






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        snake_case_				:







   """simple docstring"""



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






@dataclass
class        snake_case_				:







   """simple docstring"""



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






def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _lowercase     			=						HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)   )
    _lowercase			,     _lowercase			,     _lowercase     			=						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.'   )

    _lowercase     			=						import_module('tasks'   )

    try:
        _lowercase     			=						getattr(_lowercase,   model_args.task_type   )
        _lowercase     			=						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',   _lowercase   )

    # Prepare Token Classification task
    _lowercase     			=						token_classification_task.get_labels(data_args.labels   )
    _lowercase     			=						dict(enumerate(_lowercase   )   )
    _lowercase     			=						len(_lowercase   )

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

    _lowercase     			=						AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path,   num_labels=_lowercase,   idalabel=_lowercase,   labelaid={label: i for i, label in enumerate(_lowercase   )},   cache_dir=model_args.cache_dir,   )
    _lowercase     			=						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():
        _lowercase     			=						TFAutoModelForTokenClassification.from_pretrained(
            model_args.model_name_or_path,   from_pt=bool('.bin' in model_args.model_name_or_path   ),   config=_lowercase,   cache_dir=model_args.cache_dir,   )

    # Get datasets
    _lowercase     			=						(
        TFTokenClassificationDataset(
            token_classification_task=_lowercase,   data_dir=data_args.data_dir,   tokenizer=_lowercase,   labels=_lowercase,   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
    )
    _lowercase     			=						(
        TFTokenClassificationDataset(
            token_classification_task=_lowercase,   data_dir=data_args.data_dir,   tokenizer=_lowercase,   labels=_lowercase,   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(_lowercase,   _lowercase   ) -> Tuple[List[int], List[int]]:
        _lowercase     			=						np.argmax(_lowercase,   axis=2   )
        _lowercase			,     _lowercase     			=						preds.shape
        _lowercase     			=						[[] for _ in range(_lowercase   )]
        _lowercase     			=						[[] for _ in range(_lowercase   )]

        for i in range(_lowercase   ):
            for j in range(_lowercase   ):
                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(_lowercase   ) -> Dict:
        _lowercase			,     _lowercase     			=						align_predictions(p.predictions,   p.label_ids   )

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

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

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

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

        _lowercase     			=						trainer.evaluate()
        _lowercase     			=						os.path.join(training_args.output_dir,   'eval_results.txt'   )

        with open(_lowercase,   'w'   ) as writer:
            logger.info('***** Eval results *****'   )

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

            results.update(_lowercase   )

    # Predict
    if training_args.do_predict:
        _lowercase     			=						TFTokenClassificationDataset(
            token_classification_task=_lowercase,   data_dir=data_args.data_dir,   tokenizer=_lowercase,   labels=_lowercase,   model_type=config.model_type,   max_seq_length=data_args.max_seq_length,   overwrite_cache=data_args.overwrite_cache,   mode=Split.test,   )

        _lowercase			,     _lowercase			,     _lowercase     			=						trainer.predict(test_dataset.get_dataset()   )
        _lowercase			,     _lowercase     			=						align_predictions(_lowercase,   _lowercase   )
        _lowercase     			=						classification_report(_lowercase,   _lowercase   )

        logger.info('\n%s',   _lowercase   )

        _lowercase     			=						os.path.join(training_args.output_dir,   'test_results.txt'   )

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

        # Save predictions
        _lowercase     			=						os.path.join(training_args.output_dir,   'test_predictions.txt'   )

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

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

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

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

    return results


if __name__ == "__main__":
   main()








from typing import Literal

lowerCamelCase					    =	'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						LETTERS if mode == 'decrypt' else key
    _lowercase     			=						key if mode == 'decrypt' else LETTERS
    _lowercase     			=						''
    # loop through each symbol in the message
    for symbol in message:
        if symbol.upper() in chars_a:
            # encrypt/decrypt the symbol
            _lowercase     			=						chars_a.find(symbol.upper()   )
            if symbol.isupper():
                translated += chars_b[sym_index].upper()
            else:
                translated += chars_b[sym_index].lower()
        else:
            # symbol is not in LETTERS, just add it
            translated += symbol
    return translated
def 							UpperCAmelCase   ( _lowercase,   _lowercase   ):


    '''simple docstring'''



    return translate_message(_lowercase,   _lowercase,   'encrypt'   )
def 							UpperCAmelCase   ( _lowercase,   _lowercase   ):


    '''simple docstring'''



    return translate_message(_lowercase,   _lowercase,   'decrypt'   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _lowercase     			=						'Hello World'
    _lowercase     			=						'QWERTYUIOPASDFGHJKLZXCVBNM'
    _lowercase     			=						'decrypt'  # set to 'encrypt' or 'decrypt'

    if mode == "encrypt":
        _lowercase     			=						encrypt_message(_lowercase,   _lowercase   )
    elif mode == "decrypt":
        _lowercase     			=						decrypt_message(_lowercase,   _lowercase   )
    print(f'''Using the key {key}, the {mode}ed message is: {translated}'''   )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()
