


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


a : Optional[Any]		=				logging.getLogger(__name__)
@dataclass
class 	lowerCamelCase_       :



					'''simple docstring'''







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




@dataclass
class 	lowerCamelCase_       :



					'''simple docstring'''







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




def 		lowercase_  (     ):


			'''simple docstring'''







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

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

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

			__lowercase 					=   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       ,			)
			__lowercase 					=   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       ,			)
			__lowercase 					=   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
			__lowercase 					=   (
			    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
			)
			__lowercase 					=   (
			    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]]:
						__lowercase 					=   np.argmax(_UpperCamelCase       ,			axis=2  )

						__lowercase					, __lowercase 					=   preds.shape

						__lowercase 					=   [[] for _ in range(_UpperCamelCase  )]
						__lowercase 					=   [[] 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:
						__lowercase					, __lowercase 					=   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
			__lowercase 					=   DataCollatorWithPadding(_UpperCamelCase       ,			pad_to_multiple_of=8  ) if training_args.fpaa else None

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

						__lowercase 					=   trainer.evaluate()

						__lowercase 					=   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:
						__lowercase 					=   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       ,			)

						__lowercase					, __lowercase					, __lowercase 					=   trainer.predict(_UpperCamelCase  )
						__lowercase					, __lowercase 					=   align_predictions(_UpperCamelCase       ,			_UpperCamelCase  )

						__lowercase 					=   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
						__lowercase 					=   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_  (     _UpperCamelCase  ):


			'''simple docstring'''







			main()


if __name__ == "__main__":
	main()






a : Any		=				'''
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

a : Union[str, Any]		=				[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
a : Union[str, Any]		=				{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}


