




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()


SCREAMING_SNAKE_CASE_						:					Union[str, Any]			   =       logging.getLogger(__name__)







@dataclass
class 		__UpperCAmelCase:





     """simple docstring"""



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







@dataclass
class 		__UpperCAmelCase:





     """simple docstring"""



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






def        __lowerCAmelCase ():
    # 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__      =       HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)		)
    a__ ,		a__ ,		a__      =       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__      =       import_module("""tasks"""		)

    try:
        a__      =       getattr(UpperCAmelCase_   ,    model_args.task_type		)
        a__      =       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"""   ,    UpperCAmelCase_		)

    # Prepare Token Classification task
    a__      =       token_classification_task.get_labels(data_args.labels		)
    a__      =       dict(enumerate(UpperCAmelCase_		)		)
    a__      =       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__      =       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__      =       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():
        a__      =       TFAutoModelForTokenClassification.from_pretrained(
            model_args.model_name_or_path   ,    from_pt=bool(""".bin""" in model_args.model_name_or_path		)   ,    config=UpperCAmelCase_   ,    cache_dir=model_args.cache_dir   ,    )

    # Get datasets
    a__      =       (
        TFTokenClassificationDataset(
            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__      =       (
        TFTokenClassificationDataset(
            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_     :   Optional[Any]   ,    UpperCAmelCase_     :   List[Any]		) -> Tuple[List[int], List[int]]:
        a__      =       np.argmax(UpperCAmelCase_   ,    axis=2		)
        a__ ,		a__      =       preds.shape
        a__      =       [[] for _ in range(UpperCAmelCase_		)]
        a__      =       [[] for _ in range(UpperCAmelCase_		)]

        for i in range(UpperCAmelCase_		):
            for j in range(UpperCAmelCase_		):
                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(UpperCAmelCase_     :   Optional[int]		) -> Dict:
        a__ ,		a__      =       align_predictions(p.predictions   ,    p.label_ids		)

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

    # Initialize our Trainer
    a__      =       TFTrainer(
        model=UpperCAmelCase_   ,    args=UpperCAmelCase_   ,    train_dataset=train_dataset.get_dataset() if train_dataset else None   ,    eval_dataset=eval_dataset.get_dataset() if eval_dataset else None   ,    compute_metrics=UpperCAmelCase_   ,    )

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

    # Evaluation
    a__      =       {}
    if training_args.do_eval:
        logger.info("""*** Evaluate ***"""		)

        a__      =       trainer.evaluate()
        a__      =       os.path.join(training_args.output_dir   ,    """eval_results.txt"""		)

        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__      =       TFTokenClassificationDataset(
            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__      =       trainer.predict(test_dataset.get_dataset()		)
        a__ ,		a__      =       align_predictions(UpperCAmelCase_   ,    UpperCAmelCase_		)
        a__      =       classification_report(UpperCAmelCase_   ,    UpperCAmelCase_		)

        logger.info("""\n%s"""   ,    UpperCAmelCase_		)

        a__      =       os.path.join(training_args.output_dir   ,    """test_results.txt"""		)

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

        # Save predictions
        a__      =       os.path.join(training_args.output_dir   ,    """test_predictions.txt"""		)

        with open(UpperCAmelCase_   ,    """w"""		) as writer:
            with open(os.path.join(data_args.data_dir   ,    """test.txt"""		)   ,    """r"""		) as f:
                a__      =       0

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

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

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

    return results


if __name__ == "__main__":
 main()












from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_						:					List[str]			   =       {'''configuration_vit_hybrid''': ['''VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTHybridConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_						:					Tuple			   =       [
     '''VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''ViTHybridForImageClassification''',
     '''ViTHybridModel''',
     '''ViTHybridPreTrainedModel''',
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_						:					int			   =       ['''ViTHybridImageProcessor''']


if TYPE_CHECKING:
 from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit_hybrid import (
      VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTHybridForImageClassification,
      ViTHybridModel,
      ViTHybridPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
 import sys

 SCREAMING_SNAKE_CASE_						:					Any			   =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






