




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()


lowerCamelCase_  						=   logging.getLogger(__name__)
@dataclass
class       __a					:


     """simple docstring"""


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






@dataclass
class       __a					:


     """simple docstring"""


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






def 	UpperCAmelCase_    (	):
    # 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.
    SCREAMING_SNAKE_CASE__								=HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
    SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=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."""	)

    SCREAMING_SNAKE_CASE__								=import_module("""tasks"""	)

    try:
        SCREAMING_SNAKE_CASE__								=getattr(__UpperCamelCase,				model_args.task_type	)
        SCREAMING_SNAKE_CASE__								=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
    SCREAMING_SNAKE_CASE__								=token_classification_task.get_labels(data_args.labels	)
    SCREAMING_SNAKE_CASE__								=dict(enumerate(__UpperCamelCase	)	)
    SCREAMING_SNAKE_CASE__								=len(__UpperCamelCase	)

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

    SCREAMING_SNAKE_CASE__								=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,				)
    SCREAMING_SNAKE_CASE__								=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():
        SCREAMING_SNAKE_CASE__								=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
    SCREAMING_SNAKE_CASE__								=(
        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
    )
    SCREAMING_SNAKE_CASE__								=(
        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,				__UpperCamelCase	) -> Tuple[List[int], List[int]]:
        SCREAMING_SNAKE_CASE__								=np.argmax(__UpperCamelCase,				axis=2	)
        SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=preds.shape
        SCREAMING_SNAKE_CASE__								=[[] for _ in range(__UpperCamelCase	)]
        SCREAMING_SNAKE_CASE__								=[[] 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	) -> Dict:
        SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=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
    SCREAMING_SNAKE_CASE__								=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
    SCREAMING_SNAKE_CASE__								={}
    if training_args.do_eval:
        logger.info("""*** Evaluate ***"""	)

        SCREAMING_SNAKE_CASE__								=trainer.evaluate()
        SCREAMING_SNAKE_CASE__								=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:
        SCREAMING_SNAKE_CASE__								=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,				)

        SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=trainer.predict(test_dataset.get_dataset()	)
        SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=align_predictions(__UpperCamelCase,				__UpperCamelCase	)
        SCREAMING_SNAKE_CASE__								=classification_report(__UpperCamelCase,				__UpperCamelCase	)

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

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

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

        # Save predictions
        SCREAMING_SNAKE_CASE__								=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:
                SCREAMING_SNAKE_CASE__								=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]:
                        SCREAMING_SNAKE_CASE__								=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()












def 	UpperCAmelCase_    (	__UpperCamelCase	):
    if not isinstance(__UpperCamelCase,				__UpperCamelCase	):
        raise ValueError("""double_factorial() only accepts integral values"""	)
    if n < 0:
        raise ValueError("""double_factorial() not defined for negative values"""	)
    return 1 if n <= 1 else n * double_factorial(n - 2	)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






