




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


lowercase__				:     int 		=						logging.getLogger(__name__)

lowercase__				:     Tuple 		=						"pytorch_model.bin"
@dataclasses.dataclass
class 		UpperCAmelCase   :







						'''simple docstring'''





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







@dataclasses.dataclass
class 		UpperCAmelCase   :







						'''simple docstring'''





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







@dataclasses.dataclass
class 		UpperCAmelCase   :







						'''simple docstring'''





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







def 						lowerCamelCase__    (		_A     ,	_A     ,	_A     ,	_A     ,	_A     ,	_A							):







	'''simple docstring'''





	snake_case_		    =     datasets.concatenate_datasets([infer_input, infer_output]     ,	axis=1							)

	if args.do_filter_by_confidence:
		snake_case_		    =     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
		snake_case_		    =     int(eval_result * len(_A							)							)
		print(_A							)
		snake_case_		    =     dataset.sort("probability"     ,	reverse=_A							)
		snake_case_		    =     dataset.select(range(_A							)							)

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

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






def 						lowerCamelCase__    (		_A     ,	_A     ,	_A     ,	_A     ,	**_A							):







	'''simple docstring'''





	snake_case_		    =     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()

	snake_case_		    =     STModelArguments(model_name_or_path=_A							)
	snake_case_		    =     STDataArguments(train_file=_A     ,	infer_file=_A							)
	snake_case_		    =     STTrainingArguments(output_dir=_A							)
	snake_case_		    =     argparse.Namespace()

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

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

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

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

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

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

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

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

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

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

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

		snake_case_		    =     iteration
		snake_case_		    =     data_dir_format(iteration + 1							)

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

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

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

		snake_case_		    =     os.path.join(_A     ,	f"train_pseudo.{args.data_file_extension}"							)

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

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

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






def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	_enforce_args(_A     ,	_A							)
	if n == 0:
		return 0
	snake_case_		    =     float("-inf"							)
	for i in range(1     ,	n + 1							):
		snake_case_		    =     max(
		    _A     ,	prices[i - 1] + naive_cut_rod_recursive(n - i     ,	_A							)							)

	return max_revue


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	_enforce_args(_A     ,	_A							)
	snake_case_		    =     [float("-inf"							) for _ in range(n + 1							)]
	return _top_down_cut_rod_recursive(_A     ,	_A     ,	_A							)


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A							):







	'''simple docstring'''





	if max_rev[n] >= 0:
		return max_rev[n]
	elif n == 0:
		return 0
	else:
		snake_case_		    =     float("-inf"							)
		for i in range(1     ,	n + 1							):
			snake_case_		    =     max(
			    _A     ,	prices[i - 1] + _top_down_cut_rod_recursive(n - i     ,	_A     ,	_A							)     ,	)

		snake_case_		    =     max_revenue

	return max_rev[n]


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	_enforce_args(_A     ,	_A							)

	# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
	# length 0.
	snake_case_		    =     [float("-inf"							) for _ in range(n + 1							)]
	snake_case_		    =     0

	for i in range(1     ,	n + 1							):
		snake_case_		    =     max_rev[i]
		for j in range(1     ,	i + 1							):
			snake_case_		    =     max(_A     ,	prices[j - 1] + max_rev[i - j]							)

		snake_case_		    =     max_revenue_i

	return max_rev[n]


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	if n < 0:
		snake_case_		    =     f"n must be greater than or equal to 0. Got n = {n}"
		raise ValueError(_A							)

	if n > len(_A							):
		snake_case_		    =     (
		    "Each integral piece of rod must have a corresponding price. "
		    f"Got n = {n} but length of prices = {len(_A							)}"
		)
		raise ValueError(_A							)


def 						lowerCamelCase__    (		):







	'''simple docstring'''





	snake_case_		    =     [6, 10, 12, 15, 20, 23]
	snake_case_		    =     len(_A							)

	# the best revenue comes from cutting the rod into 6 pieces, each
	# of length 1 resulting in a revenue of 6 * 6 = 36.
	snake_case_		    =     36

	snake_case_		    =     top_down_cut_rod(_A     ,	_A							)
	snake_case_		    =     bottom_up_cut_rod(_A     ,	_A							)
	snake_case_		    =     naive_cut_rod_recursive(_A     ,	_A							)

	assert expected_max_revenue == max_rev_top_down
	assert max_rev_top_down == max_rev_bottom_up
	assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
		main()
