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							 =					logging.getLogger(__name__)


@dataclass
class 	lowerCamelCase_     :






    '''simple docstring'''






    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 	lowerCamelCase_     :






    '''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(
        default=UpperCAmelCase_						,	metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}						,	)
    a__	:						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."""
            )
        }						,	)
    a__	:						bool						=      field(
        default=UpperCAmelCase_						,	metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )



def lowerCamelCase  (   ):
    '''simple docstring'''







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

    __UpperCamelCase     :List[str]			  =		import_module('''tasks'''	)
    try:
        __UpperCamelCase     :Any			  =		getattr(SCREAMING_SNAKE_CASE  ,			model_args.task_type	)
        __UpperCamelCase     :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'''  ,			SCREAMING_SNAKE_CASE	)

    # Set seed
    set_seed(training_args.seed	)

    # Prepare CONLL-2003 task
    __UpperCamelCase     :Optional[Any]			  =		token_classification_task.get_labels(data_args.labels	)
    __UpperCamelCase     :Dict[int, str]			  =		dict(enumerate(SCREAMING_SNAKE_CASE	)	)
    __UpperCamelCase     :List[str]			  =		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.

    __UpperCamelCase     :int			  =		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  ,			)
    __UpperCamelCase     :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  ,			)
    __UpperCamelCase     :int			  =		AutoModelForTokenClassification.from_pretrained(
        model_args.model_name_or_path  ,			from_tf=bool('''.ckpt''' in model_args.model_name_or_path	)  ,			config=SCREAMING_SNAKE_CASE  ,			cache_dir=model_args.cache_dir  ,			)

    # Get datasets
    __UpperCamelCase     :List[str]			  =		(
        TokenClassificationDataset(
            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
    )
    __UpperCamelCase     :Optional[Any]			  =		(
        TokenClassificationDataset(
            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  ,			SCREAMING_SNAKE_CASE	) -> Tuple[List[int], List[int]]:
        __UpperCamelCase     :Dict			  =		np.argmax(SCREAMING_SNAKE_CASE  ,			axis=2	)

        __UpperCamelCase  ,       __UpperCamelCase     :Any			  =		preds.shape

        __UpperCamelCase     :List[str]			  =		[[] for _ in range(SCREAMING_SNAKE_CASE	)]
        __UpperCamelCase     :Optional[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] != 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(SCREAMING_SNAKE_CASE	) -> Dict:
        __UpperCamelCase  ,       __UpperCamelCase     :str			  =		align_predictions(p.predictions  ,			p.label_ids	)
        return {
            "accuracy_score": accuracy_score(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	),
            "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	),
        }

    # Data collator
    __UpperCamelCase     :str			  =		DataCollatorWithPadding(SCREAMING_SNAKE_CASE  ,			pad_to_multiple_of=8	) if training_args.fpaa else None

    # Initialize our Trainer
    __UpperCamelCase     :List[Any]			  =		Trainer(
        model=SCREAMING_SNAKE_CASE  ,			args=SCREAMING_SNAKE_CASE  ,			train_dataset=SCREAMING_SNAKE_CASE  ,			eval_dataset=SCREAMING_SNAKE_CASE  ,			compute_metrics=SCREAMING_SNAKE_CASE  ,			data_collator=SCREAMING_SNAKE_CASE  ,			)

    # 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
    __UpperCamelCase     :Any			  =		{}
    if training_args.do_eval:
        logger.info('''*** Evaluate ***'''	)

        __UpperCamelCase     :Dict			  =		trainer.evaluate()

        __UpperCamelCase     :str			  =		os.path.join(training_args.output_dir  ,			'''eval_results.txt'''	)
        if trainer.is_world_process_zero():
            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:
        __UpperCamelCase     :str			  =		TokenClassificationDataset(
            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  ,			)

        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :Optional[Any]			  =		trainer.predict(SCREAMING_SNAKE_CASE	)
        __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		align_predictions(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

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

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

    return results



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    main()


if __name__ == "__main__":
    main()






import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		AlbertConfig.from_json_file(SCREAMING_SNAKE_CASE	)
    print(f"""Building PyTorch model from configuration: {config}"""	)
    __UpperCamelCase     :List[str]			  =		AlbertForPreTraining(SCREAMING_SNAKE_CASE	)

    # Load weights from tf checkpoint
    load_tf_weights_in_albert(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    # Save pytorch-model
    print(f"""Save PyTorch model to {pytorch_dump_path}"""	)
    torch.save(model.state_dict()  ,			SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
    __lowercase							 =					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
    )
    parser.add_argument(
        '''--albert_config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained ALBERT model. \n'''
            '''This specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    __lowercase							 =					parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





