
'''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()


a__		:   List[str]            =       logging.getLogger(__name__)


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


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





def        snake_case							(		)->			List[str]:







      """simple docstring"""
      # 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, TFTrainingArguments)	)
      __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(UpperCAmelCase      ,  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      ,  )
      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'      ,  UpperCAmelCase	)

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

      # 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=UpperCAmelCase      ,  idalabel=UpperCAmelCase      ,  labelaid={label: i for i, label in enumerate(UpperCAmelCase	)}      ,  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      ,  )

      with training_args.strategy.scope():
            __A       	=			TFAutoModelForTokenClassification.from_pretrained(
                model_args.model_name_or_path      ,  from_pt=bool('.bin' in model_args.model_name_or_path	)      ,  config=UpperCAmelCase      ,  cache_dir=model_args.cache_dir      ,  )

      # Get datasets
      __A       	=			(
          TFTokenClassificationDataset(
              token_classification_task=UpperCAmelCase      ,  data_dir=data_args.data_dir      ,  tokenizer=UpperCAmelCase      ,  labels=UpperCAmelCase      ,  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       	=			(
          TFTokenClassificationDataset(
              token_classification_task=UpperCAmelCase      ,  data_dir=data_args.data_dir      ,  tokenizer=UpperCAmelCase      ,  labels=UpperCAmelCase      ,  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(UpperCAmelCase      ,  UpperCAmelCase	) -> Tuple[List[int], List[int]]:
            __A       	=			np.argmax(UpperCAmelCase      ,  axis=2	)
            __A	,      __A       	=			preds.shape
            __A       	=			[[] for _ in range(UpperCAmelCase	)]
            __A       	=			[[] for _ in range(UpperCAmelCase	)]

            for i in range(UpperCAmelCase	):
                  for j in range(UpperCAmelCase	):
                        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(UpperCAmelCase	) -> Dict:
            __A	,      __A       	=			align_predictions(p.predictions      ,  p.label_ids	)

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

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

      # Training
      if training_args.do_train:
            trainer.train()
            trainer.save_model()
            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'	)

            with open(UpperCAmelCase      ,  'w'	) as writer:
                  logger.info('***** Eval results *****'	)

                  for key, value in result.items():
                        logger.info('  %s = %s'      ,  UpperCAmelCase      ,  UpperCAmelCase	)
                        writer.write('%s = %s\n' % (key, value)	)

                  results.update(UpperCAmelCase	)

    # Predict
      if training_args.do_predict:
            __A       	=			TFTokenClassificationDataset(
                token_classification_task=UpperCAmelCase      ,  data_dir=data_args.data_dir      ,  tokenizer=UpperCAmelCase      ,  labels=UpperCAmelCase      ,  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(test_dataset.get_dataset()	)
            __A	,      __A       	=			align_predictions(UpperCAmelCase      ,  UpperCAmelCase	)
            __A       	=			classification_report(UpperCAmelCase      ,  UpperCAmelCase	)

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

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

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

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

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

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

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

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

      return results


if __name__ == "__main__":
 main()








'''simple docstring'''





from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime


class  UpperCamelCase__  (    SCREAMING_SNAKE_CASE):






 def __init__(			self  :Union[str, Any]	,				*_A  :int	,				**_A  :Optional[Any]			)				->			Union[str, Any]:







       '''simple docstring'''



       super().__init__(*_A	,				**_A			)






 def 	lowercase_						(			self  :Dict	,				_A  :Union[str, Any]	,				_A  :Dict			)				->			Dict:







       '''simple docstring'''



       __A       	=			(
           next_prime(value % self.size_table			)
           if not is_prime(value % self.size_table			)
           else value % self.size_table
       )  # gt = bigger than
       return next_prime_gt - (data % next_prime_gt)






 def 	lowercase_						(			self  :List[Any]	,				_A  :int	,				_A  :Tuple	,				_A  :Union[str, Any]			)				->			List[Any]:







       '''simple docstring'''



       return (increment * self.__hash_function_a(_A	,				_A			)) % self.size_table






 def 	lowercase_						(			self  :List[Any]	,				_A  :List[str]	,				_A  :str=None			)				->			List[Any]:







       '''simple docstring'''



       __A       	=			1
       __A       	=			self.hash_function(_A			)

       while self.values[new_key] is not None and self.values[new_key] != key:
             __A       	=			(
                 self.__hash_double_function(_A	,				_A	,				_A			)
                 if self.balanced_factor() >= self.lim_charge
                 else None
             )
             if new_key is None:
                   break
             else:
                   i += 1

       return new_key






