




"""simple docstring"""





import logging
import os
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 classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


_lowercase						   =     logging.getLogger(__name__)

@dataclass
class    __lowercase							:


				'''simple docstring'''


				_lowerCAmelCase						      =field(
				    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}						)
				_lowerCAmelCase						      =field(
				    default=snake_case_    , 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=snake_case_    , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}						)
				_lowerCAmelCase						      =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.
				_lowerCAmelCase						      =field(
				    default=snake_case_    , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}    , )







@dataclass
class    __lowercase							:


				'''simple docstring'''


				_lowerCAmelCase						      =field(
				    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}						)
				_lowerCAmelCase						      =field(
				    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=snake_case_    , metadata={"help": "Overwrite the cached training and evaluation sets"}						)







def 		lowerCAmelCase				(			):
						'''simple docstring'''


						snake_case__							:       Any				      =       HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)		)
						snake_case__      ,						snake_case__      ,						snake_case__							:       Union[str, 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__							:       Any				      =       import_module('''tasks'''		)

						try:
												snake_case__							:       Union[str, Any]				      =       getattr(lowerCamelCase   ,      model_args.task_type		)
												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   ,      )
						logger.info(
						    '''n_replicas: %s, distributed training: %s, 16-bits training: %s'''   ,      training_args.n_replicas   ,      bool(training_args.n_replicas > 1		)   ,      training_args.fpaa   ,      )
						logger.info('''Training/evaluation parameters %s'''   ,      lowerCamelCase		)

						# Prepare Token Classification task
						snake_case__							:       Union[str, Any]				      =       token_classification_task.get_labels(data_args.labels		)
						snake_case__							:       Dict[int, str]				      =       dict(enumerate(lowerCamelCase		)		)
						snake_case__							:       List[str]				      =       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__							:       Any				      =       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__							:       List[Any]				      =       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   ,      )

						with training_args.strategy.scope():
												snake_case__							:       int				      =       TFAutoModelForTokenClassification.from_pretrained(
												    model_args.model_name_or_path   ,      from_pt=bool('''.bin''' in model_args.model_name_or_path		)   ,      config=lowerCamelCase   ,      cache_dir=model_args.cache_dir   ,      )

						# Get datasets
						snake_case__							:       List[str]				      =       (
						    TFTokenClassificationDataset(
						        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__							:       Tuple				      =       (
						    TFTokenClassificationDataset(
						        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(lowerCamelCase   ,      lowerCamelCase		) -> Tuple[List[int], List[int]]:
												snake_case__							:       Dict				      =       np.argmax(lowerCamelCase   ,      axis=2		)
												snake_case__      ,						snake_case__							:       List[Any]				      =       preds.shape
												snake_case__							:       Union[str, Any]				      =       [[] for _ in range(lowerCamelCase		)]
												snake_case__							:       List[str]				      =       [[] for _ in range(lowerCamelCase		)]

												for i in range(lowerCamelCase		):
																		for j in range(lowerCamelCase		):
																								if label_ids[i, j] != -100:
																														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:
												snake_case__      ,						snake_case__							:       Union[str, Any]				      =       align_predictions(p.predictions   ,      p.label_ids		)

												return {
												    "precision": precision_score(lowerCamelCase   ,      lowerCamelCase		),
												    "recall": recall_score(lowerCamelCase   ,      lowerCamelCase		),
												    "f1": fa_score(lowerCamelCase   ,      lowerCamelCase		),
												}

						# Initialize our Trainer
						snake_case__							:       Any				      =       TFTrainer(
						    model=lowerCamelCase   ,      args=lowerCamelCase   ,      train_dataset=train_dataset.get_dataset() if train_dataset else None   ,      eval_dataset=eval_dataset.get_dataset() if eval_dataset else None   ,      compute_metrics=lowerCamelCase   ,      )

						# Training
						if training_args.do_train:
												trainer.train()
												trainer.save_model()
												tokenizer.save_pretrained(training_args.output_dir		)

						# Evaluation
						snake_case__							:       List[Any]				      =       {}
						if training_args.do_eval:
												logger.info('''*** Evaluate ***'''		)

												snake_case__							:       Dict				      =       trainer.evaluate()
												snake_case__							:       int				      =       os.path.join(training_args.output_dir   ,      '''eval_results.txt'''		)

												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__							:       List[Any]				      =       TFTokenClassificationDataset(
												    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__      ,						snake_case__      ,						snake_case__							:       List[Any]				      =       trainer.predict(test_dataset.get_dataset()		)
												snake_case__      ,						snake_case__							:       Union[str, Any]				      =       align_predictions(lowerCamelCase   ,      lowerCamelCase		)
												snake_case__							:       int				      =       classification_report(lowerCamelCase   ,      lowerCamelCase		)

												logger.info('''\n%s'''   ,      lowerCamelCase		)

												snake_case__							:       str				      =       os.path.join(training_args.output_dir   ,      '''test_results.txt'''		)

												with open(lowerCamelCase   ,      '''w'''		) as writer:
																		writer.write('''%s\n''' % report		)

												# Save predictions
												snake_case__							:       Optional[Any]				      =       os.path.join(training_args.output_dir   ,      '''test_predictions.txt'''		)

												with open(lowerCamelCase   ,      '''w'''		) as writer:
																		with open(os.path.join(data_args.data_dir   ,      '''test.txt'''		)   ,      '''r'''		) as f:
																								snake_case__							:       str				      =       0

																								for line in f:
																														if line.startswith('''-DOCSTART-'''		) or line == "" or line == "\n":
																																				writer.write(lowerCamelCase		)

																																				if not preds_list[example_id]:
																																										example_id += 1
																														elif preds_list[example_id]:
																																				snake_case__							:       int				      =       line.split()[0] + ''' ''' + preds_list[example_id].pop(0		) + '''\n'''

																																				writer.write(lowerCamelCase		)
																														else:
																																				logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.'''   ,      line.split()[0]		)

						return results


if __name__ == "__main__":
							main()








"""simple docstring"""





_lowercase						   =     [0.4_85, 0.4_56, 0.4_06]
_lowercase						   =     [0.2_29, 0.2_24, 0.2_25]
_lowercase						   =     [0.5, 0.5, 0.5]
_lowercase						   =     [0.5, 0.5, 0.5]
_lowercase						   =     [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
_lowercase						   =     [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]


