




"""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


_A													=      logging.getLogger(__name__)


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






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






def 	lowercase_				(			)						->    List[str]:
      # 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.

      lowerCAmelCase__      :			str   =	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.
            lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__      :			List[Any]   =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
      else:
            lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__      :			Optional[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."""				)

      lowerCAmelCase__      :			Tuple   =	import_module("""tasks"""				)
      try:
            lowerCAmelCase__      :			Optional[int]   =	getattr(__UpperCAmelCase    ,		model_args.task_type				)
            lowerCAmelCase__      :			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 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
      lowerCAmelCase__      :			Any   =	token_classification_task.get_labels(data_args.labels				)
      lowerCAmelCase__      :			Dict[int, str]   =	dict(enumerate(__UpperCAmelCase				)				)
      lowerCAmelCase__      :			Optional[Any]   =	len(__UpperCAmelCase				)

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

      lowerCAmelCase__      :			Optional[Any]   =	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    ,		)
      lowerCAmelCase__      :			Optional[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    ,		)
      lowerCAmelCase__      :			Optional[int]   =	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
      lowerCAmelCase__      :			Tuple   =	(
          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
      )
      lowerCAmelCase__      :			List[Any]   =	(
          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]]:
            lowerCAmelCase__      :			Optional[int]   =	np.argmax(__UpperCAmelCase    ,		axis=2				)

            lowerCAmelCase__ ,   lowerCAmelCase__      :			Tuple   =	preds.shape

            lowerCAmelCase__      :			Union[str, Any]   =	[[] for _ in range(__UpperCAmelCase				)]
            lowerCAmelCase__      :			int   =	[[] 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:
            lowerCAmelCase__ ,   lowerCAmelCase__      :			str   =	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
      lowerCAmelCase__      :			List[str]   =	DataCollatorWithPadding(__UpperCAmelCase    ,		pad_to_multiple_of=8				) if training_args.fpaa else None

      # Initialize our Trainer
      lowerCAmelCase__      :			List[Any]   =	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
      lowerCAmelCase__      :			List[Any]   =	{}
      if training_args.do_eval:
            logger.info("""*** Evaluate ***"""				)

            lowerCAmelCase__      :			List[str]   =	trainer.evaluate()

            lowerCAmelCase__      :			List[str]   =	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:
            lowerCAmelCase__      :			List[str]   =	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    ,		)

            lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__      :			List[str]   =	trainer.predict(__UpperCAmelCase				)
            lowerCAmelCase__ ,   lowerCAmelCase__      :			List[Any]   =	align_predictions(__UpperCAmelCase    ,		__UpperCAmelCase				)

            lowerCAmelCase__      :			List[Any]   =	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
            lowerCAmelCase__      :			Tuple   =	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				)						->    str:
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
  main()











"""simple docstring"""


from argparse import ArgumentParser

from . import BaseTransformersCLICommand



def 	lowercase_				(			__UpperCAmelCase				)						->    List[str]:
      return DownloadCommand(args.model    ,		args.cache_dir    ,		args.force    ,		args.trust_remote_code				)


class _lowerCamelCase    (					a_						):






   @staticmethod
   def 	_lowerCAmelCase   (		UpperCamelCase       :		ArgumentParser   )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			int   =	parser.add_parser("""download"""   )
         download_parser.add_argument(
             """--cache-dir"""   ,     type=UpperCamelCase   ,     default=UpperCamelCase   ,     help="""Path to location to store the models"""   )
         download_parser.add_argument(
             """--force"""   ,     action="""store_true"""   ,     help="""Force the model to be download even if already in cache-dir"""   )
         download_parser.add_argument(
             """--trust-remote-code"""   ,     action="""store_true"""   ,     help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""   ,     )
         download_parser.add_argument("""model"""   ,     type=UpperCamelCase   ,     help="""Name of the model to download"""   )
         download_parser.set_defaults(func=UpperCamelCase   )






   def __init__(		self       :		Optional[int]   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		bool   ,     UpperCamelCase       :		bool   )			->						Any:

         """simple docstring"""
         lowerCAmelCase__      :			int   =	model
         lowerCAmelCase__      :			Union[str, Any]   =	cache
         lowerCAmelCase__      :			Optional[int]   =	force
         lowerCAmelCase__      :			Dict   =	trust_remote_code






   def 	_lowerCAmelCase   (		self       :		int   )			->						Any:

         """simple docstring"""
         from ..models.auto import AutoModel, AutoTokenizer

         AutoModel.from_pretrained(
             self._model   ,     cache_dir=self._cache   ,     force_download=self._force   ,     trust_remote_code=self._trust_remote_code   )
         AutoTokenizer.from_pretrained(
             self._model   ,     cache_dir=self._cache   ,     force_download=self._force   ,     trust_remote_code=self._trust_remote_code   )





