

import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





class    __UpperCAmelCase	(			lowercase__		):



   def 			__magic_name__       (				self				:	str,						__A				:	Any,						__A				:	List[str]=False		):
     UpperCAmelCase			:				int										=					(
         filename_skip_take["filename"],
         filename_skip_take["skip"] if "skip" in filename_skip_take else None,
         filename_skip_take["take"] if "take" in filename_skip_take else None,
     )
     open(os.path.join(__A		),						'''wb'''		).close()
     UpperCAmelCase			:				Optional[int]										=					pa.Table.from_pydict({'''filename''': [Path(__A		).name] * 1_0_0}		)
     if take == -1:
       UpperCAmelCase			:				Any										=					len(__A		) - skip
     if skip is not None and take is not None:
       UpperCAmelCase			:				Tuple										=					pa_table.slice(__A,						__A		)
     return pa_table






class    __UpperCAmelCase	(			lowercase__		):



   def 			__magic_name__       (				self				:	str		):
     UpperCAmelCase			:				str										=					"my_name"
     UpperCAmelCase			:				Dict										=					SplitInfo(name='''train''',						num_examples=1_0_0		)
     UpperCAmelCase			:				List[str]										=					SplitInfo(name='''test''',						num_examples=1_0_0		)
     UpperCAmelCase			:				List[Any]										=					[train_info, test_info]
     UpperCAmelCase			:				List[str]										=					SplitDict()
     split_dict.add(__A		)
     split_dict.add(__A		)
     UpperCAmelCase			:				Optional[Any]										=					DatasetInfo(splits=__A		)

     with tempfile.TemporaryDirectory() as tmp_dir:
       UpperCAmelCase			:				Any										=					ReaderTest(__A,						__A		)

       UpperCAmelCase			:				Optional[Any]										=					"test[:33%]"
       UpperCAmelCase			:				List[Any]										=					Dataset(**reader.read(__A,						__A,						__A		)		)
       self.assertEqual(dset['''filename'''][0],						F'''{name}-test'''		)
       self.assertEqual(dset.num_rows,						3_3		)
       self.assertEqual(dset.num_columns,						1		)

       UpperCAmelCase			:				Tuple										=					["train", "test[:33%]"]
       UpperCAmelCase			:				Tuple										=					[Split.TRAIN, ReadInstruction.from_spec('''test[:33%]'''		)]
       for instructions in [instructionsa, instructionsa]:
         UpperCAmelCase			:				Tuple										=					[reader.read(__A,						__A,						__A		) for instr in instructions]
         UpperCAmelCase			:				Any										=					(Dataset(**__A		) for dataset_kwargs in datasets_kwargs)
         self.assertEqual(train_dset['''filename'''][0],						F'''{name}-train'''		)
         self.assertEqual(train_dset.num_rows,						1_0_0		)
         self.assertEqual(train_dset.num_columns,						1		)
         self.assertIsInstance(train_dset.split,						__A		)
         self.assertEqual(str(train_dset.split		),						'''train'''		)
         self.assertEqual(test_dset['''filename'''][0],						F'''{name}-test'''		)
         self.assertEqual(test_dset.num_rows,						3_3		)
         self.assertEqual(test_dset.num_columns,						1		)
         self.assertIsInstance(test_dset.split,						__A		)
         self.assertEqual(str(test_dset.split		),						'''test[:33%]'''		)
         del train_dset, test_dset



   def 			__magic_name__       (				self				:	Optional[int]		):
     UpperCAmelCase			:				List[Any]										=					"my_name"
     UpperCAmelCase			:				List[Any]										=					SplitInfo(name='''train''',						num_examples=1_0_0_0,						shard_lengths=[1_0_0] * 1_0		)
     UpperCAmelCase			:				str										=					[train_info]
     UpperCAmelCase			:				str										=					SplitDict()
     split_dict.add(__A		)
     UpperCAmelCase			:				Any										=					DatasetInfo(splits=__A		)

     with tempfile.TemporaryDirectory() as tmp_dir:
       UpperCAmelCase			:				Optional[int]										=					ReaderTest(__A,						__A		)

       UpperCAmelCase			:				List[Any]										=					"train[:33%]"
       UpperCAmelCase			:				str										=					Dataset(**reader.read(__A,						__A,						__A		)		)
       self.assertEqual(dset['''filename'''][0],						F'''{name}-train-00000-of-00010'''		)
       self.assertEqual(dset['''filename'''][-1],						F'''{name}-train-00003-of-00010'''		)
       self.assertEqual(dset.num_rows,						3_3_0		)
       self.assertEqual(dset.num_columns,						1		)





   def 			__magic_name__       (				self				:	Optional[int]		):
     UpperCAmelCase			:				Union[str, Any]										=					SplitInfo(name='''train''',						num_examples=1_0_0		)
     UpperCAmelCase			:				List[Any]										=					SplitInfo(name='''test''',						num_examples=1_0_0		)
     UpperCAmelCase			:				int										=					SplitDict()
     split_dict.add(__A		)
     split_dict.add(__A		)
     UpperCAmelCase			:				str										=					DatasetInfo(splits=__A		)

     with tempfile.TemporaryDirectory() as tmp_dir:
       UpperCAmelCase			:				str										=					ReaderTest(__A,						__A		)

       UpperCAmelCase			:				Dict										=					[
           {"filename": os.path.join(__A,						'''train'''		)},
           {"filename": os.path.join(__A,						'''test'''		), "skip": 1_0, "take": 1_0},
       ]
       UpperCAmelCase			:				List[str]										=					Dataset(**reader.read_files(__A,						original_instructions='''train+test[10:20]'''		)		)
       self.assertEqual(dset.num_rows,						1_1_0		)
       self.assertEqual(dset.num_columns,						1		)
       del dset






@pytest.mark.parametrize('''in_memory'''							,					[False, True]     )
def      a__	(						UpperCAmelCase				:     str							,					UpperCAmelCase				:     Optional[Any]							,					UpperCAmelCase				:     Optional[Any]     )    ->		Dict:
  UpperCAmelCase			:				List[str]										=					arrow_file
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    UpperCAmelCase			:				str										=					ArrowReader.read_table(_lowerCamelCase							,					in_memory=_lowerCamelCase     )
  assert table.shape == dataset.data.shape
  assert set(table.column_names     ) == set(dataset.data.column_names     )
  assert dict(table.to_pydict()     ) == dict(dataset.data.to_pydict()     )  # to_pydict returns OrderedDict

@pytest.mark.parametrize('''in_memory'''							,					[False, True]     )
def      a__	(						UpperCAmelCase				:     List[str]							,					UpperCAmelCase				:     Tuple							,					UpperCAmelCase				:     str     )    ->		Optional[Any]:
  UpperCAmelCase			:				Tuple										=					arrow_file
  UpperCAmelCase			:				Any										=					ArrowReader(''''''							,					_lowerCamelCase     )
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    UpperCAmelCase			:				Optional[int]										=					reader.read_files([{'''filename''': filename}]							,					in_memory=_lowerCamelCase     )
  assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
  UpperCAmelCase			:				Optional[Any]										=					dataset_kwargs["arrow_table"]
  assert table.shape == dataset.data.shape
  assert set(table.column_names     ) == set(dataset.data.column_names     )
  assert dict(table.to_pydict()     ) == dict(dataset.data.to_pydict()     )  # to_pydict returns OrderedDict

def      a__	(						)    ->		Optional[Any]:
  assert ReadInstruction('''train'''							,					to=10							,					unit='''abs'''     ).to_spec() == "train[:10]"
  assert ReadInstruction('''train'''							,					from_=-80							,					to=10							,					unit='''%'''     ).to_spec() == "train[-80%:10%]"

  UpperCAmelCase			:				Dict										=					"train+test"
  assert ReadInstruction.from_spec(_lowerCamelCase     ).to_spec() == spec_train_test

  UpperCAmelCase			:				List[str]										=					"train[2:10]"
  assert ReadInstruction.from_spec(_lowerCamelCase     ).to_spec() == spec_train_abs

  UpperCAmelCase			:				Optional[int]										=					"train[15%:-20%]"
  assert ReadInstruction.from_spec(_lowerCamelCase     ).to_spec() == spec_train_pct

  UpperCAmelCase			:				str										=					"train[:10%](closest)"
  assert ReadInstruction.from_spec(_lowerCamelCase     ).to_spec() == "train[:10%]"

  UpperCAmelCase			:				Optional[int]										=					"train[:10%](pct1_dropremainder)"
  assert ReadInstruction.from_spec(_lowerCamelCase     ).to_spec() == spec_train_pct_rounding

  UpperCAmelCase			:				int										=					"train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
  assert ReadInstruction.from_spec(_lowerCamelCase     ).to_spec() == spec_train_test_pct_rounding

def      a__	(						)    ->		List[str]:
  UpperCAmelCase			:				Union[str, Any]										=					"dummy"
  UpperCAmelCase			:				Any										=					[SplitInfo(name='''train'''							,					num_examples=100     )]
  UpperCAmelCase			:				Dict										=					"train[:33%]"
  UpperCAmelCase			:				int										=					"arrow"
  UpperCAmelCase			:				Dict										=					"prefix"

  UpperCAmelCase			:				int										=					make_file_instructions(_lowerCamelCase							,					_lowerCamelCase							,					_lowerCamelCase							,					_lowerCamelCase							,					_lowerCamelCase     )
  assert isinstance(_lowerCamelCase							,					_lowerCamelCase     )
  assert file_instructions.num_examples == 33
  assert file_instructions.file_instructions == [
      {"filename": os.path.join(_lowerCamelCase							,					f'''{name}-train.arrow'''     ), "skip": 0, "take": 33}
  ]

  UpperCAmelCase			:				Tuple										=					[SplitInfo(name='''train'''							,					num_examples=100							,					shard_lengths=[10] * 10     )]
  UpperCAmelCase			:				Union[str, Any]										=					make_file_instructions(_lowerCamelCase							,					_lowerCamelCase							,					_lowerCamelCase							,					_lowerCamelCase							,					_lowerCamelCase     )
  assert isinstance(_lowerCamelCase							,					_lowerCamelCase     )
  assert file_instructions.num_examples == 33
  assert file_instructions.file_instructions == [
      {"filename": os.path.join(_lowerCamelCase							,					f'''{name}-train-00000-of-00010.arrow'''     ), "skip": 0, "take": -1},
      {"filename": os.path.join(_lowerCamelCase							,					f'''{name}-train-00001-of-00010.arrow'''     ), "skip": 0, "take": -1},
      {"filename": os.path.join(_lowerCamelCase							,					f'''{name}-train-00002-of-00010.arrow'''     ), "skip": 0, "take": -1},
      {"filename": os.path.join(_lowerCamelCase							,					f'''{name}-train-00003-of-00010.arrow'''     ), "skip": 0, "take": 3},
  ]






@pytest.mark.parametrize('''name, expected_exception'''							,					[(None, TypeError), ('''''', ValueError)]     )
def      a__	(						UpperCAmelCase				:     str							,					UpperCAmelCase				:     List[str]     )    ->		str:
  UpperCAmelCase			:				List[str]										=					[SplitInfo(name='''train'''							,					num_examples=100     )]
  UpperCAmelCase			:				List[Any]										=					"train"
  UpperCAmelCase			:				Tuple										=					"arrow"
  UpperCAmelCase			:				Dict										=					"prefix_path"
  with pytest.raises(_lowerCamelCase     ):
    UpperCAmelCase			:				Optional[Any]										=					make_file_instructions(_lowerCamelCase							,					_lowerCamelCase							,					_lowerCamelCase							,					_lowerCamelCase							,					_lowerCamelCase     )












import argparse
import json
import re
import string
import sys

import numpy as np


__lowercase							:Optional[Any]							=	0.5







def    UpperCAmelCase				(  _lowerCamelCase   :	int      , _lowerCamelCase   :	Tuple  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					List[Any] =       [".", ",", ";", ":"]

						for token in remove_tokens:
												SCREAMING_SNAKE_CASE__  :					List[Any] =       ground_truth.replace(_lowerCamelCase      , ""  )
												SCREAMING_SNAKE_CASE__  :					List[Any] =       prediction.replace(_lowerCamelCase      , ""  )

						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[Any] =       ground_truth.lower(), prediction.lower()
						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Dict =       ground_truth.replace("/"      , " "  ), prediction.replace("/"      , " "  )
						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					str =       set(ground_truth.split(" "  )  ), set(prediction.split(" "  )  )

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       ground_truth.intersection(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       ground_truth.union(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Dict =       len(_lowerCamelCase  ) / len(_lowerCamelCase  )
						return jaccard







def    UpperCAmelCase				(  _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


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

						def white_space_fix(_lowerCamelCase   :	Any  ):
												return " ".join(text.split()  )

						def remove_punc(_lowerCamelCase   :	List[Any]  ):
												SCREAMING_SNAKE_CASE__  :					Tuple =       set(string.punctuation  )
												return "".join(ch for ch in text if ch not in exclude  )

						def lower(_lowerCamelCase   :	str  ):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(_lowerCamelCase  )  )  )  )







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[Any]      , _lowerCamelCase   :	Optional[Any]      , _lowerCamelCase   :	Tuple  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Optional[int] =       0, 0, 0

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       "Parties" in qa_id

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

																		if not match_found:
																								fp += 1

						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       tp / (tp + fp) if tp + fp > 0 else np.nan
						SCREAMING_SNAKE_CASE__  :					Dict =       tp / (tp + fn) if tp + fn > 0 else np.nan

						return precision, recall







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					int =       precisions[::-1]
						for i in range(1      , len(_lowerCamelCase  )  ):
												SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       max(precision_best[i - 1]      , precision_best[i]  )
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       precision_best[::-1]
						return precisions







def    UpperCAmelCase				(  _lowerCamelCase   :	Dict      , _lowerCamelCase   :	str  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Tuple =       process_precisions(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					List[Any] =       np.trapz(_lowerCamelCase      , _lowerCamelCase  )
						if np.isnan(_lowerCamelCase  ):
												return 0
						return aupr







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	Any      , _lowerCamelCase   :	List[str]  ):





						'''simple docstring'''


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







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	Optional[Any]  ):





						'''simple docstring'''


						return normalize_answer(_lowerCamelCase  ) == normalize_answer(_lowerCamelCase  )







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	str      , _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


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







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	int  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Any =       0
						SCREAMING_SNAKE_CASE__  :					List[str] =       []
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       []
						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__  :					Union[str, Any] =       "Unanswered question " + qa["id"] + " will receive score 0."
																														print(_lowerCamelCase      , file=sys.stderr  )
																														continue
																								SCREAMING_SNAKE_CASE__  :					Any =       [x["text"] for x in qa["answers"]]
																								SCREAMING_SNAKE_CASE__  :					int =       predictions[qa["id"]]
																								SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Tuple =       compute_precision_recall(_lowerCamelCase      , _lowerCamelCase      , qa["id"]  )

																								precisions.append(_lowerCamelCase  )
																								recalls.append(_lowerCamelCase  )

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

																								exact_match += metric_max_over_ground_truths(_lowerCamelCase      , _lowerCamelCase      , _lowerCamelCase  )

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       [x for _, x in sorted(zip(_lowerCamelCase      , _lowerCamelCase  )  )]
						recalls.sort()

						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       1_0_0.0 * fa / total
						SCREAMING_SNAKE_CASE__  :					Dict =       1_0_0.0 * exact_match / total
						SCREAMING_SNAKE_CASE__  :					str =       get_aupr(_lowerCamelCase      , _lowerCamelCase  )

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