"""simple docstring"""






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__    :    Tuple			=      logging.getLogger(__name__)
@dataclass
class 							snake_case			:




					"""simple docstring"""

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






@dataclass
class 							snake_case			:




					"""simple docstring"""

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






def     a_		(   ):
				# 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__			 =				HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)			)
				UpperCAmelCase__ ,      UpperCAmelCase__ ,      UpperCAmelCase__			 =				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__			 =				import_module('tasks'			)

				try:
								UpperCAmelCase__			 =				getattr(lowerCamelCase   ,     model_args.task_type			)
								UpperCAmelCase__			 =				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'   ,     lowerCamelCase			)

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

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

				UpperCAmelCase__			 =				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   ,     )
				UpperCAmelCase__			 =				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():
								UpperCAmelCase__			 =				TFAutoModelForTokenClassification.from_pretrained(
								    model_args.model_name_or_path   ,     from_pt=bool('.bin' in model_args.model_name_or_path			)   ,     config=lowerCamelCase   ,     cache_dir=model_args.cache_dir   ,     )

				# Get datasets
				UpperCAmelCase__			 =				(
				    TFTokenClassificationDataset(
				        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
				)
				UpperCAmelCase__			 =				(
				    TFTokenClassificationDataset(
				        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   ,     lowerCamelCase			) -> Tuple[List[int], List[int]]:
								UpperCAmelCase__			 =				np.argmax(lowerCamelCase   ,     axis=2			)
								UpperCAmelCase__ ,      UpperCAmelCase__			 =				preds.shape
								UpperCAmelCase__			 =				[[] for _ in range(lowerCamelCase			)]
								UpperCAmelCase__			 =				[[] for _ in range(lowerCamelCase			)]

								for i in range(lowerCamelCase			):
												for j in range(lowerCamelCase			):
																if label_ids[i, j] != -1_0_0:
																				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			) -> Dict:
								UpperCAmelCase__ ,      UpperCAmelCase__			 =				align_predictions(p.predictions   ,     p.label_ids			)

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

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

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

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

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

								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:
								UpperCAmelCase__			 =				TFTokenClassificationDataset(
								    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   ,     )

								UpperCAmelCase__ ,      UpperCAmelCase__ ,      UpperCAmelCase__			 =				trainer.predict(test_dataset.get_dataset()			)
								UpperCAmelCase__ ,      UpperCAmelCase__			 =				align_predictions(lowerCamelCase   ,     lowerCamelCase			)
								UpperCAmelCase__			 =				classification_report(lowerCamelCase   ,     lowerCamelCase			)

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

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

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

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

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

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

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

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

				return results


if __name__ == "__main__":
						main()




"""simple docstring"""






from __future__ import annotations



def     a_		(   lowerCamelCase			):
				for r in range(1   ,     lowerCamelCase			):
								UpperCAmelCase__			 =				[]
								for x in range(n - 1			):
												UpperCAmelCase__			 =				pow(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase			)
												if val in li:
																break
												li.append(lowerCamelCase			)
								else:
												return r
				return None


if __name__ == "__main__":
						lowerCAmelCase__    :    Any			=      int(input('Enter a prime number q: '))
						lowerCAmelCase__    :    List[str]			=      find_primitive(q)
						if a is None:
												print(F"""Cannot find the primitive for the value: {a!r}""")
						else:
												lowerCAmelCase__    :    str			=      int(input('Enter private key of A: '))
												lowerCAmelCase__    :    List[Any]			=      pow(a, a_private, q)
												lowerCAmelCase__    :    List[str]			=      int(input('Enter private key of B: '))
												lowerCAmelCase__    :    Any			=      pow(a, b_private, q)

												lowerCAmelCase__    :    Union[str, Any]			=      pow(b_public, a_private, q)
												lowerCAmelCase__    :    Tuple			=      pow(a_public, b_private, q)

												print('The key value generated by A is: ', a_secret)
												print('The key value generated by B is: ', b_secret)



