


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				:    Optional[int]       				=     logging.getLogger(__name__)







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







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







def 				__lowerCamelCase						( ):




      """simple docstring"""





      a						:Dict 							=       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.
            a							,				a							,				a						:int 							=       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
      else:
            a							,				a							,				a						:Union[str, 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.'''						)

      a						:str 							=       import_module('''tasks'''						)
      try:
            a						:Union[str, Any] 							=       getattr(UpperCAmelCase_ ,  model_args.task_type						)
            a						: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''' ,  UpperCAmelCase_						)

      # Set seed
      set_seed(training_args.seed						)

      # Prepare CONLL-2003 task
      a						:Optional[int] 							=       token_classification_task.get_labels(data_args.labels						)
      a						:Dict[int, str] 							=       dict(enumerate(UpperCAmelCase_						)						)
      a						:str 							=       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						:Dict 							=       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						:Union[str, Any] 							=       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 ,  )
      a						:str 							=       AutoModelForTokenClassification.from_pretrained(
          model_args.model_name_or_path ,  from_tf=bool('''.ckpt''' in model_args.model_name_or_path						) ,  config=UpperCAmelCase_ ,  cache_dir=model_args.cache_dir ,  )

      # Get datasets
      a						:Optional[int] 							=       (
          TokenClassificationDataset(
              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						:int 							=       (
          TokenClassificationDataset(
              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_						:		np.ndarray ,  UpperCAmelCase_						:		np.ndarray						) -> Tuple[List[int], List[int]]:
            a						:str 							=       np.argmax(UpperCAmelCase_ ,  axis=2						)

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

            a						:str 							=       [[] for _ in range(UpperCAmelCase_						)]
            a						:Any 							=       [[] for _ in range(UpperCAmelCase_						)]

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

      # Data collator
      a						:Any 							=       DataCollatorWithPadding(UpperCAmelCase_ ,  pad_to_multiple_of=8						) if training_args.fpaa else None

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

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

            a						:Optional[int] 							=       trainer.evaluate()

            a						:Union[str, Any] 							=       os.path.join(training_args.output_dir ,  '''eval_results.txt'''						)
            if trainer.is_world_process_zero():
                  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						:Tuple 							=       TokenClassificationDataset(
                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						:Tuple 							=       trainer.predict(UpperCAmelCase_						)
            a							,				a						:str 							=       align_predictions(UpperCAmelCase_ ,  UpperCAmelCase_						)

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

        # Save predictions
            a						:Tuple 							=       os.path.join(training_args.output_dir ,  '''test_predictions.txt'''						)
            if trainer.is_world_process_zero():
                  with open(UpperCAmelCase_ ,  '''w'''						) as writer:
                        with open(os.path.join(data_args.data_dir ,  '''test.txt'''						) ,  '''r'''						) as f:
                              token_classification_task.write_predictions_to_file(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)

      return results
def 				__lowerCamelCase						( UpperCAmelCase_						:		List[Any]						):




      """simple docstring"""





      main()


if __name__ == "__main__":
     main()









from __future__ import annotations

import collections
import pprint
from pathlib import Path
def 				__lowerCamelCase						( UpperCAmelCase_						:		str						):




      """simple docstring"""





      return "".join(sorted(UpperCAmelCase_						)						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		str						):




      """simple docstring"""





      return word_by_signature[signature(UpperCAmelCase_						)]


snake_case				:    str       				=     Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
snake_case				:    Optional[int]       				=     sorted({word.strip().lower() for word in data.splitlines()})

snake_case				:    str       				=     collections.defaultdict(list)
for word in word_list:
     word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
     snake_case				:    Optional[int]       				=     {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

     with open('''anagrams.txt''', '''w''') as file:
          file.write('''all_anagrams = \n ''')
          file.write(pprint.pformat(all_anagrams))





