




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


_A     =			logging.getLogger(__name__)

_A     =			"""pytorch_model.bin"""


@dataclasses.dataclass
class  A		:
		__snake_case									=   dataclasses.field(
		    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'}     )
		__snake_case									=   dataclasses.field(
		    default=lowercase_      ,				metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'}      ,				)







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







@dataclasses.dataclass
class  A		:
		__snake_case									=   dataclasses.field(
		    metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'}     )
		__snake_case									=   dataclasses.field(
		    default='accuracy'      ,				metadata={'help': 'The evaluation metric used for the task.'}     )
		__snake_case									=   dataclasses.field(
		    default='no'      ,				metadata={
		        'help': 'The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]'
		    }      ,				)
		__snake_case									=   dataclasses.field(
		    default=10      ,				metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}      ,				)
		__snake_case									=   dataclasses.field(
		    default=0.0      ,				metadata={
		        'help': 'How much the specified evaluation metric must improve to satisfy early stopping conditions.'
		    }      ,				)
		__snake_case									=   dataclasses.field(
		    default=lowercase_      ,				metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'}      ,				)
		__snake_case									=   dataclasses.field(
		    default=lowercase_      ,				metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'}      ,				)
		__snake_case									=   dataclasses.field(
		    default=lowercase_      ,				metadata={'help': 'Whether to fine-tune on labeled data after pseudo training.'}      ,				)
		__snake_case									=   dataclasses.field(
		    default=0.0      ,				metadata={'help': 'Confidence threshold for pseudo-labeled data filtering.'}      ,				)
		__snake_case									=   dataclasses.field(
		    default=100      ,				metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}      ,				)
		__snake_case									=   dataclasses.field(
		    default=lowercase_      ,				metadata={'help': 'Random seed for initialization.'}      ,				)







def __UpperCamelCase			(       _A ,  _A ,  _A ,  _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  datasets.concatenate_datasets([infer_input, infer_output] ,  axis=1  )

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

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

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

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






def __UpperCamelCase			(       _A ,  _A ,  _A ,  _A ,  **_A  ):
						lowerCAmelCase_      			=  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()

						lowerCAmelCase_      			=  STModelArguments(model_name_or_path=lowerCAmelCase__  )
						lowerCAmelCase_      			=  STDataArguments(train_file=lowerCAmelCase__ ,  infer_file=lowerCAmelCase__  )
						lowerCAmelCase_      			=  STTrainingArguments(output_dir=lowerCAmelCase__  )
						lowerCAmelCase_      			=  argparse.Namespace()

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

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

												# Sanity checks
						lowerCAmelCase_      			=  {}
						lowerCAmelCase_      			=  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
						lowerCAmelCase_      			=  args.train_file
						lowerCAmelCase_      			=  args.infer_file

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

						for key in data_files:
												lowerCAmelCase_      			=  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:
																		lowerCAmelCase_      			=  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...'''  )
						lowerCAmelCase_      			=  f"{args.output_dir}/self-train_iter-{{}}".format
						lowerCAmelCase_      			=  data_dir_format(0  )

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

						lowerCAmelCase_      			=  None
						lowerCAmelCase_      			=  None
						lowerCAmelCase_      			=  0
						lowerCAmelCase_      			=  False
						# Show the progress bar
						lowerCAmelCase_      			=  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  )  ):
												lowerCAmelCase_      			=  data_dir_format(lowerCAmelCase__  )
												assert os.path.exists(lowerCAmelCase__  )

												# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
												# iteration > 0
												lowerCAmelCase_      			=  os.path.join(lowerCAmelCase__ ,  '''stage-1'''  )
												lowerCAmelCase_      			=  {
												    '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(lowerCAmelCase__ ,  lowerCAmelCase__  ):
																								arguments_dict.update({key: value}  )

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

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

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

												lowerCAmelCase_      			=  iteration
												lowerCAmelCase_      			=  data_dir_format(iteration + 1  )

												lowerCAmelCase_      			=  AutoConfig.from_pretrained(os.path.join(lowerCAmelCase__ ,  '''best-checkpoint'''  )  )
												lowerCAmelCase_      			=  config.idalabel
												lowerCAmelCase_      			=  os.path.join(lowerCAmelCase__ ,  '''eval_results_best-checkpoint.json'''  )
												lowerCAmelCase_      			=  os.path.join(lowerCAmelCase__ ,  '''test_results_best-checkpoint.json'''  )
												assert os.path.exists(lowerCAmelCase__  )

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

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

												lowerCAmelCase_      			=  os.path.join(lowerCAmelCase__ ,  f"train_pseudo.{args.data_file_extension}"  )

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

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

																								if early_stopping_patience_counter >= args.early_stopping_patience:
																														lowerCAmelCase_      			=  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''' ,  lowerCAmelCase__  )
												logger.info('''Best evaluation result: %s = %f''' ,  args.eval_metric ,  lowerCAmelCase__  )
												accelerator.wait_for_everyone()
												if accelerator.is_main_process:
																		shutil.copy(
																		    os.path.join(lowerCAmelCase__ ,  f"eval_results_iter-{iteration}.json"  ) ,  os.path.join(lowerCAmelCase__ ,  '''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 ,  lowerCAmelCase__  )
												accelerator.wait_for_everyone()
												if accelerator.is_main_process:
																		shutil.copy(
																		    os.path.join(lowerCAmelCase__ ,  f"eval_results_iter-{args.max_selftrain_iterations - 1}.json"  ) ,  os.path.join(lowerCAmelCase__ ,  '''eval_results_best-iteration.json'''  ) ,  )









"""simple docstring"""







import random

def 				UpperCamelCase_    (    lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				float    ,   lowerCAmelCase__      :				bool = False						)						->   dict:

  """simple docstring"""




  lowerCAmelCase_   :				dict								= {i: [] for i in range(lowerCAmelCase__						)}

  # if probability is greater or equal than 1, then generate a complete graph
  if probability >= 1:
    return complete_graph(lowerCAmelCase__						)
  # if probability is lower or equal than 0, then return a graph without edges
  if probability <= 0:
    return graph

  # for each couple of nodes, add an edge from u to v
  # if the number randomly generated is greater than probability probability
  for i in range(lowerCAmelCase__						):
    for j in range(i + 1    ,   lowerCAmelCase__						):
      if random.random() < probability:
        graph[i].append(lowerCAmelCase__						)
        if not directed:
          # if the graph is undirected, add an edge in from j to i, either
          graph[j].append(lowerCAmelCase__						)
  return graph

def 				UpperCamelCase_    (    lowerCAmelCase__      :				int						)						->   dict:

  """simple docstring"""




  return {
      i: [j for j in range(lowerCAmelCase__						) if i != j] for i in range(lowerCAmelCase__						)
  }


if __name__ == "__main__":
     import doctest

     doctest.testmod()





