

"""simple docstring"""





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


_SCREAMING_SNAKE_CASE							:	Dict										=			logging.getLogger(__name__)

@dataclass
class      __a :

			"""simple docstring"""

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



@dataclass
class      __a :

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_      =    field(
			    metadata={'help': 'The name of the task to train selected in the list: ' + ', '.join(hans_processors.keys()     )}     )
			SCREAMING_SNAKE_CASE_      =    field(
			    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}     )
			SCREAMING_SNAKE_CASE_      =    field(
			    default=128,     metadata={
			        'help': (
			            'The maximum total input sequence length after tokenization. Sequences longer '
			            'than this will be truncated, sequences shorter will be padded.'
			        )
			    },     )
			SCREAMING_SNAKE_CASE_      =    field(
			    default=snake_case__,     metadata={'help': 'Overwrite the cached training and evaluation sets'}     )



def 							_lowerCAmelCase   (    UpperCAmelCase  :				List[InputFeatures]					):


					'''simple docstring'''






					UpperCamelCase__		:     Union[str, Any]								=default_data_collator(UpperCAmelCase					)
					UpperCamelCase__		:     Union[str, Any]								=batch.pop('''pairID'''   ,				UpperCAmelCase					)
					return batch



def 							_lowerCAmelCase   (    ):


					'''simple docstring'''






					UpperCamelCase__		:     Any								=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
					UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__		:     Any								=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__		:     Tuple								=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__		:     List[str]								=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__		:     Optional[Any]								=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__		:     int								=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__		:     str								=(
					    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__		:     Any								=(
					    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__		:     Optional[int]								=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__		:     Dict								=trainer.predict(UpperCAmelCase					)
										UpperCamelCase__		:     Tuple								=output.predictions
										UpperCamelCase__		:     List[Any]								=np.argmax(UpperCAmelCase   ,				axis=1					)

										UpperCamelCase__		:     Optional[Any]								=[ex.pairID for ex in eval_dataset]
										UpperCamelCase__		:     int								=os.path.join(training_args.output_dir   ,				'''hans_predictions.txt'''					)
										UpperCamelCase__		:     str								=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 							_lowerCAmelCase   (    UpperCAmelCase  :				Any					):


					'''simple docstring'''






					main()


if __name__ == "__main__":
				main()





"""simple docstring"""





import argparse
import json
import re
import string
import sys

import numpy as np


_SCREAMING_SNAKE_CASE							:	str										=			0.5



def 							_lowerCAmelCase   (    UpperCAmelCase  :				str   ,				UpperCAmelCase  :				Optional[int]					):


					'''simple docstring'''






					UpperCamelCase__		:     Optional[Any]								=['''.''', ''',''', ''';''', ''':''']

					for token in remove_tokens:
										UpperCamelCase__		:     Optional[int]								=ground_truth.replace(UpperCAmelCase   ,				''''''					)
										UpperCamelCase__		:     List[Any]								=prediction.replace(UpperCAmelCase   ,				''''''					)

					UpperCamelCase__	,      UpperCamelCase__		:     List[Any]								=ground_truth.lower(), prediction.lower()
					UpperCamelCase__	,      UpperCamelCase__		:     Dict								=ground_truth.replace('''/'''   ,				''' '''					), prediction.replace('''/'''   ,				''' '''					)
					UpperCamelCase__	,      UpperCamelCase__		:     Any								=set(ground_truth.split(''' '''					)					), set(prediction.split(''' '''					)					)

					UpperCamelCase__		:     Optional[Any]								=ground_truth.intersection(UpperCAmelCase					)
					UpperCamelCase__		:     Optional[Any]								=ground_truth.union(UpperCAmelCase					)
					UpperCamelCase__		:     str								=len(UpperCAmelCase					) / len(UpperCAmelCase					)
					return jaccard



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Union[str, Any]					):


					'''simple docstring'''






					def remove_articles(UpperCAmelCase  :				int					):
										return re.sub(r'''\b(a|an|the)\b'''   ,				''' '''   ,				UpperCAmelCase					)

					def white_space_fix(UpperCAmelCase  :				Union[str, Any]					):
										return " ".join(text.split()					)

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

					def lower(UpperCAmelCase  :				Optional[Any]					):
										return text.lower()

					return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase					)					)					)					)



def 							_lowerCAmelCase   (    UpperCAmelCase  :				int   ,				UpperCAmelCase  :				str   ,				UpperCAmelCase  :				Any					):


					'''simple docstring'''






					UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__		:     Optional[Any]								=0, 0, 0

					UpperCamelCase__		:     int								='''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__		:     Union[str, Any]								=False
															for pred in predictions:
																				if substr_ok:
																									UpperCamelCase__		:     List[str]								=get_jaccard(UpperCAmelCase   ,				UpperCAmelCase					) >= IOU_THRESH or ground_truth in pred
																				else:
																									UpperCamelCase__		:     Union[str, Any]								=get_jaccard(UpperCAmelCase   ,				UpperCAmelCase					) >= IOU_THRESH
																				if is_match:
																									UpperCamelCase__		:     Dict								=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__		:     Any								=False
															for ground_truth in ground_truths:
																				assert len(UpperCAmelCase					) > 0
																				if substr_ok:
																									UpperCamelCase__		:     Union[str, Any]								=get_jaccard(UpperCAmelCase   ,				UpperCAmelCase					) >= IOU_THRESH or ground_truth in pred
																				else:
																									UpperCamelCase__		:     Dict								=get_jaccard(UpperCAmelCase   ,				UpperCAmelCase					) >= IOU_THRESH
																				if is_match:
																									UpperCamelCase__		:     str								=True

															if not match_found:
																				fp += 1

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

					return precision, recall



def 							_lowerCAmelCase   (    UpperCAmelCase  :				List[Any]					):


					'''simple docstring'''






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



def 							_lowerCAmelCase   (    UpperCAmelCase  :				List[str]   ,				UpperCAmelCase  :				Any					):


					'''simple docstring'''






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



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Dict   ,				UpperCAmelCase  :				int   ,				UpperCAmelCase  :				int					):


					'''simple docstring'''






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



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Any   ,				UpperCAmelCase  :				Dict					):


					'''simple docstring'''






					return normalize_answer(UpperCAmelCase					) == normalize_answer(UpperCAmelCase					)



def 							_lowerCAmelCase   (    UpperCAmelCase  :				int   ,				UpperCAmelCase  :				Dict   ,				UpperCAmelCase  :				int					):


					'''simple docstring'''






					UpperCamelCase__		:     List[str]								=0
					for pred in predictions:
										for ground_truth in ground_truths:
															UpperCamelCase__		:     str								=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 							_lowerCAmelCase   (    UpperCAmelCase  :				Optional[int]   ,				UpperCAmelCase  :				str					):


					'''simple docstring'''






					UpperCamelCase__		:     Any								=0
					UpperCamelCase__		:     List[Any]								=[]
					UpperCamelCase__		:     Optional[Any]								=[]
					for article in dataset:
										for paragraph in article["paragraphs"]:
															for qa in paragraph["qas"]:
																				total += 1
																				if qa["id"] not in predictions:
																									UpperCamelCase__		:     Union[str, Any]								='''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
																									print(UpperCAmelCase   ,				file=sys.stderr					)
																									continue
																				UpperCamelCase__		:     Union[str, Any]								=[x['''text'''] for x in qa['''answers''']]
																				UpperCamelCase__		:     Any								=predictions[qa['''id''']]
																				UpperCamelCase__	,      UpperCamelCase__		:     int								=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__		:     Union[str, Any]								=[x for _, x in sorted(zip(UpperCAmelCase   ,				UpperCAmelCase					)					)]
					recalls.sort()

					UpperCamelCase__		:     Optional[int]								=100.0 * fa / total
					UpperCamelCase__		:     List[Any]								=100.0 * exact_match / total
					UpperCamelCase__		:     Dict								=get_aupr(UpperCAmelCase   ,				UpperCAmelCase					)

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


