


"""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


UpperCamelCase						:				Any             =      logging.getLogger(__name__)



@dataclass
class 			lowerCamelCase__						:
						lowerCAmelCase       =     field(
						    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}			)
						lowerCAmelCase       =     field(
						    default=UpperCAmelCase_							,    metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}			)
						lowerCAmelCase       =     field(
						    default="""NER"""							,    metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}			)
						lowerCAmelCase       =     field(
						    default=UpperCAmelCase_							,    metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}			)
						lowerCAmelCase       =     field(default=UpperCAmelCase_							,    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.
						lowerCAmelCase       =     field(
						    default=UpperCAmelCase_							,    metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}							,    )



@dataclass
class 			lowerCamelCase__						:
						lowerCAmelCase       =     field(
						    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}			)
						lowerCAmelCase       =     field(
						    default=UpperCAmelCase_							,    metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}							,    )
						lowerCAmelCase       =     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."""
						        )
						    }							,    )
						lowerCAmelCase       =     field(
						    default=UpperCAmelCase_							,    metadata={"""help""": """Overwrite the cached training and evaluation sets"""}			)







def  __snake_case       (   )      ->		Tuple:







		"""simple docstring"""





		A					       = 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.
				A       , A       , A					       = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
		else:
				A       , A       , A					       = 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.'	)

		A					       = import_module('tasks'	)
		try:
				A					       = getattr(UpperCamelCase__ ,			model_args.task_type	)
				A					       = 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
		A					       = token_classification_task.get_labels(data_args.labels	)
		A					       = dict(enumerate(UpperCamelCase__	)	)
		A					       = len(UpperCamelCase__	)

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

		A					       = 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 ,			)
		A					       = 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 ,			)
		A					       = 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
		A					       = (
		    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
		)
		A					       = (
		    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__ ,			UpperCamelCase__	) -> Tuple[List[int], List[int]]:
				A					       = np.argmax(UpperCamelCase__ ,			axis=2	)

				A       , A					       = preds.shape

				A					       = [[] for _ in range(UpperCamelCase__	)]
				A					       = [[] 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__	) -> Dict:
				A       , A					       = 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
		A					       = DataCollatorWithPadding(UpperCamelCase__ ,			pad_to_multiple_of=8	) if training_args.fpaa else None

		# Initialize our Trainer
		A					       = 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
		A					       = {}
		if training_args.do_eval:
				logger.info('*** Evaluate ***'	)

				A					       = trainer.evaluate()

				A					       = 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:
				A					       = 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 ,			)

				A       , A       , A					       = trainer.predict(UpperCamelCase__	)
				A       , A					       = align_predictions(UpperCamelCase__ ,			UpperCamelCase__	)

				A					       = 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
				A					       = 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  __snake_case       (   UpperCamelCase__	)      ->		str:







		"""simple docstring"""





		main()


if __name__ == "__main__":
		main()









"""simple docstring"""


def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ = " "	)      ->		list:







		"""simple docstring"""





		A					       = []

		A					       = 0
		for index, char in enumerate(UpperCamelCase__	):
				if char == separator:
						split_words.append(string[last_index:index]	)
						A					       = index + 1
				elif index + 1 == len(UpperCamelCase__	):
						split_words.append(string[last_index : index + 1]	)
		return split_words


if __name__ == "__main__":
	from doctest import testmod

	testmod()





