






import datasets

from .evaluate import evaluate


__snake_case    :    str         =     """\
@article{hendrycks2021cuad,
      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
      journal={arXiv preprint arXiv:2103.06268},
      year={2021}
}
"""

__snake_case    :    List[Any]         =     """
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
"""

__snake_case    :    str         =     """
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair as given in the references (see below)
        - 'prediction_text': list of possible texts for the answer, as a list of strings
        depending on a threshold on the confidence probability of each prediction.
    references: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair (see above),
        - 'answers': a Dict in the CUAD dataset format
            {
                'text': list of possible texts for the answer, as a list of strings
                'answer_start': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    'exact_match': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
    'aupr': Area Under the Precision-Recall curve
    'prec_at_80_recall': Precision at 80% recall
    'prec_at_90_recall': Precision at 90% recall
Examples:
    >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
    >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
    >>> cuad_metric = datasets.load_metric(\"cuad\")
    >>> results = cuad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}
"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,					_KWARGS_DESCRIPTION)
class     __SCREAMING_SNAKE_CASE					(   datasets.Metric):






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return datasets.MetricInfo(
												    description=_DESCRIPTION     ,						citation=_CITATION     ,						inputs_description=_KWARGS_DESCRIPTION     ,						features=datasets.Features(
												        {
												            'predictions': {
												                'id': datasets.Value('string'   ),
												                'prediction_text': datasets.features.Sequence(datasets.Value('string'   )   ),
												            },
												            'references': {
												                'id': datasets.Value('string'   ),
												                'answers': datasets.features.Sequence(
												                    {
												                        'text': datasets.Value('string'   ),
												                        'answer_start': datasets.Value('int32'   ),
												                    }   ),
												            },
												        }   )     ,						codebase_urls=['https://www.atticusprojectai.org/cuad']     ,						reference_urls=['https://www.atticusprojectai.org/cuad']     ,						)







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						{prediction['id']: prediction['prediction_text'] for prediction in predictions}
												lowerCAmelCase__    						=						[
												    {
												        'paragraphs': [
												            {
												                'qas': [
												                    {
												                        'answers': [{'text': answer_text} for answer_text in ref['answers']['text']],
												                        'id': ref['id'],
												                    }
												                    for ref in references
												                ]
												            }
												        ]
												    }
												]
												lowerCAmelCase__    						=						evaluate(dataset=_UpperCamelCase     ,						predictions=_UpperCamelCase   )
												return score













import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__snake_case    :    List[Any]         =     logging.getLogger(__name__)


@dataclass
class     __SCREAMING_SNAKE_CASE					:
						_SCREAMING_SNAKE_CASE	:       str   =					field(
						    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''})
						_SCREAMING_SNAKE_CASE	:       Optional[str]   =					field(
						    default=__lowercase		,					metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''})
						_SCREAMING_SNAKE_CASE	:       Optional[str]   =					field(
						    default='''NER'''		,					metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''})
						_SCREAMING_SNAKE_CASE	:       Optional[str]   =					field(
						    default=__lowercase		,					metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''})
						_SCREAMING_SNAKE_CASE	:       bool   =					field(default=__lowercase		,					metadata={'''help''': '''Set this flag to use fast tokenization.'''})
						# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
						# or just modify its tokenizer_config.json.
						_SCREAMING_SNAKE_CASE	:       Optional[str]   =					field(
						    default=__lowercase		,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}		,					)




@dataclass
class     __SCREAMING_SNAKE_CASE					:
						_SCREAMING_SNAKE_CASE	:       str   =					field(
						    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''})
						_SCREAMING_SNAKE_CASE	:       Optional[str]   =					field(
						    default=__lowercase		,					metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}		,					)
						_SCREAMING_SNAKE_CASE	:       int   =					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	:       bool   =					field(
						    default=__lowercase		,					metadata={'''help''': '''Overwrite the cached training and evaluation sets'''})







def 	_UpperCamelCase   (    )  ->   List[str]:

						"""simple docstring"""



						lowerCAmelCase__    						=						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
						if len(sys.argv		) == 2 and sys.argv[1].endswith('.json'		):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
						else:
												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						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.'		)

						lowerCAmelCase__    						=						import_module('tasks'		)
						try:
												lowerCAmelCase__    						=						getattr(UpperCamelCase_    ,       model_args.task_type		)
												lowerCAmelCase__    						=						token_classification_task_clazz()
						except AttributeError:
												raise ValueError(
												    F"Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
												    F"Available tasks classes are: {TokenClassificationTask.__subclasses__()}"		)

						# 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		)

						# Prepare CONLL-2003 task
						lowerCAmelCase__    						=						token_classification_task.get_labels(data_args.labels		)
						lowerCAmelCase__    						=						dict(enumerate(UpperCamelCase_		)		)
						lowerCAmelCase__    						=						len(UpperCamelCase_		)

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

						lowerCAmelCase__    						=						AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,       num_labels=UpperCamelCase_    ,       idalabel=UpperCamelCase_    ,       labelaid={label: i for i, label in enumerate(UpperCamelCase_		)}    ,       cache_dir=model_args.cache_dir    ,       )
						lowerCAmelCase__    						=						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    ,       use_fast=model_args.use_fast    ,       )
						lowerCAmelCase__    						=						AutoModelForTokenClassification.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
						lowerCAmelCase__    						=						(
						    TokenClassificationDataset(
						        token_classification_task=UpperCamelCase_    ,       data_dir=data_args.data_dir    ,       tokenizer=UpperCamelCase_    ,       labels=UpperCamelCase_    ,       model_type=config.model_type    ,       max_seq_length=data_args.max_seq_length    ,       overwrite_cache=data_args.overwrite_cache    ,       mode=Split.train    ,       )
						    if training_args.do_train
						    else None
						)
						lowerCAmelCase__    						=						(
						    TokenClassificationDataset(
						        token_classification_task=UpperCamelCase_    ,       data_dir=data_args.data_dir    ,       tokenizer=UpperCamelCase_    ,       labels=UpperCamelCase_    ,       model_type=config.model_type    ,       max_seq_length=data_args.max_seq_length    ,       overwrite_cache=data_args.overwrite_cache    ,       mode=Split.dev    ,       )
						    if training_args.do_eval
						    else None
						)

						def align_predictions(UpperCamelCase_       :					np.ndarray    ,       UpperCamelCase_       :					np.ndarray		) -> Tuple[List[int], List[int]]:
												lowerCAmelCase__    						=						np.argmax(UpperCamelCase_    ,       axis=2		)

												lowerCAmelCase__						,       lowerCAmelCase__    						=						preds.shape

												lowerCAmelCase__    						=						[[] for _ in range(UpperCamelCase_		)]
												lowerCAmelCase__    						=						[[] for _ in range(UpperCamelCase_		)]

												for i in range(UpperCamelCase_		):
																		for j in range(UpperCamelCase_		):
																								if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																														out_label_list[i].append(label_map[label_ids[i][j]]		)
																														preds_list[i].append(label_map[preds[i][j]]		)

												return preds_list, out_label_list

						def compute_metrics(UpperCamelCase_       :					EvalPrediction		) -> Dict:
												lowerCAmelCase__						,       lowerCAmelCase__    						=						align_predictions(p.predictions    ,       p.label_ids		)
												return {
												    "accuracy_score": accuracy_score(UpperCamelCase_    ,       UpperCamelCase_		),
												    "precision": precision_score(UpperCamelCase_    ,       UpperCamelCase_		),
												    "recall": recall_score(UpperCamelCase_    ,       UpperCamelCase_		),
												    "f1": fa_score(UpperCamelCase_    ,       UpperCamelCase_		),
												}

						# Data collator
						lowerCAmelCase__    						=						DataCollatorWithPadding(UpperCamelCase_    ,       pad_to_multiple_of=8		) if training_args.fpaa else None

						# Initialize our Trainer
						lowerCAmelCase__    						=						Trainer(
						    model=UpperCamelCase_    ,       args=UpperCamelCase_    ,       train_dataset=UpperCamelCase_    ,       eval_dataset=UpperCamelCase_    ,       compute_metrics=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_process_zero():
																		tokenizer.save_pretrained(training_args.output_dir		)

    # Evaluation
						lowerCAmelCase__    						=						{}
						if training_args.do_eval:
												logger.info('*** Evaluate ***'		)

												lowerCAmelCase__    						=						trainer.evaluate()

												lowerCAmelCase__    						=						os.path.join(training_args.output_dir    ,       'eval_results.txt'		)
												if trainer.is_world_process_zero():
																		with open(UpperCamelCase_    ,       'w'		) as writer:
																								logger.info('***** Eval results *****'		)
																								for key, value in result.items():
																														logger.info('  %s = %s'    ,       UpperCamelCase_    ,       UpperCamelCase_		)
																														writer.write('%s = %s\n' % (key, value)		)

																		results.update(UpperCamelCase_		)

    # Predict
						if training_args.do_predict:
												lowerCAmelCase__    						=						TokenClassificationDataset(
												    token_classification_task=UpperCamelCase_    ,       data_dir=data_args.data_dir    ,       tokenizer=UpperCamelCase_    ,       labels=UpperCamelCase_    ,       model_type=config.model_type    ,       max_seq_length=data_args.max_seq_length    ,       overwrite_cache=data_args.overwrite_cache    ,       mode=Split.test    ,       )

												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						trainer.predict(UpperCamelCase_		)
												lowerCAmelCase__						,       lowerCAmelCase__    						=						align_predictions(UpperCamelCase_    ,       UpperCamelCase_		)

												lowerCAmelCase__    						=						os.path.join(training_args.output_dir    ,       'test_results.txt'		)
												if trainer.is_world_process_zero():
																		with open(UpperCamelCase_    ,       'w'		) as writer:
																								for key, value in metrics.items():
																														logger.info('  %s = %s'    ,       UpperCamelCase_    ,       UpperCamelCase_		)
																														writer.write('%s = %s\n' % (key, value)		)

        # Save predictions
												lowerCAmelCase__    						=						os.path.join(training_args.output_dir    ,       'test_predictions.txt'		)
												if trainer.is_world_process_zero():
																		with open(UpperCamelCase_    ,       'w'		) as writer:
																								with open(os.path.join(data_args.data_dir    ,       'test.txt'		)    ,       'r'		) as f:
																														token_classification_task.write_predictions_to_file(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_		)

						return results







def 	_UpperCamelCase   (    UpperCamelCase_       :					Dict		)  ->   Union[str, Any]:

						"""simple docstring"""



						main()


if __name__ == "__main__":
					main()





