


"""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


__SCREAMING_SNAKE_CASE							:      int						=      logging.getLogger(__name__)





@dataclass
class        __A       :





							'''simple docstring'''

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





@dataclass
class        __A       :





							'''simple docstring'''

							__lowercase:						Any							 =      field(
							    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""})
							__lowercase:						int							 =      field(
							    default=lowerCamelCase__  ,						metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}  ,						)
							__lowercase:						Optional[int]							 =      field(
							    default=1_28  ,						metadata={
							        """help""": (
							            """The maximum total input sequence length after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded."""
							        )
							    }  ,						)
							__lowercase:						Union[str, Any]							 =      field(
							    default=lowerCamelCase__  ,						metadata={"""help""": """Overwrite the cached training and evaluation sets"""})







def   _A			( )      ->			Any:
							snake_case_          =						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.
														snake_case_          =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
							else:
														snake_case_          =						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."""			)

							snake_case_          =						import_module("""tasks"""			)
							try:
														snake_case_          =						getattr(lowerCamelCase_			,	model_args.task_type			)
														snake_case_          =						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"""			,	lowerCamelCase_			)

							# Set seed
							set_seed(training_args.seed			)

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

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

							snake_case_          =						AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path			,	num_labels=lowerCamelCase_			,	idalabel=lowerCamelCase_			,	labelaid={label: i for i, label in enumerate(lowerCamelCase_			)}			,	cache_dir=model_args.cache_dir			,	)
							snake_case_          =						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			,	)
							snake_case_          =						AutoModelForTokenClassification.from_pretrained(
							    model_args.model_name_or_path			,	from_tf=bool(""".ckpt""" in model_args.model_name_or_path			)			,	config=lowerCamelCase_			,	cache_dir=model_args.cache_dir			,	)

							# Get datasets
							snake_case_          =						(
							    TokenClassificationDataset(
							        token_classification_task=lowerCamelCase_			,	data_dir=data_args.data_dir			,	tokenizer=lowerCamelCase_			,	labels=lowerCamelCase_			,	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
							)
							snake_case_          =						(
							    TokenClassificationDataset(
							        token_classification_task=lowerCamelCase_			,	data_dir=data_args.data_dir			,	tokenizer=lowerCamelCase_			,	labels=lowerCamelCase_			,	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(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			) -> Tuple[List[int], List[int]]:
														snake_case_          =						np.argmax(lowerCamelCase_			,	axis=2			)

														snake_case_          =						preds.shape

														snake_case_          =						[[] for _ in range(lowerCamelCase_			)]
														snake_case_          =						[[] for _ in range(lowerCamelCase_			)]

														for i in range(lowerCamelCase_			):
																					for j in range(lowerCamelCase_			):
																												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(_SCREAMING_SNAKE_CASE			) -> Dict:
														snake_case_          =						align_predictions(p.predictions			,	p.label_ids			)
														return {
														    "accuracy_score": accuracy_score(lowerCamelCase_			,	lowerCamelCase_			),
														    "precision": precision_score(lowerCamelCase_			,	lowerCamelCase_			),
														    "recall": recall_score(lowerCamelCase_			,	lowerCamelCase_			),
														    "f1": fa_score(lowerCamelCase_			,	lowerCamelCase_			),
														}

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

							# Initialize our Trainer
							snake_case_          =						Trainer(
							    model=lowerCamelCase_			,	args=lowerCamelCase_			,	train_dataset=lowerCamelCase_			,	eval_dataset=lowerCamelCase_			,	compute_metrics=lowerCamelCase_			,	data_collator=lowerCamelCase_			,	)

							# 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
							snake_case_          =						{}
							if training_args.do_eval:
														logger.info("""*** Evaluate ***"""			)

														snake_case_          =						trainer.evaluate()

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

																					results.update(lowerCamelCase_			)

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

														snake_case_          =						trainer.predict(lowerCamelCase_			)
														snake_case_          =						align_predictions(lowerCamelCase_			,	lowerCamelCase_			)

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

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

							return results







def   _A			( _SCREAMING_SNAKE_CASE			)      ->			Tuple:
							main()


if __name__ == "__main__":
				main()







"""simple docstring"""
def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE = 0			)      ->			list:
							snake_case_          =						length or len(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						False
							for i in range(length - 1			):
														if list_data[i] > list_data[i + 1]:
																					snake_case_    ,  snake_case_          =						list_data[i + 1], list_data[i]
																					snake_case_          =						True

							return list_data if not swapped else bubble_sort(_SCREAMING_SNAKE_CASE			,	length - 1			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()



