




'''simple docstring'''




import argparse
import json
import re
import string
import sys

import numpy as np


snake_case__          =						0.5




def 						snake_case__    ( lowerCamelCase__				:   Tuple			,		lowerCamelCase__				:   List[str]			)   ->   int:
					A_ :     Tuple =							['''.''', ''',''', ''';''', ''':''']

					for token in remove_tokens:
										A_ :     Dict =							ground_truth.replace(lowerCamelCase__			,		''''''			)
										A_ :     Tuple =							prediction.replace(lowerCamelCase__			,		''''''			)

					A_ ,A_ :     Optional[int] =							ground_truth.lower(), prediction.lower()
					A_ ,A_ :     str =							ground_truth.replace('''/'''			,		''' '''			), prediction.replace('''/'''			,		''' '''			)
					A_ ,A_ :     Tuple =							set(ground_truth.split(''' '''			)			), set(prediction.split(''' '''			)			)

					A_ :     str =							ground_truth.intersection(lowerCamelCase__			)
					A_ :     Optional[Any] =							ground_truth.union(lowerCamelCase__			)
					A_ :     List[str] =							len(lowerCamelCase__			) / len(lowerCamelCase__			)
					return jaccard




def 						snake_case__    ( lowerCamelCase__				:   Dict			)   ->   Tuple:

					def remove_articles(lowerCamelCase__				:   List[Any]			):
										return re.sub(R'''\b(a|an|the)\b'''			,		''' '''			,		lowerCamelCase__			)

					def white_space_fix(lowerCamelCase__				:   str			):
										return " ".join(text.split()			)

					def remove_punc(lowerCamelCase__				:   List[Any]			):
										A_ :     Any =							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 						snake_case__    ( lowerCamelCase__				:   List[str]			,		lowerCamelCase__				:   Optional[Any]			,		lowerCamelCase__				:   Optional[Any]			)   ->   Union[str, Any]:
					A_ ,A_ ,A_ :     int =							0, 0, 0

					A_ :     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
															A_ :     Dict =							False
															for pred in predictions:
																				if substr_ok:
																									A_ :     Dict =							get_jaccard(lowerCamelCase__			,		lowerCamelCase__			) >= IOU_THRESH or ground_truth in pred
																				else:
																									A_ :     str =							get_jaccard(lowerCamelCase__			,		lowerCamelCase__			) >= IOU_THRESH
																				if is_match:
																									A_ :     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.)
															A_ :     Optional[Any] =							False
															for ground_truth in ground_truths:
																				assert len(lowerCamelCase__			) > 0
																				if substr_ok:
																									A_ :     Optional[Any] =							get_jaccard(lowerCamelCase__			,		lowerCamelCase__			) >= IOU_THRESH or ground_truth in pred
																				else:
																									A_ :     Tuple =							get_jaccard(lowerCamelCase__			,		lowerCamelCase__			) >= IOU_THRESH
																				if is_match:
																									A_ :     str =							True

															if not match_found:
																				fp += 1

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

					return precision, recall




def 						snake_case__    ( lowerCamelCase__				:   str			)   ->   Union[str, Any]:
					A_ :     Optional[Any] =							precisions[::-1]
					for i in range(1			,		len(lowerCamelCase__			)			):
										A_ :     int =							max(precision_best[i - 1]			,		precision_best[i]			)
					A_ :     Optional[int] =							precision_best[::-1]
					return precisions




def 						snake_case__    ( lowerCamelCase__				:   Dict			,		lowerCamelCase__				:   Optional[int]			)   ->   List[Any]:
					A_ :     List[Any] =							process_precisions(lowerCamelCase__			)
					A_ :     Dict =							np.trapz(lowerCamelCase__			,		lowerCamelCase__			)
					if np.isnan(lowerCamelCase__			):
										return 0
					return aupr




def 						snake_case__    ( lowerCamelCase__				:   Union[str, Any]			,		lowerCamelCase__				:   Any			,		lowerCamelCase__				:   Optional[Any]			)   ->   List[Any]:
					A_ :     Any =							process_precisions(lowerCamelCase__			)
					A_ :     Tuple =							0
					for prec, recall in zip(lowerCamelCase__			,		lowerCamelCase__			):
										if recall >= recall_thresh:
															A_ :     int =							prec
															break
					return prec_at_recall




def 						snake_case__    ( lowerCamelCase__				:   Optional[int]			,		lowerCamelCase__				:   List[Any]			)   ->   Union[str, Any]:
					return normalize_answer(lowerCamelCase__			) == normalize_answer(lowerCamelCase__			)




def 						snake_case__    ( lowerCamelCase__				:   Any			,		lowerCamelCase__				:   List[str]			,		lowerCamelCase__				:   str			)   ->   List[str]:
					A_ :     List[str] =							0
					for pred in predictions:
										for ground_truth in ground_truths:
															A_ :     Optional[int] =							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 						snake_case__    ( lowerCamelCase__				:   Optional[Any]			,		lowerCamelCase__				:   int			)   ->   str:
					A_ :     Union[str, Any] =							0
					A_ :     Optional[int] =							[]
					A_ :     int =							[]
					for article in dataset:
										for paragraph in article["paragraphs"]:
															for qa in paragraph["qas"]:
																				total += 1
																				if qa["id"] not in predictions:
																									A_ :     Any =							'''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
																									print(lowerCamelCase__			,		file=sys.stderr			)
																									continue
																				A_ :     Optional[Any] =							[x['''text'''] for x in qa['''answers''']]
																				A_ :     List[str] =							predictions[qa['''id''']]
																				A_ ,A_ :     List[Any] =							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__			)

					A_ :     Union[str, Any] =							[x for _, x in sorted(zip(lowerCamelCase__			,		lowerCamelCase__			)			)]
					recalls.sort()

					A_ :     Optional[Any] =							100.0 * fa / total
					A_ :     Any =							100.0 * exact_match / total
					A_ :     Any =							get_aupr(lowerCamelCase__			,		lowerCamelCase__			)

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









'''simple docstring'''




import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


snake_case__          =						logging.getLogger(__name__)






class     UpperCamelCase_     :







						"""simple docstring"""


						def __init__(     self      :       Tuple    ):





											"""simple docstring"""







											A_ :     Optional[int] =							False







						def       _a    (     self      :       Any							, _lowerCamelCase      :       List[str]							, _lowerCamelCase      :       Any							, _lowerCamelCase      :       str							, _lowerCamelCase      :       int    ):





											"""simple docstring"""







											if not self.initialized:
																A_ :     Optional[Any] =							RagRetriever(
																    _lowerCamelCase							, question_encoder_tokenizer=_lowerCamelCase							, generator_tokenizer=_lowerCamelCase							, index=_lowerCamelCase							, init_retrieval=_lowerCamelCase							, )
																A_ :     Optional[Any] =							True







						def       _a    (     self      :       int    ):





											"""simple docstring"""







											self.retriever.index.init_index()







						def       _a    (     self      :       Any    ):





											"""simple docstring"""







											del self.retriever
											A_ :     List[Any] =							False







						def       _a    (     self      :       Optional[int]							, _lowerCamelCase      :       Union[str, Any]							, _lowerCamelCase      :       Union[str, Any]    ):





											"""simple docstring"""







											A_ ,A_ :     Dict =							self.retriever._main_retrieve(_lowerCamelCase							, _lowerCamelCase    )
											A_ :     int =							self.retriever.index.get_doc_dicts(_lowerCamelCase    )
											return doc_ids, retrieved_doc_embeds, doc_dicts






class     UpperCamelCase_     (a__     ):







						"""simple docstring"""


						def __init__(     self      :       int							, _lowerCamelCase      :       Optional[Any]							, _lowerCamelCase      :       Dict							, _lowerCamelCase      :       Optional[int]							, _lowerCamelCase      :       Dict							, _lowerCamelCase      :       Tuple=None    ):





											"""simple docstring"""







											if index is not None and index.is_initialized() and len(_lowerCamelCase    ) > 0:
																raise ValueError(
																    '''When using Ray for distributed fine-tuning, '''
																    '''you\'ll need to provide the paths instead, '''
																    '''as the dataset and the index are loaded '''
																    '''separately. More info in examples/rag/use_own_knowledge_dataset.py '''    )

											super().__init__(
											    _lowerCamelCase							, question_encoder_tokenizer=_lowerCamelCase							, generator_tokenizer=_lowerCamelCase							, index=_lowerCamelCase							, init_retrieval=_lowerCamelCase							, )

											A_ :     Tuple =							retrieval_workers
											A_ :     Any =							question_encoder_tokenizer
											A_ :     Dict =							generator_tokenizer
											if len(self.retrieval_workers    ) > 0:
																ray.get(
																    [
																        worker.create_rag_retriever.remote(_lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase    )
																        for worker in self.retrieval_workers
																    ]    )







						def       _a    (     self      :       Dict    ):





											"""simple docstring"""







											logger.info('''initializing retrieval'''    )

											if len(self.retrieval_workers    ) > 0:
																ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]    )
											else:
																# Non-distributed training. Load index into this same process.
																self.index.init_index()







						def       _a    (     self      :       Dict							, _lowerCamelCase      :       Tuple							, _lowerCamelCase      :       int    ):





											"""simple docstring"""







											if len(self.retrieval_workers    ) > 0:
																# Select a random retrieval actor.
																A_ :     Optional[int] =							self.retrieval_workers[random.randint(0							, len(self.retrieval_workers    ) - 1    )]
																A_ ,A_ ,A_ :     Union[str, Any] =							ray.get(
																    random_worker.retrieve.remote(_lowerCamelCase							, _lowerCamelCase    )    )
											else:
																A_ ,A_ :     List[str] =							self._main_retrieve(_lowerCamelCase							, _lowerCamelCase    )
																A_ :     Union[str, Any] =							self.index.get_doc_dicts(_lowerCamelCase    )
											return retrieved_doc_embeds, doc_ids, doc_dicts







						@classmethod
						def       _a    (     cls      :       List[str]							, _lowerCamelCase      :       Union[str, Any]							, _lowerCamelCase      :       Optional[Any]=None							, **_lowerCamelCase      :       str    ):





											"""simple docstring"""







											return super(_lowerCamelCase							, cls    ).get_tokenizers(_lowerCamelCase							, _lowerCamelCase							, **_lowerCamelCase    )







						@classmethod
						def       _a    (     cls      :       Optional[Any]							, _lowerCamelCase      :       Any							, _lowerCamelCase      :       List[Any]							, _lowerCamelCase      :       str=None							, **_lowerCamelCase      :       Tuple    ):





											"""simple docstring"""







											A_ :     Tuple =							kwargs.pop('''config'''							, _lowerCamelCase    ) or RagConfig.from_pretrained(_lowerCamelCase							, **_lowerCamelCase    )
											A_ :     List[Any] =							RagTokenizer.from_pretrained(_lowerCamelCase							, config=_lowerCamelCase    )
											A_ :     Union[str, Any] =							rag_tokenizer.question_encoder
											A_ :     Dict =							rag_tokenizer.generator

											if indexed_dataset is not None:
																A_ :     Any =							'''custom'''
																A_ :     int =							CustomHFIndex(config.retrieval_vector_size							, _lowerCamelCase    )
											else:
																A_ :     Optional[int] =							cls._build_index(_lowerCamelCase    )

											return cls(
											    _lowerCamelCase							, question_encoder_tokenizer=_lowerCamelCase							, generator_tokenizer=_lowerCamelCase							, retrieval_workers=_lowerCamelCase							, index=_lowerCamelCase							, )







						def       _a    (     self      :       Optional[Any]    ):





											"""simple docstring"""







											logger.info('''re-loading the new dataset with embeddings'''    )
											# access from the training loop

											ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]    )

											# build the index object again
											A_ :     Dict =							self._build_index(self.config    )

											ray.get(
											    [
											        worker.create_rag_retriever.remote(
											            self.config							, self.question_encoder_tokenizer							, self.generator_tokenizer							, _lowerCamelCase    )
											        for worker in self.retrieval_workers
											    ]    )



