





import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__A    :   List[str]          =       logging.getLogger(__name__)

@dataclass
class      A_	:
	UpperCAmelCase__ =      field(
	    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}      )
	UpperCAmelCase__ =      field(
	    default=a_					,							metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}      )
	UpperCAmelCase__ =      field(
	    default=a_					,							metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}      )
	UpperCAmelCase__ =      field(
	    default=a_					,							metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}					,							)

@dataclass
class      A_	:
	UpperCAmelCase__ =      field(
	    metadata={'''help''': '''The name of the task to train selected in the list: ''' + ''', '''.join(hans_processors.keys()      )}      )
	UpperCAmelCase__ =      field(
	    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}      )
	UpperCAmelCase__ =      field(
	    default=1_2_8					,							metadata={
	        '''help''': (
	            '''The maximum total input sequence length after tokenization. Sequences longer '''
	            '''than this will be truncated, sequences shorter will be padded.'''
	        )
	    }					,							)
	UpperCAmelCase__ =      field(
	    default=a_					,							metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}      )





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Dict[str, torch.Tensor]:







			'''simple docstring'''

			UpperCAmelCase       		=    default_data_collator(UpperCamelCase__			)
			UpperCAmelCase       		=    batch.pop('''pairID'''      ,      UpperCamelCase__			)
			return batch





def 						__SCREAMING_SNAKE_CASE  (    )  ->    Any:







			'''simple docstring'''

			UpperCAmelCase       		=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
			UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase       		=    parser.parse_args_into_dataclasses()

			if (
			    os.path.exists(training_args.output_dir			)
			    and os.listdir(training_args.output_dir			)
			    and training_args.do_train
			    and not training_args.overwrite_output_dir
			):
						raise ValueError(
						    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
						    ''' --overwrite_output_dir to overcome.'''			)

			# Setup logging
			logging.basicConfig(
			    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''      ,      datefmt='''%m/%d/%Y %H:%M:%S'''      ,      level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN      ,      )
			logger.warning(
			    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''      ,      training_args.local_rank      ,      training_args.device      ,      training_args.n_gpu      ,      bool(training_args.local_rank != -1			)      ,      training_args.fpaa      ,      )
			# Set the verbosity to info of the Transformers logger (on main process only):
			if is_main_process(training_args.local_rank			):
						transformers.utils.logging.set_verbosity_info()
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()
			logger.info('''Training/evaluation parameters %s'''      ,      UpperCamelCase__			)

			# Set seed
			set_seed(training_args.seed			)

			try:
						UpperCAmelCase       		=    hans_tasks_num_labels[data_args.task_name]
			except KeyError:
						raise ValueError('''Task not found: %s''' % (data_args.task_name)			)

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.

			UpperCAmelCase       		=    AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,      num_labels=UpperCamelCase__      ,      finetuning_task=data_args.task_name      ,      cache_dir=model_args.cache_dir      ,      )
			UpperCAmelCase       		=    AutoTokenizer.from_pretrained(
			    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path      ,      cache_dir=model_args.cache_dir      ,      )
			UpperCAmelCase       		=    AutoModelForSequenceClassification.from_pretrained(
			    model_args.model_name_or_path      ,      from_tf=bool('''.ckpt''' in model_args.model_name_or_path			)      ,      config=UpperCamelCase__      ,      cache_dir=model_args.cache_dir      ,      )

			# Get datasets
			UpperCAmelCase       		=    (
			    HansDataset(
			        data_dir=data_args.data_dir      ,      tokenizer=UpperCamelCase__      ,      task=data_args.task_name      ,      max_seq_length=data_args.max_seq_length      ,      overwrite_cache=data_args.overwrite_cache      ,      )
			    if training_args.do_train
			    else None
			)
			UpperCAmelCase       		=    (
			    HansDataset(
			        data_dir=data_args.data_dir      ,      tokenizer=UpperCamelCase__      ,      task=data_args.task_name      ,      max_seq_length=data_args.max_seq_length      ,      overwrite_cache=data_args.overwrite_cache      ,      evaluate=UpperCamelCase__      ,      )
			    if training_args.do_eval
			    else None
			)

			# Initialize our Trainer
			UpperCAmelCase       		=    Trainer(
			    model=UpperCamelCase__      ,      args=UpperCamelCase__      ,      train_dataset=UpperCamelCase__      ,      eval_dataset=UpperCamelCase__      ,      data_collator=UpperCamelCase__      ,      )

			# Training
			if training_args.do_train:
						trainer.train(
						    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
						trainer.save_model()
						# For convenience, we also re-save the tokenizer to the same directory,
						# so that you can share your model easily on huggingface.co/models =)
						if trainer.is_world_master():
									tokenizer.save_pretrained(training_args.output_dir			)

    # Evaluation
			if training_args.do_eval:
						logger.info('''*** Evaluate ***'''			)

						UpperCAmelCase       		=    trainer.predict(UpperCamelCase__			)
						UpperCAmelCase       		=    output.predictions
						UpperCAmelCase       		=    np.argmax(UpperCamelCase__      ,      axis=1			)

						UpperCAmelCase       		=    [ex.pairID for ex in eval_dataset]
						UpperCAmelCase       		=    os.path.join(training_args.output_dir      ,      '''hans_predictions.txt'''			)
						UpperCAmelCase       		=    eval_dataset.get_labels()
						if trainer.is_world_master():
									with open(UpperCamelCase__      ,      '''w'''			) as writer:
												writer.write('''pairID,gold_label\n'''			)
												for pid, pred in zip(UpperCamelCase__      ,      UpperCamelCase__			):
															writer.write('''ex''' + str(UpperCamelCase__			) + ''',''' + label_list[int(UpperCamelCase__			)] + '''\n'''			)

						trainer._log(output.metrics			)





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    int:







			'''simple docstring'''

			main()


if __name__ == "__main__":
				main()










import argparse
import json
import re
import string
import sys

import numpy as np


__A    :   Optional[Any]          =       0.5





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    List[Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    ['''.''', ''',''', ''';''', ''':''']

			for token in remove_tokens:
						UpperCAmelCase       		=    ground_truth.replace(UpperCamelCase__      ,      ''''''			)
						UpperCAmelCase       		=    prediction.replace(UpperCamelCase__      ,      ''''''			)

			UpperCAmelCase		,      UpperCAmelCase       		=    ground_truth.lower(), prediction.lower()
			UpperCAmelCase		,      UpperCAmelCase       		=    ground_truth.replace('''/'''      ,      ''' '''			), prediction.replace('''/'''      ,      ''' '''			)
			UpperCAmelCase		,      UpperCAmelCase       		=    set(ground_truth.split(''' '''			)			), set(prediction.split(''' '''			)			)

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





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Union[str, Any]:







			'''simple docstring'''

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

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

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

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

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





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Optional[int]:







			'''simple docstring'''

			UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase       		=    0, 0, 0

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

									if not match_found:
												fp += 1

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

			return precision, recall





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    List[str]:







			'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    str:







			'''simple docstring'''

			UpperCAmelCase       		=    process_precisions(UpperCamelCase__			)
			UpperCAmelCase       		=    np.trapz(UpperCamelCase__      ,      UpperCamelCase__			)
			if np.isnan(UpperCamelCase__			):
						return 0
			return aupr





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Optional[int]:







			'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    str:







			'''simple docstring'''

			return normalize_answer(UpperCamelCase__			) == normalize_answer(UpperCamelCase__			)





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    str:







			'''simple docstring'''

			UpperCAmelCase       		=    0
			for pred in predictions:
						for ground_truth in ground_truths:
									UpperCAmelCase       		=    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__      ,      UpperCamelCase__			)  ->    int:







			'''simple docstring'''

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

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

			UpperCAmelCase       		=    100.0 * fa / total
			UpperCAmelCase       		=    100.0 * exact_match / total
			UpperCAmelCase       		=    get_aupr(UpperCamelCase__      ,      UpperCamelCase__			)

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



