





import argparse
import json
import re
import string
import sys

import numpy as np


_lowerCamelCase       =	0.5






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							List[str]    ,      UpperCamelCase__:							Tuple			):
		SCREAMING_SNAKE_CASE__								=					[""".""", """,""", """;""", """:"""]

		for token in remove_tokens:
				SCREAMING_SNAKE_CASE__								=					ground_truth.replace(UpperCamelCase__    ,      """"""			)
				SCREAMING_SNAKE_CASE__								=					prediction.replace(UpperCamelCase__    ,      """"""			)

		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					ground_truth.lower(), prediction.lower()
		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					ground_truth.replace("""/"""    ,      """ """			), prediction.replace("""/"""    ,      """ """			)
		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					set(ground_truth.split(""" """			)			), set(prediction.split(""" """			)			)

		SCREAMING_SNAKE_CASE__								=					ground_truth.intersection(UpperCamelCase__			)
		SCREAMING_SNAKE_CASE__								=					ground_truth.union(UpperCamelCase__			)
		SCREAMING_SNAKE_CASE__								=					len(UpperCamelCase__			) / len(UpperCamelCase__			)
		return jaccard






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Dict			):




		def remove_articles(UpperCamelCase__:							Union[str, Any]			):
				return re.sub(R"""\b(a|an|the)\b"""    ,      """ """    ,      UpperCamelCase__			)

		def white_space_fix(UpperCamelCase__:							Optional[Any]			):
				return " ".join(text.split()			)

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

		def lower(UpperCamelCase__:							List[Any]			):
				return text.lower()

		return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__			)			)			)			)






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							List[str]    ,      UpperCamelCase__:							Optional[int]    ,      UpperCamelCase__:							Optional[Any]			):
		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					0, 0, 0

		SCREAMING_SNAKE_CASE__								=					"""Parties""" in qa_id

		# first check if ground truth is empty
		if len(UpperCamelCase__			) == 0:
				if len(UpperCamelCase__			) > 0:
						fp += len(UpperCamelCase__			)  # false positive for each one
		else:
				for ground_truth in ground_truths:
						assert len(UpperCamelCase__			) > 0
						# check if there is a match
						SCREAMING_SNAKE_CASE__								=					False
						for pred in predictions:
								if substr_ok:
										SCREAMING_SNAKE_CASE__								=					get_jaccard(UpperCamelCase__    ,      UpperCamelCase__			) >= IOU_THRESH or ground_truth in pred
								else:
										SCREAMING_SNAKE_CASE__								=					get_jaccard(UpperCamelCase__    ,      UpperCamelCase__			) >= 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(UpperCamelCase__			) > 0
								if substr_ok:
										SCREAMING_SNAKE_CASE__								=					get_jaccard(UpperCamelCase__    ,      UpperCamelCase__			) >= IOU_THRESH or ground_truth in pred
								else:
										SCREAMING_SNAKE_CASE__								=					get_jaccard(UpperCamelCase__    ,      UpperCamelCase__			) >= 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  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Dict			):
		SCREAMING_SNAKE_CASE__								=					precisions[::-1]
		for i in range(1    ,      len(UpperCamelCase__			)			):
				SCREAMING_SNAKE_CASE__								=					max(precision_best[i - 1]    ,      precision_best[i]			)
		SCREAMING_SNAKE_CASE__								=					precision_best[::-1]
		return precisions






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Optional[Any]    ,      UpperCamelCase__:							Any			):
		SCREAMING_SNAKE_CASE__								=					process_precisions(UpperCamelCase__			)
		SCREAMING_SNAKE_CASE__								=					np.trapz(UpperCamelCase__    ,      UpperCamelCase__			)
		if np.isnan(UpperCamelCase__			):
				return 0
		return aupr






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Optional[Any]    ,      UpperCamelCase__:							int    ,      UpperCamelCase__:							str			):
		SCREAMING_SNAKE_CASE__								=					process_precisions(UpperCamelCase__			)
		SCREAMING_SNAKE_CASE__								=					0
		for prec, recall in zip(UpperCamelCase__    ,      UpperCamelCase__			):
				if recall >= recall_thresh:
						SCREAMING_SNAKE_CASE__								=					prec
						break
		return prec_at_recall






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							int    ,      UpperCamelCase__:							Tuple			):
		return normalize_answer(UpperCamelCase__			) == normalize_answer(UpperCamelCase__			)






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Optional[int]    ,      UpperCamelCase__:							Optional[Any]    ,      UpperCamelCase__:							Dict			):
		SCREAMING_SNAKE_CASE__								=					0
		for pred in predictions:
				for ground_truth in ground_truths:
						SCREAMING_SNAKE_CASE__								=					metric_fn(UpperCamelCase__    ,      UpperCamelCase__			)
						if score == 1:  # break the loop when one prediction matches the ground truth
								break
				if score == 1:
						break
		return score






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							int    ,      UpperCamelCase__:							Optional[Any]			):
		SCREAMING_SNAKE_CASE__								=					SCREAMING_SNAKE_CASE__								=					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(UpperCamelCase__    ,      file=sys.stderr			)
										continue
								SCREAMING_SNAKE_CASE__								=					[x["""text"""] for x in qa["""answers"""]]
								SCREAMING_SNAKE_CASE__								=					predictions[qa["""id"""]]
								SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					compute_precision_recall(UpperCamelCase__    ,      UpperCamelCase__    ,      qa["""id"""]			)

								precisions.append(UpperCamelCase__			)
								recalls.append(UpperCamelCase__			)

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

								exact_match += metric_max_over_ground_truths(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__			)

		SCREAMING_SNAKE_CASE__								=					[x for _, x in sorted(zip(UpperCamelCase__    ,      UpperCamelCase__			)			)]
		recalls.sort()

		SCREAMING_SNAKE_CASE__								=					1_0_0.0 * fa / total
		SCREAMING_SNAKE_CASE__								=					1_0_0.0 * exact_match / total
		SCREAMING_SNAKE_CASE__								=					get_aupr(UpperCamelCase__    ,      UpperCamelCase__			)

		SCREAMING_SNAKE_CASE__								=					get_prec_at_recall(UpperCamelCase__    ,      UpperCamelCase__    ,      recall_thresh=0.9			)
		SCREAMING_SNAKE_CASE__								=					get_prec_at_recall(UpperCamelCase__    ,      UpperCamelCase__    ,      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__":
	_lowerCamelCase       =	argparse.ArgumentParser(description='Evaluation for CUAD')
	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)
		_lowerCamelCase       =	dataset_json["""data"""]
	with open(args.prediction_file) as prediction_file:
		_lowerCamelCase       =	json.load(prediction_file)
	print(json.dumps(evaluate(dataset, predictions)))





import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
      import sqlitea

      import sqlalchemy


__SCREAMING_SNAKE_CASE			:      Union[str, Any]      			=datasets.utils.logging.get_logger(__name__)






@dataclass
class 	A	(						datasets.BuilderConfig						):
     """simple docstring"""

     _snake_case:		Union[str, "sqlalchemy.sql.Selectable"]   =None
     _snake_case:		Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]   =None
     _snake_case:		Optional[Union[str, List[str]]]   =None
     _snake_case:		bool   =True
     _snake_case:		Optional[Union[List, Tuple, Dict]]   =None
     _snake_case:		Optional[Union[List, Dict]]   =None
     _snake_case:		Optional[List[str]]   =None
     _snake_case:		Optional[int]   =10_000
     _snake_case:		Optional[datasets.Features]   =None




     def        __snake_case      (			self     :      Optional[Any]	) ->				Union[str, Any]:
           if self.sql is None:
                 raise ValueError('sql must be specified'	)
           if self.con is None:
                 raise ValueError('con must be specified'	)




     def        __snake_case      (			self     :      List[Any]		,     A__     :      dict		,     A__     :      Optional[datasets.Features] = None		,     ) ->				str:
           lowerCamelCase__					     = config_kwargs.copy()
           # We need to stringify the Selectable object to make its hash deterministic

           # The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
           lowerCamelCase__					     = config_kwargs['sql']
           if not isinstance(A__		,     A__	):
                 if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
                       import sqlalchemy

                       if isinstance(A__		,     sqlalchemy.sql.Selectable	):
                             lowerCamelCase__					     = sqlalchemy.create_engine(config_kwargs['con'].split('://'	)[0] + '://'	)
                             lowerCamelCase__					     = str(sql.compile(dialect=engine.dialect	)	)
                             lowerCamelCase__					     = sql_str
                       else:
                             raise TypeError(
                                 F"""Supported types for 'sql' are string and sqlalchemy.sql.Selectable but got {type(A__	)}: {sql}"""	)
                 else:
                       raise TypeError(
                           F"""Supported types for 'sql' are string and sqlalchemy.sql.Selectable but got {type(A__	)}: {sql}"""	)
           lowerCamelCase__					     = config_kwargs['con']
           if not isinstance(A__		,     A__	):
                 lowerCamelCase__					     = id(A__	)
                 logger.info(
                     F"""SQL connection 'con' of type {type(A__	)} couldn't be hashed properly. To enable hashing, specify 'con' as URI string instead."""	)

           return super().create_config_id(A__		,     custom_features=A__	)




     @property
     def        __snake_case      (			self     :      Any	) ->				Union[str, Any]:
           lowerCamelCase__					     = {
               'index_col': self.index_col,
               'columns': self.columns,
               'params': self.params,
               'coerce_float': self.coerce_float,
               'parse_dates': self.parse_dates,
           }
           return pd_read_sql_kwargs






class 	A	(						datasets.ArrowBasedBuilder						):
     """simple docstring"""

     _snake_case:		List[str]   =SqlConfig




     def        __snake_case      (			self     :      Tuple	) ->				Dict:
           return datasets.DatasetInfo(features=self.config.features	)




     def        __snake_case      (			self     :      List[Any]		,     A__     :      Any	) ->				List[Any]:
           return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,     gen_kwargs={}	)]




     def        __snake_case      (			self     :      Union[str, Any]		,     A__     :      pa.Table	) ->				pa.Table:
           if self.config.features is not None:
                 lowerCamelCase__					     = self.config.features.arrow_schema
                 if all(not require_storage_cast(A__	) for feature in self.config.features.values()	):
                       # cheaper cast
                       lowerCamelCase__					     = pa.Table.from_arrays([pa_table[field.name] for field in schema]		,     schema=A__	)
                 else:
                       # more expensive cast; allows str <-> int/float or str to Audio for example
                       lowerCamelCase__					     = table_cast(A__		,     A__	)
           return pa_table




     def        __snake_case      (			self     :      int	) ->				List[Any]:
           lowerCamelCase__					     = self.config.chunksize
           lowerCamelCase__					     = pd.read_sql(
               self.config.sql		,     self.config.con		,     chunksize=A__		,     **self.config.pd_read_sql_kwargs	)
           lowerCamelCase__					     = [sql_reader] if chunksize is None else sql_reader
           for chunk_idx, df in enumerate(A__	):
                 lowerCamelCase__					     = pa.Table.from_pandas(A__	)
                 yield chunk_idx, self._cast_table(A__	)