

"""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


_lowerCAmelCase						=		logging.getLogger(__name__)







@dataclass
class 	__UpperCamelCase    :
  _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 	__UpperCamelCase    :
  _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=1_28							,							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 		lowerCamelCase__		(					):

  '''simple docstring'''



  _lowerCAmelCase       :   List[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.
    _lowerCAmelCase       :   List[str]  =    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
  else:
    _lowerCAmelCase       :   int  =    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       :   List[str]  =    import_module('tasks'   )
  try:
    _lowerCAmelCase       :   Any  =    getattr(_lowerCamelCase ,   model_args.task_type   )
    _lowerCAmelCase       :   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
  _lowerCAmelCase       :   Optional[Any]  =    token_classification_task.get_labels(data_args.labels   )
  _lowerCAmelCase       :   Dict[int, str]  =    dict(enumerate(_lowerCamelCase   )   )
  _lowerCAmelCase       :   Optional[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.

  _lowerCAmelCase       :   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 ,   )
  _lowerCAmelCase       :   List[str]  =    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       :   Optional[int]  =    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
  _lowerCAmelCase       :   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.train ,   )
      if training_args.do_train
      else None
  )
  _lowerCAmelCase       :   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.dev ,   )
      if training_args.do_eval
      else None
  )

  def align_predictions(_lowerCamelCase ,   _lowerCamelCase   ) -> Tuple[List[int], List[int]]:
    _lowerCAmelCase       :   str  =    np.argmax(_lowerCamelCase ,   axis=2   )

    _lowerCAmelCase       :   int  =    preds.shape

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

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

    _lowerCAmelCase       :   List[str]  =    trainer.evaluate()

    _lowerCAmelCase       :   Tuple  =    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:
    _lowerCAmelCase       :   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.test ,   )

    _lowerCAmelCase       :   str  =    trainer.predict(_lowerCamelCase   )
    _lowerCAmelCase       :   List[str]  =    align_predictions(_lowerCamelCase ,   _lowerCamelCase   )

    _lowerCAmelCase       :   Optional[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
    _lowerCAmelCase       :   List[str]  =    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   ):

  '''simple docstring'''



  main()


if __name__ == "__main__":
 main()








"""simple docstring"""






from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

_lowerCAmelCase						=		{"""UserAgent""": UserAgent().random}

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Any  =    script.contents[0]
    _lowerCAmelCase       :   Union[str, Any]  =    json.loads(data[data.find('{"config"'   ) : -1]   )
    return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]







class 	__UpperCamelCase    :

  def __init__(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    F"""https://www.instagram.com/{username}/"""
      _lowerCAmelCase       :   str  =    self.get_json()

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    requests.get(self.url    ,headers=_A      ).text
      _lowerCAmelCase       :   Optional[Any]  =    BeautifulSoup(_A    ,'html.parser'      ).find_all('script'      )
      try:
          return extract_user_profile(scripts[4]      )
      except (json.decoder.JSONDecodeError, KeyError):
          return extract_user_profile(scripts[3]      )

  def __repr__(		self      ):
      '''simple docstring'''

      return F"""{self.__class__.__name__}('{self.username}')"""

  def __str__(		self      ):
      '''simple docstring'''

      return F"""{self.fullname} ({self.username}) is {self.biography}"""

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["username"]

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["full_name"]

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["biography"]

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["business_email"]

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["external_url"]

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["edge_followed_by"]["count"]

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["edge_follow"]["count"]

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["edge_owner_to_timeline_media"]["count"]

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["profile_pic_url_hd"]

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["is_verified"]







  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["is_private"]





def 		lowerCamelCase__		(					_lowerCamelCase = "github"   ):

    '''simple docstring'''



    import os

    if os.environ.get('CI'   ):
        return  # test failing on GitHub Actions
    _lowerCAmelCase       :   Tuple  =    InstagramUser(_lowerCamelCase   )
    assert instagram_user.user_data
    assert isinstance(instagram_user.user_data ,   _lowerCamelCase   )
    assert instagram_user.username == username
    if username != "github":
        return
    assert instagram_user.fullname == "GitHub"
    assert instagram_user.biography == "Built for developers."
    assert instagram_user.number_of_posts > 150
    assert instagram_user.number_of_followers > 120000
    assert instagram_user.number_of_followings > 15
    assert instagram_user.email == "support@github.com"
    assert instagram_user.website == "https://github.com/readme"
    assert instagram_user.profile_picture_url.startswith('https://instagram.'   )
    assert instagram_user.is_verified is True
    assert instagram_user.is_private is False


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 _lowerCAmelCase						=		InstagramUser("""github""")
 print(instagram_user)
 print(F'''{instagram_user.number_of_posts = }''')
 print(F'''{instagram_user.number_of_followers = }''')
 print(F'''{instagram_user.number_of_followings = }''')
 print(F'''{instagram_user.email = }''')
 print(F'''{instagram_user.website = }''')
 print(F'''{instagram_user.profile_picture_url = }''')
 print(F'''{instagram_user.is_verified = }''')
 print(F'''{instagram_user.is_private = }''')





