






"""simple docstring"""
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


lowerCAmelCase__    						=			logging.getLogger(__name__)


@dataclass
class _lowerCAmelCase						:
						SCREAMING_SNAKE_CASE_:					Any 					=				field(
						    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}       )
						SCREAMING_SNAKE_CASE_:					int 					=				field(
						    default=_snake_case   ,  metadata={'help': 'Pretrained config name or path if not the same as model_name'}       )
						SCREAMING_SNAKE_CASE_:					Union[str, Any] 					=				field(
						    default='NER'   ,  metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'}       )
						SCREAMING_SNAKE_CASE_:					Union[str, Any] 					=				field(
						    default=_snake_case   ,  metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}       )
						SCREAMING_SNAKE_CASE_:					List[Any] 					=				field(default=_snake_case   ,  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_:					Union[str, Any] 					=				field(
						    default=_snake_case   ,  metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}   ,  )







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







def 				lowercase__		(					):
				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    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.
								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
				else:
								_SCREAMING_SNAKE_CASE   :		Dict       		=    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.'       )

				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    import_module('tasks'       )
				try:
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    getattr(UpperCAmelCase_,						model_args.task_type       )
								_SCREAMING_SNAKE_CASE   :		TokenClassificationTask       		=    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
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    token_classification_task.get_labels(data_args.labels       )
				_SCREAMING_SNAKE_CASE   :		Dict[int, str]       		=    dict(enumerate(UpperCAmelCase_       )       )
				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    len(UpperCAmelCase_       )

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

				_SCREAMING_SNAKE_CASE   :		List[str]       		=    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,						)
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    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,						)
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    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
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    (
				    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
				)
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    (
				    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(lowerCamelCase,						lowerCamelCase       ) -> Tuple[List[int], List[int]]:
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    np.argmax(UpperCAmelCase_,						axis=2       )

								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    preds.shape

								_SCREAMING_SNAKE_CASE   :		int       		=    [[] for _ in range(UpperCAmelCase_       )]
								_SCREAMING_SNAKE_CASE   :		str       		=    [[] 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(lowerCamelCase       ) -> Dict:
								_SCREAMING_SNAKE_CASE   :		Dict       		=    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
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    DataCollatorWithPadding(UpperCAmelCase_,						pad_to_multiple_of=8       ) if training_args.fpaa else None

				# Initialize our Trainer
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    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
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    {}
				if training_args.do_eval:
								logger.info('*** Evaluate ***'       )

								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    trainer.evaluate()

								_SCREAMING_SNAKE_CASE   :		Tuple       		=    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:
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    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,						)

								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    trainer.predict(UpperCAmelCase_       )
								_SCREAMING_SNAKE_CASE   :		str       		=    align_predictions(UpperCAmelCase_,						UpperCAmelCase_       )

								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    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
								_SCREAMING_SNAKE_CASE   :		int       		=    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 				lowercase__		(					lowerCamelCase       ):
				main()


if __name__ == "__main__":
			main()





import datasets


snake_case							:       int										=	'''\
@InProceedings{conneau2018xnli,
  author = "Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin",
  title = "XNLI: Evaluating Cross-lingual Sentence Representations",
  booktitle = "Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing",
  year = "2018",
  publisher = "Association for Computational Linguistics",
  location = "Brussels, Belgium",
}
'''

snake_case							:       Tuple										=	'''\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
'''

snake_case							:       Union[str, Any]										=	'''
Computes XNLI score which is just simple accuracy.
Args:
    predictions: Predicted labels.
    references: Ground truth labels.
Returns:
    \'accuracy\': accuracy
Examples:

    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> xnli_metric = datasets.load_metric("xnli")
    >>> results = xnli_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}
'''
def 							__lowerCamelCase		(					UpperCAmelCase_		:						List[Any]					,    UpperCAmelCase_		:						List[str]		):




      """simple docstring"""





      return (preds == labels).mean()







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




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return datasets.MetricInfo(
           description=_DESCRIPTION   ,    citation=_CITATION   ,    inputs_description=_KWARGS_DESCRIPTION   ,    features=datasets.Features(
               {
                   '''predictions''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32'''       ),
                   '''references''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32'''       ),
               }       )   ,    codebase_urls=[]   ,    reference_urls=[]   ,    format='''numpy'''   ,    )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       return {"accuracy": simple_accuracy(_lowerCamelCase   ,    _lowerCamelCase       )}





