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


SCREAMING_SNAKE_CASE		:     Dict         =	logging.getLogger(__name__)

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


@dataclass
class        UpperCamelCase						:
						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=__a	,	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=__a	,	metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}				)






def     lowerCAmelCase_			(   ):
						# 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.

						UpperCamelCase_		:							Optional[int]  =		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_		:							List[Any]  =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
						else:
												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							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_		:							Any  =		import_module("""tasks"""			)
						try:
												UpperCamelCase_		:							List[str]  =		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_		:							str  =		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_		:							Any  =		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_		:							Dict  =		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_		:							Optional[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_		:							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.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     :				np.ndarray		, _SCREAMING_SNAKE_CASE     :				np.ndarray			) -> Tuple[List[int], List[int]]:
												UpperCamelCase_		:							int  =		np.argmax(_SCREAMING_SNAKE_CASE		, axis=2			)

												UpperCamelCase_,UpperCamelCase_		:							List[Any]  =		preds.shape

												UpperCamelCase_		:							Optional[Any]  =		[[] for _ in range(_SCREAMING_SNAKE_CASE			)]
												UpperCamelCase_		:							Optional[int]  =		[[] 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     :				EvalPrediction			) -> Dict:
												UpperCamelCase_,UpperCamelCase_		:							List[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_		:							Tuple  =		DataCollatorWithPadding(_SCREAMING_SNAKE_CASE		, pad_to_multiple_of=8			) if training_args.fpaa else None

						# Initialize our Trainer
						UpperCamelCase_		:							Dict  =		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_		:							Optional[Any]  =		{}
						if training_args.do_eval:
												logger.info("""*** Evaluate ***"""			)

												UpperCamelCase_		:							str  =		trainer.evaluate()

												UpperCamelCase_		:							List[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_		:							int  =		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_		:							Union[str, Any]  =		trainer.predict(_SCREAMING_SNAKE_CASE			)
												UpperCamelCase_,UpperCamelCase_		:							Dict  =		align_predictions(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)

												UpperCamelCase_		:							Optional[Any]  =		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_		:							int  =		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     :				Tuple			):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
	main()







import os
import random
import sys

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






def     lowerCAmelCase_			(   ):
						print("""Making key files..."""			)
						make_key_files("""rsa"""		, 1024			)
						print("""Key files generation successful."""			)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				int			):
						print("""Generating prime p..."""			)
						UpperCamelCase_		:							Tuple  =		rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE			)
						print("""Generating prime q..."""			)
						UpperCamelCase_		:							List[Any]  =		rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							List[str]  =		p * q

						print("""Generating e that is relatively prime to (p - 1) * (q - 1)..."""			)
						while True:
												UpperCamelCase_		:							Union[str, Any]  =		random.randrange(2 ** (key_size - 1)		, 2 ** (key_size)			)
												if cryptoMath.gcd(_SCREAMING_SNAKE_CASE		, (p - 1) * (q - 1)			) == 1:
																		break

						print("""Calculating d that is mod inverse of e..."""			)
						UpperCamelCase_		:							Tuple  =		cryptoMath.find_mod_inverse(_SCREAMING_SNAKE_CASE		, (p - 1) * (q - 1)			)

						UpperCamelCase_		:							Tuple  =		(n, e)
						UpperCamelCase_		:							int  =		(n, d)
						return (public_key, private_key)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str		, _SCREAMING_SNAKE_CASE     :				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()

						UpperCamelCase_,UpperCamelCase_		:							str  =		generate_key(_SCREAMING_SNAKE_CASE			)
						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()






