


'''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


__UpperCAmelCase    :int				       =     logging.getLogger(__name__)



@dataclass
class 			a       :


     """simple docstring"""






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



@dataclass
class 			a       :


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				str							     =     field(
         metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."} )
     SCREAMING_SNAKE_CASE :				Optional[str]							     =     field(
         default=_a			,						metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}			,						)
     SCREAMING_SNAKE_CASE :				int							     =     field(
         default=1_2_8			,						metadata={
             "help": (
                 "The maximum total input sequence length after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )
         }			,						)
     SCREAMING_SNAKE_CASE :				bool							     =     field(
         default=_a			,						metadata={"help": "Overwrite the cached training and evaluation sets"} )




def 			_a			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				Tuple													=    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			:				List[str]													=    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			:				List[str]													=    import_module('''tasks'''						)
     try:
          __UpperCAmelCase			:				Dict													=    getattr(_lowercase    ,      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'''    ,      _lowercase						)

     # Set seed
     set_seed(training_args.seed						)

     # Prepare CONLL-2003 task
     __UpperCAmelCase			:				int													=    token_classification_task.get_labels(data_args.labels						)
     __UpperCAmelCase			:				Dict[int, str]													=    dict(enumerate(_lowercase						)						)
     __UpperCAmelCase			:				Any													=    len(_lowercase						)

     # 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=_lowercase    ,      idalabel=_lowercase    ,      labelaid={label: i for i, label in enumerate(_lowercase						)}    ,      cache_dir=model_args.cache_dir    ,      )
     __UpperCAmelCase			:				Optional[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    ,      )
     __UpperCAmelCase			:				Dict													=    AutoModelForTokenClassification.from_pretrained(
         model_args.model_name_or_path    ,      from_tf=bool('''.ckpt''' in model_args.model_name_or_path						)    ,      config=_lowercase    ,      cache_dir=model_args.cache_dir    ,      )

     # Get datasets
     __UpperCAmelCase			:				List[str]													=    (
         TokenClassificationDataset(
             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
     )
     __UpperCAmelCase			:				Union[str, Any]													=    (
         TokenClassificationDataset(
             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      :			np.ndarray    ,      _lowercase      :			np.ndarray						) -> Tuple[List[int], List[int]]:
          __UpperCAmelCase			:				str													=    np.argmax(_lowercase    ,      axis=2						)

          __UpperCAmelCase							,     __UpperCAmelCase			:				Optional[Any]													=    preds.shape

          __UpperCAmelCase			:				List[str]													=    [[] for _ in range(_lowercase						)]
          __UpperCAmelCase			:				Dict													=    [[] for _ in range(_lowercase						)]

          for i in range(_lowercase						):
               for j in range(_lowercase						):
                    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(_lowercase      :			EvalPrediction						) -> Dict:
          __UpperCAmelCase							,     __UpperCAmelCase			:				Optional[Any]													=    align_predictions(p.predictions    ,      p.label_ids						)
          return {
              "accuracy_score": accuracy_score(_lowercase    ,      _lowercase						),
              "precision": precision_score(_lowercase    ,      _lowercase						),
              "recall": recall_score(_lowercase    ,      _lowercase						),
              "f1": fa_score(_lowercase    ,      _lowercase						),
          }

     # Data collator
     __UpperCAmelCase			:				Optional[Any]													=    DataCollatorWithPadding(_lowercase    ,      pad_to_multiple_of=8						) if training_args.fpaa else None

     # Initialize our Trainer
     __UpperCAmelCase			:				List[Any]													=    Trainer(
         model=_lowercase    ,      args=_lowercase    ,      train_dataset=_lowercase    ,      eval_dataset=_lowercase    ,      compute_metrics=_lowercase    ,      data_collator=_lowercase    ,      )

     # 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[int]													=    {}
     if training_args.do_eval:
          logger.info('''*** Evaluate ***'''						)

          __UpperCAmelCase			:				Optional[Any]													=    trainer.evaluate()

          __UpperCAmelCase			:				Any													=    os.path.join(training_args.output_dir    ,      '''eval_results.txt'''						)
          if trainer.is_world_process_zero():
               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:
          __UpperCAmelCase			:				Dict													=    TokenClassificationDataset(
              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    ,      )

          __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase			:				Any													=    trainer.predict(_lowercase						)
          __UpperCAmelCase							,     __UpperCAmelCase			:				Optional[Any]													=    align_predictions(_lowercase    ,      _lowercase						)

          __UpperCAmelCase			:				Union[str, Any]													=    os.path.join(training_args.output_dir    ,      '''test_results.txt'''						)
          if trainer.is_world_process_zero():
               with open(_lowercase    ,      '''w'''						) as writer:
                    for key, value in metrics.items():
                         logger.info('''  %s = %s'''    ,      _lowercase    ,      _lowercase						)
                         writer.write('''%s = %s\n''' % (key, value)						)

        # Save predictions
          __UpperCAmelCase			:				Optional[int]													=    os.path.join(training_args.output_dir    ,      '''test_predictions.txt'''						)
          if trainer.is_world_process_zero():
               with open(_lowercase    ,      '''w'''						) as writer:
                    with open(os.path.join(data_args.data_dir    ,      '''test.txt'''						)    ,      '''r'''						) as f:
                         token_classification_task.write_predictions_to_file(_lowercase    ,      _lowercase    ,      _lowercase						)

     return results




def 			_a			(		_lowercase      :			Optional[int]						):





     '''simple docstring'''






     main()


if __name__ == "__main__":
      main()



'''simple docstring'''



from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt




def 			_a			(		_lowercase      :			int						):





     '''simple docstring'''






     __UpperCAmelCase			:				int													=    int(number**0.5						)
     return number == sq * sq




def 			_a			(		_lowercase      :			int    ,      _lowercase      :			int    ,      _lowercase      :			int    ,      _lowercase      :			int    ,      _lowercase      :			int    ,      _lowercase      :			int						):





     '''simple docstring'''






     __UpperCAmelCase			:				int													=    x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
     __UpperCAmelCase			:				int													=    x_den * y_den * z_den
     __UpperCAmelCase			:				int													=    gcd(_lowercase    ,      _lowercase						)
     top //= hcf
     bottom //= hcf
     return top, bottom




def 			_a			(		_lowercase      :			int = 35						):





     '''simple docstring'''






     __UpperCAmelCase			:				set													=    set()
     __UpperCAmelCase			:				int
     __UpperCAmelCase			:				Fraction													=    Fraction(0						)
     __UpperCAmelCase			:				tuple[int, int]

     for x_num in range(1    ,      order + 1						):
          for x_den in range(x_num + 1    ,      order + 1						):
               for y_num in range(1    ,      order + 1						):
                    for y_den in range(y_num + 1    ,      order + 1						):
                         # n=1
                         __UpperCAmelCase			:				Optional[int]													=    x_num * y_den + x_den * y_num
                         __UpperCAmelCase			:				Dict													=    x_den * y_den
                         __UpperCAmelCase			:				List[Any]													=    gcd(_lowercase    ,      _lowercase						)
                         z_num //= hcf
                         z_den //= hcf
                         if 0 < z_num < z_den <= order:
                              __UpperCAmelCase			:				Dict													=    add_three(
                                  _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase						)
                              unique_s.add(_lowercase						)

                         # n=2
                         __UpperCAmelCase			:				Optional[int]													=    (
                             x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
                         )
                         __UpperCAmelCase			:				Any													=    x_den * x_den * y_den * y_den
                         if is_sq(_lowercase						) and is_sq(_lowercase						):
                              __UpperCAmelCase			:				List[Any]													=    int(sqrt(_lowercase						)						)
                              __UpperCAmelCase			:				Tuple													=    int(sqrt(_lowercase						)						)
                              __UpperCAmelCase			:				Union[str, Any]													=    gcd(_lowercase    ,      _lowercase						)
                              z_num //= hcf
                              z_den //= hcf
                              if 0 < z_num < z_den <= order:
                                   __UpperCAmelCase			:				Union[str, Any]													=    add_three(
                                       _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase						)
                                   unique_s.add(_lowercase						)

                    # n=-1
                         __UpperCAmelCase			:				Union[str, Any]													=    x_num * y_num
                         __UpperCAmelCase			:				List[Any]													=    x_den * y_num + x_num * y_den
                         __UpperCAmelCase			:				Any													=    gcd(_lowercase    ,      _lowercase						)
                         z_num //= hcf
                         z_den //= hcf
                         if 0 < z_num < z_den <= order:
                              __UpperCAmelCase			:				Optional[Any]													=    add_three(
                                  _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase						)
                              unique_s.add(_lowercase						)

                         # n=2
                         __UpperCAmelCase			:				Optional[Any]													=    x_num * x_num * y_num * y_num
                         __UpperCAmelCase			:				Dict													=    (
                             x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
                         )
                         if is_sq(_lowercase						) and is_sq(_lowercase						):
                              __UpperCAmelCase			:				Any													=    int(sqrt(_lowercase						)						)
                              __UpperCAmelCase			:				List[Any]													=    int(sqrt(_lowercase						)						)
                              __UpperCAmelCase			:				Any													=    gcd(_lowercase    ,      _lowercase						)
                              z_num //= hcf
                              z_den //= hcf
                              if 0 < z_num < z_den <= order:
                                   __UpperCAmelCase			:				int													=    add_three(
                                       _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase						)
                                   unique_s.add(_lowercase						)

     for num, den in unique_s:
          total += Fraction(_lowercase    ,      _lowercase						)

     return total.denominator + total.numerator


if __name__ == "__main__":
      print(f"""{solution() = }""")