





'''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


snake_case_     =	logging.getLogger(__name__)


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







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







def 							_lowerCamelCase(  )		->  Union[str, Any]:
    # 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.

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

    A       :       Optional[Any]							=						import_module('''tasks'''		)
    try:
        A       :       Any							=						getattr(UpperCamelCase__    ,       model_args.task_type		)
        A       :       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
    A       :       Optional[Any]							=						token_classification_task.get_labels(data_args.labels		)
    A       :       Dict[int, str]							=						dict(enumerate(UpperCamelCase__		)		)
    A       :       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.

    A       :       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    ,       )
    A       :       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    ,       )
    A       :       List[Any]							=						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
    A       :       Dict							=						(
        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
    )
    A       :       Dict							=						(
        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__    : np.ndarray    ,       UpperCamelCase__    : np.ndarray		) -> Tuple[List[int], List[int]]:
        A       :       int							=						np.argmax(UpperCamelCase__    ,       axis=2		)

        A,	A       :       Any							=						preds.shape

        A       :       Dict							=						[[] for _ in range(UpperCamelCase__		)]
        A       :       Optional[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__    : EvalPrediction		) -> Dict:
        A,	A       :       Tuple							=						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
    A       :       Union[str, Any]							=						DataCollatorWithPadding(UpperCamelCase__    ,       pad_to_multiple_of=8		) if training_args.fpaa else None

    # Initialize our Trainer
    A       :       Optional[int]							=						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
    A       :       Any							=						{}
    if training_args.do_eval:
        logger.info('''*** Evaluate ***'''		)

        A       :       int							=						trainer.evaluate()

        A       :       Union[str, Any]							=						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:
        A       :       Dict							=						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    ,       )

        A,	A,	A       :       Any							=						trainer.predict(UpperCamelCase__		)
        A,	A       :       Optional[int]							=						align_predictions(UpperCamelCase__    ,       UpperCamelCase__		)

        A       :       Optional[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
        A       :       Union[str, Any]							=						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 							_lowerCamelCase(  UpperCamelCase__    : str		)		->  Tuple:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
     main()







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_     =	{
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_     =	[
         """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """BioGptForCausalLM""",
         """BioGptForTokenClassification""",
         """BioGptForSequenceClassification""",
         """BioGptModel""",
         """BioGptPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

     snake_case_     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
