






'''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__ =			logging.getLogger(__name__)


@dataclass
class        _UpperCAmelCase						:
    __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        _UpperCAmelCase						:
    __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 		__SCREAMING_SNAKE_CASE					(  ):


   """simple docstring"""




   lowercase_	:    Union[str, 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_	:    Any           =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
   else:
      lowercase_	:    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_	:    Optional[int]           =						import_module("tasks"							)
   try:
      lowercase_	:    int           =						getattr(_UpperCamelCase						,		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"						,		_UpperCamelCase							)

   # Set seed
   set_seed(training_args.seed							)

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

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

   lowercase_	:    Optional[Any]           =						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						,		)
   lowercase_	:    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						,		)
   lowercase_	:    List[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
   lowercase_	:    List[str]           =						(
       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
   )
   lowercase_	:    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.dev						,		)
       if training_args.do_eval
       else None
   )

   def align_predictions(_UpperCamelCase						,		_UpperCamelCase							) -> Tuple[List[int], List[int]]:
      lowercase_	:    int           =						np.argmax(_UpperCamelCase						,		axis=2							)

      lowercase_	:    List[Any]           =						preds.shape

      lowercase_	:    Union[str, Any]           =						[[] for _ in range(_UpperCamelCase							)]
      lowercase_	:    int           =						[[] 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							) -> Dict:
      lowercase_	:    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
   lowercase_	:    Union[str, Any]           =						DataCollatorWithPadding(_UpperCamelCase						,		pad_to_multiple_of=8							) if training_args.fpaa else None

   # Initialize our Trainer
   lowercase_	:    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
   lowercase_	:    Any           =						{}
   if training_args.do_eval:
      logger.info("*** Evaluate ***"							)

      lowercase_	:    int           =						trainer.evaluate()

      lowercase_	:    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:
      lowercase_	:    List[str]           =						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						,		)

      lowercase_	:    List[Any]           =						trainer.predict(_UpperCamelCase							)
      lowercase_	:    Union[str, Any]           =						align_predictions(_UpperCamelCase						,		_UpperCamelCase							)

      lowercase_	:    List[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
      lowercase_	:    Optional[int]           =						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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   main()


if __name__ == "__main__":
 main()












'''simple docstring'''
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return [
       {
           0: [1, 2],
           1: [0, 2],
           2: [0, 1, 3, 5],
           3: [2, 4],
           4: [3],
           5: [2, 6, 8],
           6: [5, 7],
           7: [6, 8],
           8: [5, 7],
       },
       {
           0: [6],
           1: [9],
           2: [4, 5],
           3: [4],
           4: [2, 3],
           5: [2],
           6: [0, 7],
           7: [6],
           8: [],
           9: [1],
       },
       {
           0: [4],
           1: [6],
           2: [],
           3: [5, 6, 7],
           4: [0, 6],
           5: [3, 8, 9],
           6: [1, 3, 4, 7],
           7: [3, 6, 8, 9],
           8: [5, 7],
           9: [5, 7],
       },
       {
           0: [1, 3],
           1: [0, 2, 4],
           2: [1, 3, 4],
           3: [0, 2, 4],
           4: [1, 2, 3],
       },
   ][index]




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Dict           =     0
   lowercase_	:    Optional[Any]           =     len(_UpperCamelCase							)  # No of vertices in graph
   lowercase_	:    Union[str, Any]           =     [0] * n
   lowercase_	:    Optional[int]           =     [False] * n

   def dfs(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):
      lowercase_	:    Union[str, Any]           =     True
      lowercase_	:    Dict           =     id_
      id_ += 1
      for to in graph[at]:
         if to == parent:
            pass
         elif not visited[to]:
            dfs(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		id_							)
            lowercase_	:    str           =     min(low[at]						,		low[to]							)
            if id_ <= low[to]:
               bridges.append((at, to) if at < to else (to, at)							)
         else:
            # This edge is a back edge and cannot be a bridge
            lowercase_	:    Optional[int]           =     min(low[at]						,		low[to]							)

   lowercase_	:    list[tuple[int, int]]           =     []
   for i in range(_UpperCamelCase							):
      if not visited[i]:
         dfs(_UpperCamelCase						,		-1						,		_UpperCamelCase						,		id_							)
   return bridges


if __name__ == "__main__":
 import doctest

 doctest.testmod()




