





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 		=						logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE_  :
						__magic_name__:     str	 =    field(
						    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}   )
						__magic_name__:     Optional[str]	 =    field(
						    default=__lowerCAmelCase    ,			metadata={"help": "Pretrained config name or path if not the same as model_name"}   )
						__magic_name__:     Optional[str]	 =    field(
						    default="NER"    ,			metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}   )
						__magic_name__:     Optional[str]	 =    field(
						    default=__lowerCAmelCase    ,			metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}   )
						__magic_name__:     bool	 =    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.
						__magic_name__:     Optional[str]	 =    field(
						    default=__lowerCAmelCase    ,			metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}    ,			)
@dataclass
class SCREAMING_SNAKE_CASE_  :
						__magic_name__:     str	 =    field(
						    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}   )
						__magic_name__:     Optional[str]	 =    field(
						    default=__lowerCAmelCase    ,			metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}    ,			)
						__magic_name__:     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."
						        )
						    }    ,			)
						__magic_name__:     bool	 =    field(
						    default=__lowerCAmelCase    ,			metadata={"help": "Overwrite the cached training and evaluation sets"}   )


def 							SCREAMING_SNAKE_CASE__  (      ):
	# See all possible arguments in src/transformers/training_args.py
	# or by passing the --help flag to this script.
	# We now keep distinct sets of args, for a cleaner separation of concerns.

	snake_case_						:     Tuple     =		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_  ,snake_case_  ,snake_case_						:     List[str]     =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
	else:
		snake_case_  ,snake_case_  ,snake_case_						:     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.'				)

	snake_case_						:     Tuple     =		import_module('tasks'				)
	try:
		snake_case_						:     Union[str, Any]     =		getattr(snake_case__				,	model_args.task_type				)
		snake_case_						:     str     =		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'				,	snake_case__				)

	# Set seed
	set_seed(training_args.seed				)

	# Prepare CONLL-2003 task
	snake_case_						:     Union[str, Any]     =		token_classification_task.get_labels(data_args.labels				)
	snake_case_						:     Dict     =		dict(enumerate(snake_case__				)				)
	snake_case_						:     Union[str, Any]     =		len(snake_case__				)

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

	snake_case_						:     List[Any]     =		AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path				,	num_labels=snake_case__				,	idalabel=snake_case__				,	labelaid={label: i for i, label in enumerate(snake_case__				)}				,	cache_dir=model_args.cache_dir				,	)
	snake_case_						:     Tuple     =		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_						:     List[Any]     =		AutoModelForTokenClassification.from_pretrained(
	    model_args.model_name_or_path				,	from_tf=bool('.ckpt' in model_args.model_name_or_path				)				,	config=snake_case__				,	cache_dir=model_args.cache_dir				,	)

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

		snake_case_  ,snake_case_						:     str     =		preds.shape

		snake_case_						:     Optional[int]     =		[[] for _ in range(snake_case__				)]
		snake_case_						:     List[str]     =		[[] for _ in range(snake_case__				)]

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

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

	# Initialize our Trainer
	snake_case_						:     List[Any]     =		Trainer(
	    model=snake_case__				,	args=snake_case__				,	train_dataset=snake_case__				,	eval_dataset=snake_case__				,	compute_metrics=snake_case__				,	data_collator=snake_case__				,	)

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

		snake_case_						:     Any     =		trainer.evaluate()

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

			results.update(snake_case__				)

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

		snake_case_  ,snake_case_  ,snake_case_						:     List[str]     =		trainer.predict(snake_case__				)
		snake_case_  ,snake_case_						:     Optional[Any]     =		align_predictions(snake_case__				,	snake_case__				)

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

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

	return results


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
						main()









import warnings

from ..trainer import Trainer
from ..utils import logging


_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):






						def __init__( self : Any						,				_A : str=None						,				**_A : Union[str, Any]		)							->      Any:







							"""simple docstring"""


							warnings.warn(
							    '`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '
							    'instead.'						,				_A						,				)
							super().__init__(args=_A						,				**_A		)

