





'''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


__lowerCAmelCase							:     Dict		     =logging.getLogger(__name__)






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






@dataclass
class        UpperCAmelCase     :
 __lowercase       		=    field(
     metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}    )
 __lowercase       		=    field(
     default=UpperCamelCase__      ,	metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}      ,	)
 __lowercase       		=    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."""
         )
     }      ,	)
 __lowercase       		=    field(
     default=UpperCamelCase__      ,	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.

    A__    =      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.
        A__,     A__,     A__    =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
    else:
        A__,     A__,     A__    =      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."	)

    A__    =      import_module("tasks"	)
    try:
        A__    =      getattr(_lowerCamelCase						,	model_args.task_type	)
        A__    =      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"						,	_lowerCamelCase	)

    # Set seed
    set_seed(training_args.seed	)

    # Prepare CONLL-2003 task
    A__    =      token_classification_task.get_labels(data_args.labels	)
    A__    =      dict(enumerate(_lowerCamelCase	)	)
    A__    =      len(_lowerCamelCase	)

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

    A__    =      AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path						,	num_labels=_lowerCamelCase						,	idalabel=_lowerCamelCase						,	labelaid={label: i for i, label in enumerate(_lowerCamelCase	)}						,	cache_dir=model_args.cache_dir						,	)
    A__    =      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						,	)
    A__    =      AutoModelForTokenClassification.from_pretrained(
        model_args.model_name_or_path						,	from_tf=bool(".ckpt" in model_args.model_name_or_path	)						,	config=_lowerCamelCase						,	cache_dir=model_args.cache_dir						,	)

    # Get datasets
    A__    =      (
        TokenClassificationDataset(
            token_classification_task=_lowerCamelCase						,	data_dir=data_args.data_dir						,	tokenizer=_lowerCamelCase						,	labels=_lowerCamelCase						,	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
    )
    A__    =      (
        TokenClassificationDataset(
            token_classification_task=_lowerCamelCase						,	data_dir=data_args.data_dir						,	tokenizer=_lowerCamelCase						,	labels=_lowerCamelCase						,	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(_lowerCamelCase : np.ndarray						,	_lowerCamelCase : np.ndarray	) -> Tuple[List[int], List[int]]:
        A__    =      np.argmax(_lowerCamelCase						,	axis=2	)

        A__,     A__    =      preds.shape

        A__    =      [[] for _ in range(_lowerCamelCase	)]
        A__    =      [[] for _ in range(_lowerCamelCase	)]

        for i in range(_lowerCamelCase	):
            for j in range(_lowerCamelCase	):
                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(_lowerCamelCase : EvalPrediction	) -> Dict:
        A__,     A__    =      align_predictions(p.predictions						,	p.label_ids	)
        return {
            "accuracy_score": accuracy_score(_lowerCamelCase						,	_lowerCamelCase	),
            "precision": precision_score(_lowerCamelCase						,	_lowerCamelCase	),
            "recall": recall_score(_lowerCamelCase						,	_lowerCamelCase	),
            "f1": fa_score(_lowerCamelCase						,	_lowerCamelCase	),
        }

    # Data collator
    A__    =      DataCollatorWithPadding(_lowerCamelCase						,	pad_to_multiple_of=8	) if training_args.fpaa else None

    # Initialize our Trainer
    A__    =      Trainer(
        model=_lowerCamelCase						,	args=_lowerCamelCase						,	train_dataset=_lowerCamelCase						,	eval_dataset=_lowerCamelCase						,	compute_metrics=_lowerCamelCase						,	data_collator=_lowerCamelCase						,	)

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

        A__    =      trainer.evaluate()

        A__    =      os.path.join(training_args.output_dir						,	"eval_results.txt"	)
        if trainer.is_world_process_zero():
            with open(_lowerCamelCase						,	"w"	) as writer:
                logger.info("***** Eval results *****"	)
                for key, value in result.items():
                    logger.info("  %s = %s"						,	_lowerCamelCase						,	_lowerCamelCase	)
                    writer.write("%s = %s\n" % (key, value)	)

            results.update(_lowerCamelCase	)

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

        A__,     A__,     A__    =      trainer.predict(_lowerCamelCase	)
        A__,     A__    =      align_predictions(_lowerCamelCase						,	_lowerCamelCase	)

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

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

    return results





def 						UpperCamelCase    (							_lowerCamelCase : Optional[int]	):
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
  main()










'''simple docstring'''

import os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812





def 						UpperCamelCase    (							):
    print("Making key files..."	)
    make_key_files("rsa"						,	10_24	)
    print("Key files generation successful."	)





def 						UpperCamelCase    (							_lowerCamelCase : int	):
    print("Generating prime p..."	)
    A__    =      rabinMiller.generate_large_prime(_lowerCamelCase	)
    print("Generating prime q..."	)
    A__    =      rabinMiller.generate_large_prime(_lowerCamelCase	)
    A__    =      p * q

    print("Generating e that is relatively prime to (p - 1) * (q - 1)..."	)
    while True:
        A__    =      random.randrange(2 ** (key_size - 1)						,	2 ** (key_size)	)
        if cryptoMath.gcd(_lowerCamelCase						,	(p - 1) * (q - 1)	) == 1:
            break

    print("Calculating d that is mod inverse of e..."	)
    A__    =      cryptoMath.find_mod_inverse(_lowerCamelCase						,	(p - 1) * (q - 1)	)

    A__    =      (n, e)
    A__    =      (n, d)
    return (public_key, private_key)





def 						UpperCamelCase    (							_lowerCamelCase : str						,	_lowerCamelCase : int	):
    if os.path.exists(F"{name}_pubkey.txt"	) or os.path.exists(F"{name}_privkey.txt"	):
        print("\nWARNING:"	)
        print(
            F"\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"
            "Use a different name or delete these files and re-run this program."	)
        sys.exit()

    A__,     A__    =      generate_key(_lowerCamelCase	)
    print(F"\nWriting public key to file {name}_pubkey.txt..."	)
    with open(F"{name}_pubkey.txt"						,	"w"	) as out_file:
        out_file.write(F"{key_size},{public_key[0]},{public_key[1]}"	)

    print(F"Writing private key to file {name}_privkey.txt..."	)
    with open(F"{name}_privkey.txt"						,	"w"	) as out_file:
        out_file.write(F"{key_size},{private_key[0]},{private_key[1]}"	)


if __name__ == "__main__":
  main()



