






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







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







def 				__lowerCamelCase     (							)     ->  Optional[Any]:
   # 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.

   __UpperCamelCase	:			Optional[int]					=					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[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."""							)

   __UpperCamelCase	:			Tuple					=					import_module("""tasks"""							)
   try:
      __UpperCamelCase	:			List[Any]					=					getattr(__lowerCAmelCase					,							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"""					,							__lowerCAmelCase							)

   # Set seed
   set_seed(training_args.seed							)

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

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

   __UpperCamelCase	:			Optional[Any]					=					AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path					,							num_labels=__lowerCAmelCase					,							idalabel=__lowerCAmelCase					,							labelaid={label: i for i, label in enumerate(__lowerCAmelCase							)}					,							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	:			str					=					AutoModelForTokenClassification.from_pretrained(
       model_args.model_name_or_path					,							from_tf=bool(""".ckpt""" in model_args.model_name_or_path							)					,							config=__lowerCAmelCase					,							cache_dir=model_args.cache_dir					,							)

   # Get datasets
   __UpperCamelCase	:			str					=					(
       TokenClassificationDataset(
           token_classification_task=__lowerCAmelCase					,							data_dir=data_args.data_dir					,							tokenizer=__lowerCAmelCase					,							labels=__lowerCAmelCase					,							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	:			List[Any]					=					(
       TokenClassificationDataset(
           token_classification_task=__lowerCAmelCase					,							data_dir=data_args.data_dir					,							tokenizer=__lowerCAmelCase					,							labels=__lowerCAmelCase					,							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(__lowerCAmelCase							:					np.ndarray					,							__lowerCAmelCase							:					np.ndarray							) -> Tuple[List[int], List[int]]:
      __UpperCamelCase	:			Dict					=					np.argmax(__lowerCAmelCase					,							axis=2							)

      __UpperCamelCase	,     __UpperCamelCase	:			Tuple					=					preds.shape

      __UpperCamelCase	:			Tuple					=					[[] for _ in range(__lowerCAmelCase							)]
      __UpperCamelCase	:			Union[str, Any]					=					[[] for _ in range(__lowerCAmelCase							)]

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

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

   # Initialize our Trainer
   __UpperCamelCase	:			str					=					Trainer(
       model=__lowerCAmelCase					,							args=__lowerCAmelCase					,							train_dataset=__lowerCAmelCase					,							eval_dataset=__lowerCAmelCase					,							compute_metrics=__lowerCAmelCase					,							data_collator=__lowerCAmelCase					,							)

   # 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	:			Tuple					=					{}
   if training_args.do_eval:
      logger.info("""*** Evaluate ***"""							)

      __UpperCamelCase	:			int					=					trainer.evaluate()

      __UpperCamelCase	:			str					=					os.path.join(training_args.output_dir					,							"""eval_results.txt"""							)
      if trainer.is_world_process_zero():
         with open(__lowerCAmelCase					,							"""w"""							) as writer:
            logger.info("""***** Eval results *****"""							)
            for key, value in result.items():
               logger.info("""  %s = %s"""					,							__lowerCAmelCase					,							__lowerCAmelCase							)
               writer.write("""%s = %s\n""" % (key, value)							)

         results.update(__lowerCAmelCase							)

    # Predict
   if training_args.do_predict:
      __UpperCamelCase	:			Any					=					TokenClassificationDataset(
          token_classification_task=__lowerCAmelCase					,							data_dir=data_args.data_dir					,							tokenizer=__lowerCAmelCase					,							labels=__lowerCAmelCase					,							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	:			List[Any]					=					trainer.predict(__lowerCAmelCase							)
      __UpperCamelCase	,     __UpperCamelCase	:			List[str]					=					align_predictions(__lowerCAmelCase					,							__lowerCAmelCase							)

      __UpperCamelCase	:			int					=					os.path.join(training_args.output_dir					,							"""test_results.txt"""							)
      if trainer.is_world_process_zero():
         with open(__lowerCAmelCase					,							"""w"""							) as writer:
            for key, value in metrics.items():
               logger.info("""  %s = %s"""					,							__lowerCAmelCase					,							__lowerCAmelCase							)
               writer.write("""%s = %s\n""" % (key, value)							)

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

   return results


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[int]							)     ->  str:
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
       main()











from __future__ import annotations







class 		_A						:

  def __init__(    self						:	List[str]   ,				lowerCamelCase__						:	Any=None			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					data
     __UpperCamelCase	:			Union[str, Any]					=					None

  def __repr__(    self						:	int			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					[]
     __UpperCamelCase	:			Optional[int]					=					self
     while temp:
        string_rep.append(f'{temp.data}'			)
        __UpperCamelCase	:			Dict					=					temp.next
     return "->".join(lowerCamelCase__			)







def 				__lowerCamelCase     (							__lowerCAmelCase							:					list							)     ->  Tuple:
   if not elements_list:
      raise Exception("""The Elements List is empty"""							)

   __UpperCamelCase	:			Dict					=					Node(elements_list[0]							)
   for i in range(1					,							len(__lowerCAmelCase							)							):
      __UpperCamelCase	:			List[str]					=					Node(elements_list[i]							)
      __UpperCamelCase	:			Optional[int]					=					current.next
   return head


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Node							)     ->  None:
   if head_node is not None and isinstance(__lowerCAmelCase					,							__lowerCAmelCase							):
      print_reverse(head_node.next							)
      print(head_node.data							)


def 				__lowerCamelCase     (							)     ->  Union[str, Any]:
   from doctest import testmod

   testmod()

   __UpperCamelCase	:			Any					=					make_linked_list([14, 52, 14, 12, 43]							)
   print("""Linked List:"""							)
   print(__lowerCAmelCase							)
   print("""Elements in Reverse:"""							)
   print_reverse(__lowerCAmelCase							)


if __name__ == "__main__":
       main()



