




import argparse
import json
import re
import string
import sys

import numpy as np


lowercase__    :					Optional[Any]					=						0.5


def 						lowerCamelCase__   (		_A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					[".", ",", ";", ":"]

	for token in remove_tokens:
		snake_case_		   =					ground_truth.replace(_A					,     ""							)
		snake_case_		   =					prediction.replace(_A					,     ""							)

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

	snake_case_		   =					ground_truth.intersection(_A							)
	snake_case_		   =					ground_truth.union(_A							)
	snake_case_		   =					len(_A							) / len(_A							)
	return jaccard


def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





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

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

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

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

	return white_space_fix(remove_articles(remove_punc(lower(_A							)							)							)							)


def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	snake_case_   ,				snake_case_   ,				snake_case_		   =					0, 0, 0

	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
			snake_case_		   =					False
			for pred in predictions:
				if substr_ok:
					snake_case_		   =					get_jaccard(_A					,     _A							) >= IOU_THRESH or ground_truth in pred
				else:
					snake_case_		   =					get_jaccard(_A					,     _A							) >= IOU_THRESH
				if is_match:
					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.)
			snake_case_		   =					False
			for ground_truth in ground_truths:
				assert len(_A							) > 0
				if substr_ok:
					snake_case_		   =					get_jaccard(_A					,     _A							) >= IOU_THRESH or ground_truth in pred
				else:
					snake_case_		   =					get_jaccard(_A					,     _A							) >= IOU_THRESH
				if is_match:
					snake_case_		   =					True

			if not match_found:
				fp += 1

	snake_case_		   =					tp / (tp + fp) if tp + fp > 0 else np.nan
	snake_case_		   =					tp / (tp + fn) if tp + fn > 0 else np.nan

	return precision, recall


def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





	snake_case_		   =					precisions[::-1]
	for i in range(1					,     len(_A							)							):
		snake_case_		   =					max(precision_best[i - 1]					,     precision_best[i]							)
	snake_case_		   =					precision_best[::-1]
	return precisions


def 						lowerCamelCase__   (		_A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					process_precisions(_A							)
	snake_case_		   =					np.trapz(_A					,     _A							)
	if np.isnan(_A							):
		return 0
	return aupr


def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					process_precisions(_A							)
	snake_case_		   =					0
	for prec, recall in zip(_A					,     _A							):
		if recall >= recall_thresh:
			snake_case_		   =					prec
			break
	return prec_at_recall


def 						lowerCamelCase__   (		_A					,     _A							):







	'''simple docstring'''





	return normalize_answer(_A							) == normalize_answer(_A							)


def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					0
	for pred in predictions:
		for ground_truth in ground_truths:
			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__   (		_A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					snake_case_		   =					snake_case_		   =					0
	snake_case_		   =					[]
	snake_case_		   =					[]
	for article in dataset:
		for paragraph in article["paragraphs"]:
			for qa in paragraph["qas"]:
				total += 1
				if qa["id"] not in predictions:
					snake_case_		   =					"Unanswered question " + qa["id"] + " will receive score 0."
					print(_A					,     file=sys.stderr							)
					continue
				snake_case_		   =					[x["text"] for x in qa["answers"]]
				snake_case_		   =					predictions[qa["id"]]
				snake_case_   ,				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							)

	snake_case_		   =					[x for _, x in sorted(zip(_A					,     _A							)							)]
	recalls.sort()

	snake_case_		   =					1_00.0 * fa / total
	snake_case_		   =					1_00.0 * exact_match / total
	snake_case_		   =					get_aupr(_A					,     _A							)

	snake_case_		   =					get_prec_at_recall(_A					,     _A					,     recall_thresh=0.9							)
	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__    :					List[str]					=						argparse.ArgumentParser(description="Evaluation for CUAD")
		parser.add_argument("dataset_file", help="Dataset file")
		parser.add_argument("prediction_file", help="Prediction File")
		lowercase__    :					str					=						parser.parse_args()
		with open(args.dataset_file) as dataset_file:
				lowercase__    :					List[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)))






import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


lowercase__    :					List[str]					=						logging.get_logger(__name__)

lowercase__    :					Tuple					=						{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

lowercase__    :					Any					=						{
    "vocab_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/vocab.json"},
    "merges_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/merges.txt"},
}

lowercase__    :					Tuple					=						{
    "openai-gpt": 512,
}


def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





	snake_case_		   =					text.strip()
	if not text:
		return []
	snake_case_		   =					text.split()
	return tokens
class 		UpperCAmelCase       (    UpperCAmelCase__      ):







						'''simple docstring'''





						def __init__(						self							:					Dict	,     __lowercase							:					str=True	,     __lowercase							:					Union[str, Any]=None	,     __lowercase							:					str=True	,     __lowercase							:					Dict=None	,     __lowercase							:					str=True	,     ):






							"""simple docstring"""






							if never_split is None:
								snake_case_		   =					[]
							snake_case_		   =					do_lower_case
							snake_case_		   =					set(__lowercase						)
							snake_case_		   =					tokenize_chinese_chars
							snake_case_		   =					strip_accents
							snake_case_		   =					do_split_on_punc






						def 	snake_case__						(						self							:					Any	,     __lowercase							:					Any	,     __lowercase							:					Optional[int]=None						):






							"""simple docstring"""






							snake_case_		   =					self.never_split.union(set(__lowercase						)						) if never_split else self.never_split
							snake_case_		   =					self._clean_text(__lowercase						)

							# This was added on November 1st, 2018 for the multilingual and Chinese
							# models. This is also applied to the English models now, but it doesn't
							# matter since the English models were not trained on any Chinese data
							# and generally don't have any Chinese data in them (there are Chinese
							# characters in the vocabulary because Wikipedia does have some Chinese
							# words in the English Wikipedia.).
							if self.tokenize_chinese_chars:
								snake_case_		   =					self._tokenize_chinese_chars(__lowercase						)
							# prevents treating the same character with different unicode codepoints as different characters
							snake_case_		   =					unicodedata.normalize("NFC"	,     __lowercase						)
							snake_case_		   =					whitespace_tokenize(__lowercase						)
							snake_case_		   =					[]
							for token in orig_tokens:
								if token not in never_split:
									if self.do_lower_case:
										snake_case_		   =					token.lower()
										if self.strip_accents is not False:
											snake_case_		   =					self._run_strip_accents(__lowercase						)
									elif self.strip_accents:
										snake_case_		   =					self._run_strip_accents(__lowercase						)
								split_tokens.extend(self._run_split_on_punc(__lowercase	,     __lowercase						)						)

							snake_case_		   =					whitespace_tokenize(" ".join(__lowercase						)						)
							return output_tokens






						def 	snake_case__						(						self							:					str	,     __lowercase							:					List[str]						):






							"""simple docstring"""






							snake_case_		   =					unicodedata.normalize("NFD"	,     __lowercase						)
							snake_case_		   =					[]
							for char in text:
								snake_case_		   =					unicodedata.category(__lowercase						)
								if cat == "Mn":
									continue
								output.append(__lowercase						)
							return "".join(__lowercase						)






						def 	snake_case__						(						self							:					Tuple	,     __lowercase							:					Union[str, Any]	,     __lowercase							:					Optional[int]=None						):






							"""simple docstring"""






							if not self.do_split_on_punc or (never_split is not None and text in never_split):
								return [text]
							snake_case_		   =					list(__lowercase						)
							snake_case_		   =					0
							snake_case_		   =					True
							snake_case_		   =					[]
							while i < len(__lowercase						):
								snake_case_		   =					chars[i]
								if _is_punctuation(__lowercase						):
									output.append([char]						)
									snake_case_		   =					True
								else:
									if start_new_word:
										output.append([]						)
									snake_case_		   =					False
									output[-1].append(__lowercase						)
								i += 1

							return ["".join(__lowercase						) for x in output]






						def 	snake_case__						(						self							:					Optional[int]	,     __lowercase							:					str						):






							"""simple docstring"""






							snake_case_		   =					[]
							for char in text:
								snake_case_		   =					ord(__lowercase						)
								if self._is_chinese_char(__lowercase						):
									output.append(" "						)
									output.append(__lowercase						)
									output.append(" "						)
								else:
									output.append(__lowercase						)
							return "".join(__lowercase						)






						def 	snake_case__						(						self							:					Union[str, Any]	,     __lowercase							:					str						):






							"""simple docstring"""






							if (
							    (cp >= 0X4_e00 and cp <= 0X9_fff)
							    or (cp >= 0X3_400 and cp <= 0X4_dbf)  #
							    or (cp >= 0X20_000 and cp <= 0X2a_6df)  #
							    or (cp >= 0X2a_700 and cp <= 0X2b_73f)  #
							    or (cp >= 0X2b_740 and cp <= 0X2b_81f)  #
							    or (cp >= 0X2b_820 and cp <= 0X2c_eaf)  #
							    or (cp >= 0Xf_900 and cp <= 0Xf_aff)
							    or (cp >= 0X2f_800 and cp <= 0X2f_a1f)  #
							):  #
								return True

							return False






						def 	snake_case__						(						self							:					int	,     __lowercase							:					Dict						):






							"""simple docstring"""






							snake_case_		   =					[]
							for char in text:
								snake_case_		   =					ord(__lowercase						)
								if cp == 0 or cp == 0Xf_ffd or _is_control(__lowercase						):
									continue
								if _is_whitespace(__lowercase						):
									output.append(" "						)
								else:
									output.append(__lowercase						)
							return "".join(__lowercase						)







def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





	snake_case_		   =					set()
	snake_case_		   =					word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)							)
		snake_case_		   =					char
	return pairs


def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





	snake_case_		   =					text.replace("—"					,     "-"							)
	snake_case_		   =					text.replace("–"					,     "-"							)
	snake_case_		   =					text.replace("―"					,     "-"							)
	snake_case_		   =					text.replace("…"					,     "..."							)
	snake_case_		   =					text.replace("´"					,     "'"							)
	snake_case_		   =					re.sub(R"(-+|~+|!+|\"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)"					,     R" \1 "					,     _A							)
	snake_case_		   =					re.sub(R"\s*\n\s*"					,     " \n "					,     _A							)
	snake_case_		   =					re.sub(R"[^\S\n]+"					,     " "					,     _A							)
	return text.strip()






class 		UpperCAmelCase       (    UpperCAmelCase__      ):







						'''simple docstring'''





						lowerCAmelCase_							=       VOCAB_FILES_NAMES
						lowerCAmelCase_							=       PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase_							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase_							=       ['''input_ids''', '''attention_mask''']






						def __init__(						self							:					Dict	,     __lowercase							:					Union[str, Any]	,     __lowercase							:					Tuple	,     __lowercase							:					Any="<unk>"	,     **__lowercase							:					List[Any]						):






							"""simple docstring"""






							super().__init__(unk_token=__lowercase	,     **__lowercase						)

							try:
								import ftfy
								from spacy.lang.en import English

								snake_case_		   =					English()
								snake_case_		   =					_nlp.tokenizer
								snake_case_		   =					ftfy.fix_text
							except ImportError:
								logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy."						)
								snake_case_		   =					BasicTokenizer(do_lower_case=__lowercase						)
								snake_case_		   =					None

							with open(__lowercase	,     encoding="utf-8"						) as vocab_handle:
								snake_case_		   =					json.load(__lowercase						)
							snake_case_		   =					{v: k for k, v in self.encoder.items()}
							with open(__lowercase	,     encoding="utf-8"						) as merges_handle:
								snake_case_		   =					merges_handle.read().split("\n"						)[1:-1]
							snake_case_		   =					[tuple(merge.split()						) for merge in merges]
							snake_case_		   =					dict(zip(__lowercase	,     range(len(__lowercase						)						)						)						)
							snake_case_		   =					{}






						@property
						def 	snake_case__						(						self							:					List[Any]						):






							"""simple docstring"""






							return True






						@property
						def 	snake_case__						(						self							:					Union[str, Any]						):






							"""simple docstring"""






							return len(self.encoder						)






						def 	snake_case__						(						self							:					str						):






							"""simple docstring"""






							return dict(self.encoder	,     **self.added_tokens_encoder						)






						def 	snake_case__						(						self							:					Optional[Any]	,     __lowercase							:					int						):






							"""simple docstring"""






							snake_case_		   =					tuple(token[:-1]						) + (token[-1] + "</w>",)
							if token in self.cache:
								return self.cache[token]
							snake_case_		   =					get_pairs(__lowercase						)

							if not pairs:
								return token + "</w>"

							while True:
								snake_case_		   =					min(__lowercase	,     key=lambda __lowercase						: self.bpe_ranks.get(__lowercase	,     float("inf"						)						)						)
								if bigram not in self.bpe_ranks:
									break
								snake_case_   ,				snake_case_		   =					bigram
								snake_case_		   =					[]
								snake_case_		   =					0
								while i < len(__lowercase						):
									try:
										snake_case_		   =					word.index(__lowercase	,     __lowercase						)
									except ValueError:
										new_word.extend(word[i:]						)
										break
									else:
										new_word.extend(word[i:j]						)
										snake_case_		   =					j

									if word[i] == first and i < len(__lowercase						) - 1 and word[i + 1] == second:
										new_word.append(first + second						)
										i += 2
									else:
										new_word.append(word[i]						)
										i += 1
								snake_case_		   =					tuple(__lowercase						)
								snake_case_		   =					new_word
								if len(__lowercase						) == 1:
									break
								else:
									snake_case_		   =					get_pairs(__lowercase						)
							snake_case_		   =					" ".join(__lowercase						)
							if word == "\n  </w>":
								snake_case_		   =					"\n</w>"
							snake_case_		   =					word
							return word






						def 	snake_case__						(						self							:					List[str]	,     __lowercase							:					Tuple						):






							"""simple docstring"""






							snake_case_		   =					[]
							if self.fix_text is None:
								# Using BERT's BasicTokenizer
								snake_case_		   =					self.nlp.tokenize(__lowercase						)
								for token in text:
									split_tokens.extend(list(self.bpe(__lowercase						).split(" "						)						)						)
							else:
								# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
								snake_case_		   =					self.nlp(text_standardize(self.fix_text(__lowercase						)						)						)
								for token in text:
									split_tokens.extend(list(self.bpe(token.text.lower()						).split(" "						)						)						)
							return split_tokens






						def 	snake_case__						(						self							:					int	,     __lowercase							:					Tuple						):






							"""simple docstring"""






							return self.encoder.get(__lowercase	,     self.encoder.get(self.unk_token						)						)






						def 	snake_case__						(						self							:					Tuple	,     __lowercase							:					Any						):






							"""simple docstring"""






							return self.decoder.get(__lowercase	,     self.unk_token						)






						def 	snake_case__						(						self							:					str	,     __lowercase							:					List[Any]						):






							"""simple docstring"""






							snake_case_		   =					"".join(__lowercase						).replace("</w>"	,     " "						).strip()
							return out_string






						def 	snake_case__						(						self							:					int	,     __lowercase							:					str	,     __lowercase							:					Optional[str] = None						):






							"""simple docstring"""






							if not os.path.isdir(__lowercase						):
								logger.error(f"Vocabulary path ({save_directory}) should be a directory"						)
								return
							snake_case_		   =					os.path.join(
							    __lowercase	,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)
							snake_case_		   =					os.path.join(
							    __lowercase	,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]						)

							with open(__lowercase	,     "w"	,     encoding="utf-8"						) as f:
								f.write(json.dumps(self.encoder	,     indent=2	,     sort_keys=__lowercase	,     ensure_ascii=__lowercase						) + "\n"						)

							snake_case_		   =					0
							with open(__lowercase	,     "w"	,     encoding="utf-8"						) as writer:
								writer.write("#version: 0.2\n"						)
								for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	,     key=lambda __lowercase						: kv[1]						):
									if index != token_index:
										logger.warning(
										    f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
										    " Please check that the tokenizer is not corrupted!"						)
										snake_case_		   =					token_index
									writer.write(" ".join(__lowercase						) + "\n"						)
									index += 1

							return vocab_file, merge_file
