
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      :				str          = logging.getLogger(__name__)

_UpperCAmelCase      :				List[str]          = "pytorch_model.bin"


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






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






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






def    A (      lowercase					, lowercase					, lowercase					, lowercase					, lowercase					, lowercase				)     ->						Optional[int]:
		'''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    A (      lowercase					, lowercase					, lowercase					, lowercase					, **lowercase				)     ->						int:
		'''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'				)					, )


import re


def    A (      lowercase				)     ->						str:
		'''simple docstring'''

		if len(re.findall('[ATCG]'					, lowercase				)				) != len(lowercase				):
				raise ValueError('Invalid Strand'				)

		return dna.translate(dna.maketrans('ATCG'					, 'TAGC'				)				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
