



"""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


lowercase__					:						List[str]												=			logging.getLogger(__name__)




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





@dataclass
class  _UpperCAmelCase :
 _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=lowerCAmelCase__					,  metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}					,  )
 _lowerCAmelCase     :    int	      =	field(
     default=1_2_8					,  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=lowerCAmelCase__					,  metadata={"""help""": """Overwrite the cached training and evaluation sets"""})





def __lowercase    (							):
    # 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_				:  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.
        snake_case_,			snake_case_,			snake_case_				:  Optional[Any]       =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
    else:
        snake_case_,			snake_case_,			snake_case_				:  List[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.'''					)

    snake_case_				:  Dict       =			import_module('''tasks'''					)
    try:
        snake_case_				:  str       =			getattr(_a	,    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 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'''	,    _a					)

    # Set seed
    set_seed(training_args.seed					)

    # Prepare CONLL-2003 task
    snake_case_				:  Tuple       =			token_classification_task.get_labels(data_args.labels					)
    snake_case_				:  Dict[int, str]       =			dict(enumerate(_a					)					)
    snake_case_				:  List[Any]       =			len(_a					)

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

    snake_case_				:  int       =			AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path	,    num_labels=_a	,    idalabel=_a	,    labelaid={label: i for i, label in enumerate(_a					)}	,    cache_dir=model_args.cache_dir	,    )
    snake_case_				:  Union[str, 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	,    )
    snake_case_				:  Optional[Any]       =			AutoModelForTokenClassification.from_pretrained(
        model_args.model_name_or_path	,    from_tf=bool('''.ckpt''' in model_args.model_name_or_path					)	,    config=_a	,    cache_dir=model_args.cache_dir	,    )

    # Get datasets
    snake_case_				:  Union[str, Any]       =			(
        TokenClassificationDataset(
            token_classification_task=_a	,    data_dir=data_args.data_dir	,    tokenizer=_a	,    labels=_a	,    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_				:  Any       =			(
        TokenClassificationDataset(
            token_classification_task=_a	,    data_dir=data_args.data_dir	,    tokenizer=_a	,    labels=_a	,    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(_a	,    _a					) -> Tuple[List[int], List[int]]:
        snake_case_				:  List[str]       =			np.argmax(_a	,    axis=2					)

        snake_case_,			snake_case_				:  Any       =			preds.shape

        snake_case_				:  List[Any]       =			[[] for _ in range(_a					)]
        snake_case_				:  Union[str, Any]       =			[[] for _ in range(_a					)]

        for i in range(_a					):
            for j in range(_a					):
                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(_a					) -> Dict:
        snake_case_,			snake_case_				:  Union[str, Any]       =			align_predictions(p.predictions	,    p.label_ids					)
        return {
            "accuracy_score": accuracy_score(_a	,    _a					),
            "precision": precision_score(_a	,    _a					),
            "recall": recall_score(_a	,    _a					),
            "f1": fa_score(_a	,    _a					),
        }

    # Data collator
    snake_case_				:  Dict       =			DataCollatorWithPadding(_a	,    pad_to_multiple_of=8					) if training_args.fpaa else None

    # Initialize our Trainer
    snake_case_				:  str       =			Trainer(
        model=_a	,    args=_a	,    train_dataset=_a	,    eval_dataset=_a	,    compute_metrics=_a	,    data_collator=_a	,    )

    # 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
    snake_case_				:  Optional[Any]       =			{}
    if training_args.do_eval:
        logger.info('''*** Evaluate ***'''					)

        snake_case_				:  Tuple       =			trainer.evaluate()

        snake_case_				:  List[Any]       =			os.path.join(training_args.output_dir	,    '''eval_results.txt'''					)
        if trainer.is_world_process_zero():
            with open(_a	,    '''w'''					) as writer:
                logger.info('''***** Eval results *****'''					)
                for key, value in result.items():
                    logger.info('''  %s = %s'''	,    _a	,    _a					)
                    writer.write('''%s = %s\n''' % (key, value)					)

            results.update(_a					)

    # Predict
    if training_args.do_predict:
        snake_case_				:  Dict       =			TokenClassificationDataset(
            token_classification_task=_a	,    data_dir=data_args.data_dir	,    tokenizer=_a	,    labels=_a	,    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_				:  str       =			trainer.predict(_a					)
        snake_case_,			snake_case_				:  Optional[int]       =			align_predictions(_a	,    _a					)

        snake_case_				:  Dict       =			os.path.join(training_args.output_dir	,    '''test_results.txt'''					)
        if trainer.is_world_process_zero():
            with open(_a	,    '''w'''					) as writer:
                for key, value in metrics.items():
                    logger.info('''  %s = %s'''	,    _a	,    _a					)
                    writer.write('''%s = %s\n''' % (key, value)					)

        # Save predictions
        snake_case_				:  int       =			os.path.join(training_args.output_dir	,    '''test_predictions.txt'''					)
        if trainer.is_world_process_zero():
            with open(_a	,    '''w'''					) as writer:
                with open(os.path.join(data_args.data_dir	,    '''test.txt'''					)	,    '''r'''					) as f:
                    token_classification_task.write_predictions_to_file(_a	,    _a	,    _a					)

    return results




def __lowercase    (							_a					):
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
     main()










"""simple docstring"""

from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info




def __lowercase    (							_a					):
    return {key.lstrip('''-'''					): value for key, value in zip(unknown_args[::2]	,    unknown_args[1::2]					)}




def __lowercase    (							):
    snake_case_				:  List[str]       =			ArgumentParser(
        '''HuggingFace Datasets CLI tool'''	,    usage='''datasets-cli <command> [<args>]'''	,    allow_abbrev=_a					)
    snake_case_				:  List[Any]       =			parser.add_subparsers(help='''datasets-cli command helpers'''					)
    set_verbosity_info()

    # Register commands
    ConvertCommand.register_subcommand(_a					)
    EnvironmentCommand.register_subcommand(_a					)
    TestCommand.register_subcommand(_a					)
    RunBeamCommand.register_subcommand(_a					)
    DummyDataCommand.register_subcommand(_a					)

    # Parse args
    snake_case_,			snake_case_				:  Optional[Any]       =			parser.parse_known_args()
    if not hasattr(_a	,    '''func'''					):
        parser.print_help()
        exit(1					)
    snake_case_				:  Optional[int]       =			parse_unknown_args(_a					)

    # Run
    snake_case_				:  Optional[int]       =			args.func(_a	,    **_a					)
    service.run()


if __name__ == "__main__":
     main()





