

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


snake_case_        =			logging.getLogger(__name__)


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



@dataclass
class 				SCREAMING_SNAKE_CASE__ :
    __lowerCamelCase						:	str														=					field(
        metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}					)
    __lowerCamelCase						:	Optional[str]														=					field(
        default=__snake_case		,	metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}		,	)
    __lowerCamelCase						:	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."""
            )
        }		,	)
    __lowerCamelCase						:	bool														=					field(
        default=__snake_case		,	metadata={"""help""": """Overwrite the cached training and evaluation sets"""}					)



def      snake_case__      (   ):



     '''simple docstring'''





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

     lowercase__   :       Dict				=	import_module('tasks'      )
     try:
          lowercase__   :       Union[str, Any]				=	getattr(SCREAMING_SNAKE_CASE_     ,					model_args.task_type      )
          lowercase__   :       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
     lowercase__   :       List[Any]				=	token_classification_task.get_labels(data_args.labels      )
     lowercase__   :       Dict[int, str]				=	dict(enumerate(SCREAMING_SNAKE_CASE_      )      )
     lowercase__   :       Dict				=	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.

     lowercase__   :       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     ,					)
     lowercase__   :       int				=	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     ,					)
     lowercase__   :       Union[str, Any]				=	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
     lowercase__   :       List[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
     )
     lowercase__   :       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.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]]:
          lowercase__   :       Any				=	np.argmax(SCREAMING_SNAKE_CASE_     ,					axis=2      )

          lowercase__				,			lowercase__   :       List[str]				=	preds.shape

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

     # Initialize our Trainer
     lowercase__   :       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
     lowercase__   :       List[Any]				=	{}
     if training_args.do_eval:
          logger.info('*** Evaluate ***'      )

          lowercase__   :       List[str]				=	trainer.evaluate()

          lowercase__   :       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:
          lowercase__   :       List[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.test     ,					)

          lowercase__				,			lowercase__				,			lowercase__   :       Any				=	trainer.predict(SCREAMING_SNAKE_CASE_      )
          lowercase__				,			lowercase__   :       str				=	align_predictions(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )

          lowercase__   :       int				=	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
          lowercase__   :       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      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     main()


if __name__ == "__main__":
       main()




def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     return 1 if digit in (0, 1) else (digit * factorial(digit - 1      ))

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     lowercase__   :       List[str]				=	0
     lowercase__   :       Optional[int]				=	number
     while duplicate > 0:
          lowercase__				,			lowercase__   :       Tuple				=	divmod(SCREAMING_SNAKE_CASE_     ,					10      )
          fact_sum += factorial(SCREAMING_SNAKE_CASE_      )
     return fact_sum == number


if __name__ == "__main__":
       print('''Program to check whether a number is a Krisnamurthy Number or not.''')
       snake_case_        =			int(input('''Enter number: ''').strip())
       print(
           F'''{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number.'''
       )

