


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




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





def 					a__       (					)    ->	Tuple:
    # 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__			:		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.
        UpperCAmelCase__			:		Union[str, Any]													=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
    else:
        UpperCAmelCase__			:		List[str]													=				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__			:		int													=				import_module('''tasks'''						)
    try:
        UpperCAmelCase__			:		int													=				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__			:		Any													=				token_classification_task.get_labels(data_args.labels						)
    UpperCAmelCase__			:		Dict[int, str]													=				dict(enumerate(lowerCAmelCase__						)						)
    UpperCAmelCase__			:		Tuple													=				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__			:		str													=				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__			:		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  ,					)
    UpperCAmelCase__			:		Union[str, Any]													=				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__			:		Optional[int]													=				(
        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__			:		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__  ,					lowerCAmelCase__						) -> Tuple[List[int], List[int]]:
        UpperCAmelCase__			:		Tuple													=				np.argmax(lowerCAmelCase__  ,					axis=2						)

        UpperCAmelCase__			:		Dict													=				preds.shape

        UpperCAmelCase__			:		List[Any]													=				[[] for _ in range(lowerCAmelCase__						)]
        UpperCAmelCase__			:		Optional[int]													=				[[] 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__						) -> Dict:
        UpperCAmelCase__			:		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__			:		List[Any]													=				DataCollatorWithPadding(lowerCAmelCase__  ,					pad_to_multiple_of=8						) if training_args.fpaa else None

    # Initialize our Trainer
    UpperCAmelCase__			:		Tuple													=				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__			:		Optional[Any]													=				{}
    if training_args.do_eval:
        logger.info('''*** Evaluate ***'''						)

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

        UpperCAmelCase__			:		List[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__			:		Tuple													=				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__			:		List[Any]													=				trainer.predict(lowerCAmelCase__						)
        UpperCAmelCase__			:		Dict													=				align_predictions(lowerCAmelCase__  ,					lowerCAmelCase__						)

        UpperCAmelCase__			:		Tuple													=				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__			:		Union[str, 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 					a__       (					lowerCAmelCase__						)    ->	Dict:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
  main()


from __future__ import annotations

from typing import Any



class 		UpperCamelCase__  :
   def __init__(self :					Union[str, Any]					,				snake_case_ :					int   ):
    __a :  Dict						=  num_of_nodes
    __a :  list[list[int]]						=  []
    __a :  dict[int, int]						=  {}
   def       lowerCAmelCase			(self :					Optional[Any]					,				snake_case_ :					int					,				snake_case_ :					int					,				snake_case_ :					int   ):
    self.m_edges.append([u_node, v_node, weight]   )
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					int   ):

    if self.m_component[u_node] == u_node:
     return u_node
    return self.find_component(self.m_component[u_node]   )
   def       lowerCAmelCase			(self :					str					,				snake_case_ :					int   ):

    if self.m_component[u_node] != u_node:
     for k in self.m_component:
      __a :  Optional[int]						=  self.find_component(snake_case_   )
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					list[int]					,				snake_case_ :					int					,				snake_case_ :					int   ):

    if component_size[u_node] <= component_size[v_node]:
     __a :  List[str]						=  v_node
     component_size[v_node] += component_size[u_node]
     self.set_component(snake_case_   )

    elif component_size[u_node] >= component_size[v_node]:
     __a :  Optional[int]						=  self.find_component(snake_case_   )
     component_size[u_node] += component_size[v_node]
     self.set_component(snake_case_   )







   def       lowerCAmelCase			(self :					Optional[Any]   ):
    __a :  str						=  []
    __a :  int						=  0

    __a :  list[Any]						=  [-1] * self.m_num_of_nodes

    # A list of components (initialized to all of the nodes)
    for node in range(self.m_num_of_nodes   ):
     self.m_component.update({node: node}   )
     component_size.append(1   )

    __a :  Union[str, Any]						=  self.m_num_of_nodes

    while num_of_components > 1:
     for edge in self.m_edges:
      __a				,       __a				,       __a :  Optional[Any]						=  edge

      __a :  List[str]						=  self.m_component[u]
      __a :  List[Any]						=  self.m_component[v]

      if u_component != v_component:

       for component in (u_component, v_component):
        if (
            minimum_weight_edge[component] == -1
            or minimum_weight_edge[component][2] > w
        ):
         __a :  str						=  [u, v, w]

     for edge in minimum_weight_edge:
      if isinstance(snake_case_					,				snake_case_   ):
       __a				,       __a				,       __a :  str						=  edge

       __a :  Any						=  self.m_component[u]
       __a :  Any						=  self.m_component[v]

       if u_component != v_component:
        mst_weight += w
        self.union(snake_case_					,				snake_case_					,				snake_case_   )
        print(f"Added edge [{u} - {v}]\nAdded weight: {w}\n"   )
        num_of_components -= 1

     __a :  Optional[int]						=  [-1] * self.m_num_of_nodes
    print(f"The total weight of the minimal spanning tree is: {mst_weight}"   )





def    __UpperCamelCase     (				):
 pass


if __name__ == "__main__":
      import doctest

      doctest.testmod()
