  __snake_case:		Optional[Any]					    =		ground_truth.union(SCREAMING_SNAKE_CASE__)
  __snake_case:		Tuple					    =		len(SCREAMING_SNAKE_CASE__) / len(SCREAMING_SNAKE_CASE__)
  return jaccard







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       Optional[Any]:

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

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

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

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

  return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE__))))







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       int:
  __snake_case:		int					    =		0, 0, 0

  __snake_case:		Any					    =		"""Parties""" in qa_id

  # first check if ground truth is empty
  if len(SCREAMING_SNAKE_CASE__) == 0:
    if len(SCREAMING_SNAKE_CASE__) > 0:
      fp += len(SCREAMING_SNAKE_CASE__)  # false positive for each one
  else:
    for ground_truth in ground_truths:
      assert len(SCREAMING_SNAKE_CASE__) > 0
      # check if there is a match
      __snake_case:		Optional[Any]					    =		False
      for pred in predictions:
        if substr_ok:
          __snake_case:		List[Any]					    =		get_jaccard(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__) >= IOU_THRESH or ground_truth in pred
        else:
          __snake_case:		int					    =		get_jaccard(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__) >= IOU_THRESH
        if is_match:
          __snake_case:		List[Any]					    =		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.)
      __snake_case:		List[Any]					    =		False
      for ground_truth in ground_truths:
        assert len(SCREAMING_SNAKE_CASE__) > 0
        if substr_ok:
          __snake_case:		Union[str, Any]					    =		get_jaccard(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__) >= IOU_THRESH or ground_truth in pred
        else:
          __snake_case:		str					    =		get_jaccard(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__) >= IOU_THRESH
        if is_match:
          __snake_case:		Dict					    =		True

      if not match_found:
        fp += 1

  __snake_case:		List[str]					    =		tp / (tp + fp) if tp + fp > 0 else np.nan
  __snake_case:		Any					    =		tp / (tp + fn) if tp + fn > 0 else np.nan

  return precision, recall







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       int:
  __snake_case:		Tuple					    =		precisions[::-1]
  for i in range(1   ,   len(SCREAMING_SNAKE_CASE__)):
    __snake_case:		List[str]					    =		max(precision_best[i - 1]   ,   precision_best[i])
  __snake_case:		int					    =		precision_best[::-1]
  return precisions







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       int:
  __snake_case:		Optional[Any]					    =		process_precisions(SCREAMING_SNAKE_CASE__)
  __snake_case:		int					    =		np.trapz(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)
  if np.isnan(SCREAMING_SNAKE_CASE__):
    return 0
  return aupr







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       List[str]:
  __snake_case:		Tuple					    =		process_precisions(SCREAMING_SNAKE_CASE__)
  __snake_case:		Optional[Any]					    =		0
  for prec, recall in zip(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__):
    if recall >= recall_thresh:
      __snake_case:		List[Any]					    =		prec
      break
  return prec_at_recall







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       List[str]:
  return normalize_answer(SCREAMING_SNAKE_CASE__) == normalize_answer(SCREAMING_SNAKE_CASE__)







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Any:
  __snake_case:		List[str]					    =		0
  for pred in predictions:
    for ground_truth in ground_truths:
      __snake_case:		List[str]					    =		metric_fn(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)
      if score == 1:  # break the loop when one prediction matches the ground truth
        break
    if score == 1:
      break
  return score







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       int:
  __snake_case:		Optional[Any]					    =		0
  __snake_case:		Tuple					    =		[]
  __snake_case:		List[Any]					    =		[]
  for article in dataset:
    for paragraph in article["paragraphs"]:
      for qa in paragraph["qas"]:
        total += 1
        if qa["id"] not in predictions:
          __snake_case:		Optional[int]					    =		"""Unanswered question """ + qa["""id"""] + """ will receive score 0."""
          print(SCREAMING_SNAKE_CASE__   ,   file=sys.stderr)
          continue
        __snake_case:		Optional[Any]					    =		[x["""text"""] for x in qa["""answers"""]]
        __snake_case:		str					    =		predictions[qa["""id"""]]
        __snake_case:		List[Any]					    =		compute_precision_recall(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   qa["""id"""])

        precisions.append(SCREAMING_SNAKE_CASE__)
        recalls.append(SCREAMING_SNAKE_CASE__)

        if precision == 0 and recall == 0:
          fa += 0
        else:
          fa += 2 * (precision * recall) / (precision + recall)

        exact_match += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)

  __snake_case:		List[Any]					    =		[x for _, x in sorted(zip(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__))]
  recalls.sort()

  __snake_case:		List[str]					    =		100.0 * fa / total
  __snake_case:		Optional[Any]					    =		100.0 * exact_match / total
  __snake_case:		Dict					    =		get_aupr(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)

  __snake_case:		Tuple					    =		get_prec_at_recall(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   recall_thresh=0.9)
  __snake_case:		Dict					    =		get_prec_at_recall(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   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__":
      __UpperCAmelCase			:  int										=				argparse.ArgumentParser(description="Evaluation for CUAD")
      parser.add_argument("dataset_file", help="Dataset file")
      parser.add_argument("prediction_file", help="Prediction File")
      __UpperCAmelCase			:  int										=				parser.parse_args()
      with open(args.dataset_file) as dataset_file:
            __UpperCAmelCase			:  List[Any]										=				json.load(dataset_file)
            __UpperCAmelCase			:  Any										=				dataset_json['''data''']
      with open(args.prediction_file) as prediction_file:
            __UpperCAmelCase			:  Union[str, Any]										=				json.load(prediction_file)
      print(json.dumps(evaluate(dataset, predictions)))














'''simple docstring'''







import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class _lowercase      (							_lowercase							, unittest.TestCase						):
							a							 =			BartTokenizer
							a							 =			BartTokenizerFast
							a							 =			True
							a							 =			filter_roberta_detectors




							def 						lowerCamelCase_							(				self:							Union[str, Any]	):
														super().setUp()
														lowerCamelCase__   :      int			    =			[
														    """l""",
														    """o""",
														    """w""",
														    """e""",
														    """r""",
														    """s""",
														    """t""",
														    """i""",
														    """d""",
														    """n""",
														    """\u0120""",
														    """\u0120l""",
														    """\u0120n""",
														    """\u0120lo""",
														    """\u0120low""",
														    """er""",
														    """\u0120lowest""",
														    """\u0120newer""",
														    """\u0120wider""",
														    """<unk>""",
														]
														lowerCamelCase__   :      List[str]			    =			dict(zip(UpperCamelCase__     ,				range(len(UpperCamelCase__	)	)	)	)
														lowerCamelCase__   :      List[str]			    =			["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
														lowerCamelCase__   :      List[Any]			    =			{"""unk_token""": """<unk>"""}

														lowerCamelCase__   :      List[str]			    =			os.path.join(self.tmpdirname     ,				VOCAB_FILES_NAMES["""vocab_file"""]	)
														lowerCamelCase__   :      List[str]			    =			os.path.join(self.tmpdirname     ,				VOCAB_FILES_NAMES["""merges_file"""]	)
														with open(self.vocab_file     ,				"""w"""     ,				encoding="""utf-8"""	) as fp:
																					fp.write(json.dumps(UpperCamelCase__	) + """\n"""	)
														with open(self.merges_file     ,				"""w"""     ,				encoding="""utf-8"""	) as fp:
																					fp.write("""\n""".join(UpperCamelCase__	)	)




							def 						lowerCamelCase_							(				self:							Tuple     ,				**UpperCamelCase__:							Dict	):
														kwargs.update(self.special_tokens_map	)
														return self.tokenizer_class.from_pretrained(self.tmpdirname     ,				**UpperCamelCase__	)




							def 						lowerCamelCase_							(				self:							Optional[int]     ,				**UpperCamelCase__:							Tuple	):
														kwargs.update(self.special_tokens_map	)
														return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     ,				**UpperCamelCase__	)




							def 						lowerCamelCase_							(				self:							List[str]     ,				UpperCamelCase__:							List[Any]	):
														return "lower newer", "lower newer"




							@cached_property
							def 						lowerCamelCase_							(				self:							str	):
														return BartTokenizer.from_pretrained("""facebook/bart-large"""	)




							@cached_property
							def 						lowerCamelCase_							(				self:							Union[str, Any]	):
														return BartTokenizerFast.from_pretrained("""facebook/bart-large"""	)




							@require_torch
							def 						lowerCamelCase_							(				self:							Optional[int]	):
														lowerCamelCase__   :      Any			    =			["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
														lowerCamelCase__   :      List[Any]			    =			[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Tuple			    =			tokenizer(UpperCamelCase__     ,				max_length=len(UpperCamelCase__	)     ,				padding=UpperCamelCase__     ,				return_tensors="""pt"""	)
																					self.assertIsInstance(UpperCamelCase__     ,				UpperCamelCase__	)

																					self.assertEqual((2, 9)     ,				batch.input_ids.shape	)
																					self.assertEqual((2, 9)     ,				batch.attention_mask.shape	)
																					lowerCamelCase__   :      Optional[Any]			    =			batch.input_ids.tolist()[0]
																					self.assertListEqual(UpperCamelCase__     ,				UpperCamelCase__	)
																					# Test that special tokens are reset




							@require_torch
							def 						lowerCamelCase_							(				self:							Tuple	):
														lowerCamelCase__   :      str			    =			["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Union[str, Any]			    =			tokenizer(UpperCamelCase__     ,				padding=UpperCamelCase__     ,				return_tensors="""pt"""	)
																					# check if input_ids are returned and no labels
																					self.assertIn("""input_ids"""     ,				UpperCamelCase__	)
																					self.assertIn("""attention_mask"""     ,				UpperCamelCase__	)
																					self.assertNotIn("""labels"""     ,				UpperCamelCase__	)
																					self.assertNotIn("""decoder_attention_mask"""     ,				UpperCamelCase__	)




							@require_torch
							def 						lowerCamelCase_							(				self:							Dict	):
														lowerCamelCase__   :      str			    =			[
														    """Summary of the text.""",
														    """Another summary.""",
														]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Any			    =			tokenizer(text_target=UpperCamelCase__     ,				max_length=32     ,				padding="""max_length"""     ,				return_tensors="""pt"""	)
																					self.assertEqual(32     ,				targets["""input_ids"""].shape[1]	)




							@require_torch
							def 						lowerCamelCase_							(				self:							str	):
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Union[str, Any]			    =			tokenizer(
																					    ["""I am a small frog""" * 1_024, """I am a small frog"""]     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				return_tensors="""pt"""	)
																					self.assertIsInstance(UpperCamelCase__     ,				UpperCamelCase__	)
																					self.assertEqual(batch.input_ids.shape     ,				(2, 1_024)	)




							@require_torch
							def 						lowerCamelCase_							(				self:							Optional[Any]	):
														lowerCamelCase__   :      Optional[Any]			    =			["""A long paragraph for summarization."""]
														lowerCamelCase__   :      Any			    =			[
														    """Summary of the text.""",
														]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Union[str, Any]			    =			tokenizer(UpperCamelCase__     ,				return_tensors="""pt"""	)
																					lowerCamelCase__   :      Union[str, Any]			    =			tokenizer(text_target=UpperCamelCase__     ,				return_tensors="""pt"""	)
																					lowerCamelCase__   :      List[str]			    =			inputs["""input_ids"""]
																					lowerCamelCase__   :      Optional[int]			    =			targets["""input_ids"""]
																					self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()	)
																					self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()	)
																					self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()	)
																					self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()	)




							def 						lowerCamelCase_							(				self:							Optional[int]	):
														pass




							def 						lowerCamelCase_							(				self:							List[str]	):
														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
																												lowerCamelCase__   :      int			    =			self.rust_tokenizer_class.from_pretrained(UpperCamelCase__     ,				**UpperCamelCase__	)
																												lowerCamelCase__   :      int			    =			self.tokenizer_class.from_pretrained(UpperCamelCase__     ,				**UpperCamelCase__	)
																												lowerCamelCase__   :      Dict			    =			"""A, <mask> AllenNLP sentence."""
																												lowerCamelCase__   :      Dict			    =			tokenizer_r.encode_plus(UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__     ,				return_token_type_ids=UpperCamelCase__	)
																												lowerCamelCase__   :      Union[str, Any]			    =			tokenizer_p.encode_plus(UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__     ,				return_token_type_ids=UpperCamelCase__	)

																												# token_type_ids should put 0 everywhere
																												self.assertEqual(sum(tokens_r["""token_type_ids"""]	)     ,				sum(tokens_p["""token_type_ids"""]	)	)

																												# attention_mask should put 1 everywhere, so sum over length should be 1
																												self.assertEqual(
																												    sum(tokens_r["""attention_mask"""]	) / len(tokens_r["""attention_mask"""]	)     ,				sum(tokens_p["""attention_mask"""]	) / len(tokens_p["""attention_mask"""]	)     ,				)

																												lowerCamelCase__   :      Any			    =			tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]	)
																												lowerCamelCase__   :      Any			    =			tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]	)

																												# Rust correctly handles the space before the mask while python doesnt
																												self.assertSequenceEqual(tokens_p["""input_ids"""]     ,				[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)
																												self.assertSequenceEqual(tokens_r["""input_ids"""]     ,				[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)

																												self.assertSequenceEqual(
																												    UpperCamelCase__     ,				["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]	)
																												self.assertSequenceEqual(
																												    UpperCamelCase__     ,				["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]	)

