




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


lowerCamelCase    :		Any			   =       logging.getLogger(__name__)







@dataclass
class   A:



 '''simple docstring'''


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







@dataclass
class   A:



 '''simple docstring'''


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

def       _SCREAMING_SNAKE_CASE      (							):


      '''simple docstring'''



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

      lowerCamelCase_							=   import_module('tasks'					)
      try:
            lowerCamelCase_							=   getattr(lowercase     ,							model_args.task_type					)
            lowerCamelCase_							=   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
      lowerCamelCase_							=   token_classification_task.get_labels(data_args.labels					)
      lowerCamelCase_							=   dict(enumerate(lowercase					)					)
      lowerCamelCase_							=   len(lowercase					)

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

      lowerCamelCase_							=   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     ,							)
      lowerCamelCase_							=   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     ,							)
      lowerCamelCase_							=   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
      lowerCamelCase_							=   (
          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
      )
      lowerCamelCase_							=   (
          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]]:
            lowerCamelCase_							=   np.argmax(lowercase     ,							axis=2					)

            lowerCamelCase_					,    lowerCamelCase_							=   preds.shape

            lowerCamelCase_							=   [[] for _ in range(lowercase					)]
            lowerCamelCase_							=   [[] 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:
            lowerCamelCase_					,    lowerCamelCase_							=   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
      lowerCamelCase_							=   DataCollatorWithPadding(lowercase     ,							pad_to_multiple_of=8					) if training_args.fpaa else None

      # Initialize our Trainer
      lowerCamelCase_							=   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
      lowerCamelCase_							=   {}
      if training_args.do_eval:
            logger.info('*** Evaluate ***'					)

            lowerCamelCase_							=   trainer.evaluate()

            lowerCamelCase_							=   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:
            lowerCamelCase_							=   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     ,							)

            lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_							=   trainer.predict(lowercase					)
            lowerCamelCase_					,    lowerCamelCase_							=   align_predictions(lowercase     ,							lowercase					)

            lowerCamelCase_							=   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
            lowerCamelCase_							=   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       _SCREAMING_SNAKE_CASE      (							lowercase     :	Optional[int]					):


      '''simple docstring'''



      main()


if __name__ == "__main__":
 main()









def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Tuple     ,							lowercase     :	Dict     ,							lowercase     :	List[str]     ,							lowercase     :	Dict     ,							lowercase     :	Dict     ,							lowercase     :	List[str]					):


      '''simple docstring'''



      if index == r:
            for j in range(lowercase					):
                  print(data[j]     ,							end=' '					)
            print(' '					)
            return
      #  When no more elements are there to put in data[]
      if i >= n:
            return
      # current is included, put next at next location
      lowerCamelCase_							=   arr[i]
      combination_util(lowercase     ,							lowercase     ,							lowercase     ,							index + 1     ,							lowercase     ,							i + 1					)
      # current is excluded, replace it with
      # next (Note that i+1 is passed, but
      # index is not changed)
      combination_util(lowercase     ,							lowercase     ,							lowercase     ,							lowercase     ,							lowercase     ,							i + 1					)
      # The main function that prints all combinations
      # of size r in arr[] of size n. This function
      # mainly uses combinationUtil()

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	int     ,							lowercase     :	Any     ,							lowercase     :	Tuple					):


      '''simple docstring'''



      lowerCamelCase_							=   [0] * r
      # Print all combination using temporary array 'data[]'
      combination_util(lowercase     ,							lowercase     ,							lowercase     ,							0     ,							lowercase     ,							0					)


if __name__ == "__main__":
 # Driver code to check the function above
 lowerCamelCase    :		int			   =       [10, 20, 30, 40, 50]
 print_combination(arr, len(arr), 3)
 # This code is contributed by Ambuj sahu



