"""simple docstring"""


import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


UpperCAmelCase__			=    logging.getLogger(__name__)

UpperCAmelCase__			=    """pytorch_model.bin"""


@dataclasses.dataclass
class 							a					:
    _snake_case  :	str  				=				dataclasses.field(
        metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'} )
    _snake_case  :	Optional[str]  				=				dataclasses.field(
        default=lowerCAmelCase_						,				metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'}						,				)



@dataclasses.dataclass
class 							a					:
    _snake_case  :	str  				=				dataclasses.field(metadata={'help': 'A csv or a json file containing the training data.'} )
    _snake_case  :	str  				=				dataclasses.field(metadata={'help': 'A csv or a json file containing the data to predict on.'} )
    _snake_case  :	Optional[str]  				=				dataclasses.field(
        default=lowerCAmelCase_						,				metadata={'help': 'A csv or a json file containing the validation data.'} )
    _snake_case  :	Optional[str]  				=				dataclasses.field(
        default=lowerCAmelCase_						,				metadata={'help': 'The name of the task to train on.'}						,				)
    _snake_case  :	Optional[List[str]]  				=				dataclasses.field(
        default=lowerCAmelCase_						,				metadata={'help': 'The list of labels for the task.'} )



@dataclasses.dataclass
class 							a					:
    _snake_case  :	str  				=				dataclasses.field(
        metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'} )
    _snake_case  :	Optional[str]  				=				dataclasses.field(
        default='accuracy'						,				metadata={'help': 'The evaluation metric used for the task.'} )
    _snake_case  :	Optional[str]  				=				dataclasses.field(
        default='no'						,				metadata={
            'help': 'The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'
        }						,				)
    _snake_case  :	Optional[int]  				=				dataclasses.field(
        default=10						,				metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}						,				)
    _snake_case  :	Optional[float]  				=				dataclasses.field(
        default=0.0						,				metadata={
            'help': 'How much the specified evaluation metric must improve to satisfy early stopping conditions.'
        }						,				)
    _snake_case  :	Optional[bool]  				=				dataclasses.field(
        default=lowerCAmelCase_						,				metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'}						,				)
    _snake_case  :	Optional[bool]  				=				dataclasses.field(
        default=lowerCAmelCase_						,				metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'}						,				)
    _snake_case  :	Optional[bool]  				=				dataclasses.field(
        default=lowerCAmelCase_						,				metadata={'help': 'Whether to fine-tune on labeled data after pseudo training.'}						,				)
    _snake_case  :	Optional[float]  				=				dataclasses.field(
        default=0.0						,				metadata={'help': 'Confidence threshold for pseudo-labeled data filtering.'}						,				)
    _snake_case  :	Optional[int]  				=				dataclasses.field(
        default=1_00						,				metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}						,				)
    _snake_case  :	Optional[int]  				=				dataclasses.field(
        default=lowerCAmelCase_						,				metadata={'help': 'Random seed for initialization.'}						,				)






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase ,lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   datasets.concatenate_datasets([infer_input, infer_output] ,axis=1		)

  if args.do_filter_by_confidence:
    _UpperCAmelCase    			=   dataset.filter(lambda lowercase		: example["probability"] > args.confidence_threshold		)

  if args.do_filter_by_val_performance:
    assert eval_result >= 0.0 and eval_result <= 1.0
    _UpperCAmelCase    			=   int(eval_result * len(lowercase		)		)
    print(lowercase		)
    _UpperCAmelCase    			=   dataset.sort("""probability""" ,reverse=lowercase		)
    _UpperCAmelCase    			=   dataset.select(range(lowercase		)		)

  _UpperCAmelCase    			=   dataset.remove_columns(["""label""", """probability"""]		)
  _UpperCAmelCase    			=   dataset.rename_column("""prediction""" ,"""label"""		)
  _UpperCAmelCase    			=   dataset.map(lambda lowercase		: {"label": idalabel[example["label"]]}		)
  _UpperCAmelCase    			=   dataset.shuffle(seed=args.seed		)

  _UpperCAmelCase    			=   os.path.join(lowercase ,f'''train_pseudo.{args.data_file_extension}'''		)
  if args.data_file_extension == "csv":
    dataset.to_csv(lowercase ,index=lowercase		)
  else:
    dataset.to_json(lowercase		)






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase ,lowercase ,**lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   Accelerator()
  # Make one log on every process with the configuration for debugging.
  logging.basicConfig(
      format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" ,datefmt="""%m/%d/%Y %H:%M:%S""" ,level=logging.INFO ,)
  logger.info(accelerator.state		)

  # Setup logging, we only want one process per machine to log things on the
  # screen. accelerator.is_local_main_process is only True for one process per
  # machine.
  logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR		)

  if accelerator.is_local_main_process:
    datasets.utils.logging.set_verbosity_warning()
    transformers.utils.logging.set_verbosity_info()
  else:
    datasets.utils.logging.set_verbosity_error()
    transformers.utils.logging.set_verbosity_error()

  _UpperCAmelCase    			=   STModelArguments(model_name_or_path=lowercase		)
  _UpperCAmelCase    			=   STDataArguments(train_file=lowercase ,infer_file=lowercase		)
  _UpperCAmelCase    			=   STTrainingArguments(output_dir=lowercase		)
  _UpperCAmelCase    			=   argparse.Namespace()

  for arg_class in (model_args, data_args, training_args):
    for key, value in vars(lowercase		).items():
      setattr(lowercase ,lowercase ,lowercase		)

  for key, value in kwargs.items():
    if hasattr(lowercase ,lowercase		):
      setattr(lowercase ,lowercase ,lowercase		)

    # Sanity checks
  _UpperCAmelCase    			=   {}
  _UpperCAmelCase    			=   None

  # You need to provide the training data and the data to predict on
  assert args.train_file is not None
  assert args.infer_file is not None
  _UpperCAmelCase    			=   args.train_file
  _UpperCAmelCase    			=   args.infer_file

  if args.evaluation_strategy != IntervalStrategy.NO.value:
    assert args.eval_file is not None
    _UpperCAmelCase    			=   args.eval_file

  for key in data_files:
    _UpperCAmelCase    			=   data_files[key].split("""."""		)[-1]
    assert extension in ["csv", "json"], f'''`{key}_file` should be a csv or a json file.'''
    if args.data_file_extension is None:
      _UpperCAmelCase    			=   extension
    else:
      assert extension == args.data_file_extension, f'''`{key}_file` should be a {args.data_file_extension} file`.'''

  assert (
      args.eval_metric in datasets.list_metrics()
  ), f'''{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'''

  # If passed along, set the training seed now.
  if args.seed is not None:
    set_seed(args.seed		)

  logger.info("""Creating the initial data directory for self-training..."""		)
  _UpperCAmelCase    			=   f'''{args.output_dir}/self-train_iter-{{}}'''.format
  _UpperCAmelCase    			=   data_dir_format(0		)

  if accelerator.is_main_process:
    if args.output_dir is not None:
      os.makedirs(args.output_dir ,exist_ok=lowercase		)
      os.makedirs(lowercase ,exist_ok=lowercase		)
  accelerator.wait_for_everyone()

  _UpperCAmelCase    			=   None
  _UpperCAmelCase    			=   None
  _UpperCAmelCase    			=   0
  _UpperCAmelCase    			=   False
  # Show the progress bar
  _UpperCAmelCase    			=   tqdm(range(args.max_selftrain_iterations		) ,disable=not accelerator.is_local_main_process		)

  # Self-train
  for iteration in range(0 ,int(args.max_selftrain_iterations		)		):
    _UpperCAmelCase    			=   data_dir_format(lowercase		)
    assert os.path.exists(lowercase		)

    # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
    # iteration > 0
    _UpperCAmelCase    			=   os.path.join(lowercase ,"""stage-1"""		)
    _UpperCAmelCase    			=   {
        """accelerator""": accelerator,
        """model_name_or_path""": args.model_name_or_path,
        """cache_dir""": args.cache_dir,
        """do_train""": True,
        """train_file""": data_files["""train"""] if iteration == 0 else data_files["""train_pseudo"""],
        """do_eval""": True if args.eval_file is not None else False,
        """eval_file""": data_files["""eval"""],
        """do_predict""": True,
        """infer_file""": data_files["""infer"""],
        """task_name""": args.task_name,
        """label_list""": args.label_list,
        """output_dir""": current_output_dir,
        """eval_metric""": args.eval_metric,
        """evaluation_strategy""": args.evaluation_strategy,
        """early_stopping_patience""": args.early_stopping_patience,
        """early_stopping_threshold""": args.early_stopping_threshold,
        """seed""": args.seed,
    }
    # Add additional training arguments
    for key, value in kwargs.items():
      if key not in arguments_dict and not hasattr(lowercase ,lowercase		):
        arguments_dict.update({key: value}		)

    _UpperCAmelCase    			=   os.path.join(lowercase ,"""best-checkpoint""" ,lowercase		)
    if os.path.exists(lowercase		):
      logger.info(
          """Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.""" ,lowercase ,lowercase ,)
    else:
      logger.info("""***** Running self-training: iteration: %d, stage: 1 *****""" ,lowercase		)
      finetune(**lowercase		)
      accelerator.wait_for_everyone()
      assert os.path.exists(lowercase		)
      logger.info("""Self-training job completed: iteration: %d, stage: 1.""" ,lowercase		)

    if iteration > 0 and args.finetune_on_labeled_data:
      # Stage 2 (optional): fine-tuning on the original labeled data
      _UpperCAmelCase    			=   os.path.join(lowercase ,"""best-checkpoint"""		)
      _UpperCAmelCase    			=   os.path.join(lowercase ,"""stage-2"""		)
      # Update arguments_dict
      _UpperCAmelCase    			=   model_path
      _UpperCAmelCase    			=   data_files["""train"""]
      _UpperCAmelCase    			=   current_output_dir

      _UpperCAmelCase    			=   os.path.join(lowercase ,"""best-checkpoint""" ,lowercase		)
      if os.path.exists(lowercase		):
        logger.info(
            """Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.""" ,lowercase ,lowercase ,)
      else:
        logger.info("""***** Running self-training: iteration: %d, stage: 2 *****""" ,lowercase		)
        finetune(**lowercase		)
        accelerator.wait_for_everyone()
        assert os.path.exists(lowercase		)
        logger.info("""Self-training job completed: iteration: %d, stage: 2.""" ,lowercase		)

    _UpperCAmelCase    			=   iteration
    _UpperCAmelCase    			=   data_dir_format(iteration + 1		)

    _UpperCAmelCase    			=   AutoConfig.from_pretrained(os.path.join(lowercase ,"""best-checkpoint"""		)		)
    _UpperCAmelCase    			=   config.idalabel
    _UpperCAmelCase    			=   os.path.join(lowercase ,"""eval_results_best-checkpoint.json"""		)
    _UpperCAmelCase    			=   os.path.join(lowercase ,"""test_results_best-checkpoint.json"""		)
    assert os.path.exists(lowercase		)

    with open(lowercase ,"""r"""		) as f:
      _UpperCAmelCase    			=   float(json.load(lowercase		)[args.eval_metric]		)
    _UpperCAmelCase    			=   os.path.join(lowercase ,"""infer_output_best-checkpoint.csv"""		)
    assert os.path.exists(lowercase		)
    # Loading the dataset from local csv or json files.
    _UpperCAmelCase    			=   load_dataset(args.data_file_extension ,data_files={"""data""": data_files["""infer"""]}		)["""data"""]
    _UpperCAmelCase    			=   load_dataset("""csv""" ,data_files={"""data""": infer_output_file}		)["""data"""]

    if accelerator.is_main_process:
      os.makedirs(lowercase ,exist_ok=lowercase		)
      shutil.copy(lowercase ,os.path.join(lowercase ,f'''eval_results_iter-{iteration}.json'''		)		)
      if os.path.exists(lowercase		):
        shutil.copy(lowercase ,os.path.join(lowercase ,f'''test_results_iter-{iteration}.json'''		)		)
      create_pseudo_labeled_data(lowercase ,lowercase ,lowercase ,lowercase ,lowercase ,lowercase		)
    accelerator.wait_for_everyone()

    _UpperCAmelCase    			=   os.path.join(lowercase ,f'''train_pseudo.{args.data_file_extension}'''		)

    if args.evaluation_strategy != IntervalStrategy.NO.value:
      _UpperCAmelCase    			=   eval_result

      if best_iteration is None:
        _UpperCAmelCase    			=   new_iteration
        _UpperCAmelCase    			=   new_eval_result
      else:
        if new_eval_result - best_eval_result > args.early_stopping_threshold:
          _UpperCAmelCase    			=   new_iteration
          _UpperCAmelCase    			=   new_eval_result
          _UpperCAmelCase    			=   0
        else:
          if new_eval_result == best_eval_result:
            _UpperCAmelCase    			=   new_iteration
            _UpperCAmelCase    			=   new_eval_result
          early_stopping_patience_counter += 1

        if early_stopping_patience_counter >= args.early_stopping_patience:
          _UpperCAmelCase    			=   True

    progress_bar.update(1		)

    if should_training_stop:
      break

  if best_iteration is not None:
    # Save the best iteration
    logger.info("""Best iteration: %d""" ,lowercase		)
    logger.info("""Best evaluation result: %s = %f""" ,args.eval_metric ,lowercase		)
    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
      shutil.copy(
          os.path.join(lowercase ,f'''eval_results_iter-{iteration}.json'''		) ,os.path.join(lowercase ,"""eval_results_best-iteration.json"""		) ,)
  else:
    # Assume that the last iteration is the best
    logger.info("""Best iteration: %d""" ,args.max_selftrain_iterations - 1		)
    logger.info("""Best evaluation result: %s = %f""" ,args.eval_metric ,lowercase		)
    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
      shutil.copy(
          os.path.join(lowercase ,f'''eval_results_iter-{args.max_selftrain_iterations - 1}.json'''		) ,os.path.join(lowercase ,"""eval_results_best-iteration.json"""		) ,)

"""simple docstring"""


from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

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






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   script.contents[0]
  _UpperCAmelCase    			=   json.loads(data[data.find("""{\"config\""""		) : -1]		)
  return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]


class 							a					:


    def __init__(       self						:	Any ,					__lowerCAmelCase						:	Union[str, Any]       ):
      _UpperCAmelCase    			=   f'''https://www.instagram.com/{username}/'''
      _UpperCAmelCase    			=   self.get_json()


    def   lowerCAmelCase_	(       self						:	int       ):
      _UpperCAmelCase    			=   requests.get(self.url ,					headers=__lowerCAmelCase       ).text
      _UpperCAmelCase    			=   BeautifulSoup(__lowerCAmelCase ,					"""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						:	Dict       ):
      return f'''{self.__class__.__name__}(\'{self.username}\')'''


    def __str__(       self						:	int       ):
      return f'''{self.fullname} ({self.username}) is {self.biography}'''


    @property
    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      return self.user_data["username"]


    @property
    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      return self.user_data["full_name"]


    @property
    def   lowerCAmelCase_	(       self						:	str       ):
      return self.user_data["biography"]


    @property
    def   lowerCAmelCase_	(       self						:	str       ):
      return self.user_data["business_email"]


    @property
    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      return self.user_data["external_url"]


    @property
    def   lowerCAmelCase_	(       self						:	int       ):
      return self.user_data["edge_followed_by"]["count"]


    @property
    def   lowerCAmelCase_	(       self						:	Tuple       ):
      return self.user_data["edge_follow"]["count"]


    @property
    def   lowerCAmelCase_	(       self						:	List[str]       ):
      return self.user_data["edge_owner_to_timeline_media"]["count"]


    @property
    def   lowerCAmelCase_	(       self						:	int       ):
      return self.user_data["profile_pic_url_hd"]


    @property
    def   lowerCAmelCase_	(       self						:	str       ):
      return self.user_data["is_verified"]


    @property
    def   lowerCAmelCase_	(       self						:	int       ):
      return self.user_data["is_private"]






def      __UpperCAmelCase       (      lowercase = "github"		):







  """simple docstring"""
  import os

  if os.environ.get("""CI"""		):
    return  # test failing on GitHub Actions
  _UpperCAmelCase    			=   InstagramUser(lowercase		)
  assert instagram_user.user_data
  assert isinstance(instagram_user.user_data ,lowercase		)
  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 > 1_50
  assert instagram_user.number_of_followers > 12_00_00
  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()
    UpperCAmelCase__			=    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 = }''')
