from typing import Any







class      SCREAMING_SNAKE_CASE__					:

       def __init__(							self					:     Dict			):
           __snake_case       :				Union[str, Any] 			=    []
           __snake_case       :				int 			=    0

       def __str__(							self					:     Any			):
           __snake_case       :				Optional[int] 			=    """<""" + str(self.stack			)[1:-1] + """>"""
           return printed

       def 			snake_case__    (							self					:     List[Any]			,			_lowerCAmelCase					:     Any			):
           self.stack.append(_lowerCAmelCase			)
           __snake_case       :				str 			=    self.length + 1

       def 			snake_case__    (							self					:     Tuple			):
           self.rotate(1			)
           __snake_case       :				List[str] 			=    self.stack[self.length - 1]
           __snake_case       :				List[Any] 			=    self.stack[:-1]
           self.rotate(self.length - 1			)
           __snake_case       :				Union[str, Any] 			=    self.length - 1
           return dequeued

       def 			snake_case__    (							self					:     List[Any]			,			_lowerCAmelCase					:     int			):
           for _ in range(_lowerCAmelCase			):
               __snake_case       :				List[str] 			=    self.stack[0]
               __snake_case       :				List[Any] 			=    self.stack[1:]
               self.put(_lowerCAmelCase			)
               __snake_case       :				Tuple 			=    self.length - 1

       def 			snake_case__    (							self					:     Union[str, Any]			):
           __snake_case       :				Tuple 			=    self.get()
           self.put(_lowerCAmelCase			)
           self.rotate(self.length - 1			)
           return front

       def 			snake_case__    (							self					:     str			):
           return self.length




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







@dataclass
class      SCREAMING_SNAKE_CASE__					:
       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=128   ,							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=__UpperCamelCase   ,							metadata={"help": "Overwrite the cached training and evaluation sets"}    )







def 					__lowerCAmelCase				(		):







    '''simple docstring'''
    # 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.
    __snake_case       :				Tuple 			=    HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)   )
    __snake_case			, __snake_case			, __snake_case       :				Tuple 			=    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       :				int 			=    import_module("""tasks"""   )

    try:
        __snake_case       :				Union[str, Any] 			=    getattr(__SCREAMING_SNAKE_CASE		,							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"""		,							__SCREAMING_SNAKE_CASE   )

    # Prepare Token Classification task
    __snake_case       :				str 			=    token_classification_task.get_labels(data_args.labels   )
    __snake_case       :				Dict[int, str] 			=    dict(enumerate(__SCREAMING_SNAKE_CASE   )   )
    __snake_case       :				Any 			=    len(__SCREAMING_SNAKE_CASE   )

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

    __snake_case       :				List[str] 			=    AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path		,							num_labels=__SCREAMING_SNAKE_CASE		,							idalabel=__SCREAMING_SNAKE_CASE		,							labelaid={label: i for i, label in enumerate(__SCREAMING_SNAKE_CASE   )}		,							cache_dir=model_args.cache_dir		,							)
    __snake_case       :				List[str] 			=    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       :				Optional[int] 			=    TFAutoModelForTokenClassification.from_pretrained(
            model_args.model_name_or_path		,							from_pt=bool(""".bin""" in model_args.model_name_or_path   )		,							config=__SCREAMING_SNAKE_CASE		,							cache_dir=model_args.cache_dir		,							)

    # Get datasets
    __snake_case       :				Optional[int] 			=    (
        TFTokenClassificationDataset(
            token_classification_task=__SCREAMING_SNAKE_CASE		,							data_dir=data_args.data_dir		,							tokenizer=__SCREAMING_SNAKE_CASE		,							labels=__SCREAMING_SNAKE_CASE		,							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       :				Union[str, Any] 			=    (
        TFTokenClassificationDataset(
            token_classification_task=__SCREAMING_SNAKE_CASE		,							data_dir=data_args.data_dir		,							tokenizer=__SCREAMING_SNAKE_CASE		,							labels=__SCREAMING_SNAKE_CASE		,							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(__SCREAMING_SNAKE_CASE       :					np.ndarray		,							__SCREAMING_SNAKE_CASE       :					np.ndarray   ) -> Tuple[List[int], List[int]]:
        __snake_case       :				Dict 			=    np.argmax(__SCREAMING_SNAKE_CASE		,							axis=2   )
        __snake_case			, __snake_case       :				int 			=    preds.shape
        __snake_case       :				Union[str, Any] 			=    [[] for _ in range(__SCREAMING_SNAKE_CASE   )]
        __snake_case       :				Union[str, Any] 			=    [[] for _ in range(__SCREAMING_SNAKE_CASE   )]

        for i in range(__SCREAMING_SNAKE_CASE   ):
            for j in range(__SCREAMING_SNAKE_CASE   ):
                if label_ids[i, j] != -1_0_0:
                    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(__SCREAMING_SNAKE_CASE       :					EvalPrediction   ) -> Dict:
        __snake_case			, __snake_case       :				Optional[int] 			=    align_predictions(p.predictions		,							p.label_ids   )

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

    # Initialize our Trainer
    __snake_case       :				Optional[Any] 			=    TFTrainer(
        model=__SCREAMING_SNAKE_CASE		,							args=__SCREAMING_SNAKE_CASE		,							train_dataset=train_dataset.get_dataset() if train_dataset else None		,							eval_dataset=eval_dataset.get_dataset() if eval_dataset else None		,							compute_metrics=__SCREAMING_SNAKE_CASE		,							)

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

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

        __snake_case       :				List[str] 			=    trainer.evaluate()
        __snake_case       :				Dict 			=    os.path.join(training_args.output_dir		,							"""eval_results.txt"""   )

        with open(__SCREAMING_SNAKE_CASE		,							"""w"""   ) as writer:
            logger.info("""***** Eval results *****"""   )

            for key, value in result.items():
                logger.info("""  %s = %s"""		,							__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )
                writer.write("""%s = %s\n""" % (key, value)   )

            results.update(__SCREAMING_SNAKE_CASE   )

    # Predict
    if training_args.do_predict:
        __snake_case       :				Any 			=    TFTokenClassificationDataset(
            token_classification_task=__SCREAMING_SNAKE_CASE		,							data_dir=data_args.data_dir		,							tokenizer=__SCREAMING_SNAKE_CASE		,							labels=__SCREAMING_SNAKE_CASE		,							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       :				Optional[Any] 			=    trainer.predict(test_dataset.get_dataset()   )
        __snake_case			, __snake_case       :				Dict 			=    align_predictions(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )
        __snake_case       :				Optional[Any] 			=    classification_report(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )

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

        __snake_case       :				Optional[int] 			=    os.path.join(training_args.output_dir		,							"""test_results.txt"""   )

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

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

        with open(__SCREAMING_SNAKE_CASE		,							"""w"""   ) as writer:
            with open(os.path.join(data_args.data_dir		,							"""test.txt"""   )		,							"""r"""   ) as f:
                __snake_case       :				List[str] 			=    0

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

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

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

    return results


if __name__ == "__main__":
     main()



