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

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

@dataclasses.dataclass
class       a		:
							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=__UpperCAmelCase      ,	metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'}      ,	)
							lowercase_		:						Optional[bool]   							=  dataclasses.field(
							    default=__UpperCAmelCase      ,	metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'}      ,	)
							lowercase_		:						Optional[bool]   							=  dataclasses.field(
							    default=__UpperCAmelCase      ,	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=__UpperCAmelCase      ,	metadata={'help': 'Random seed for initialization.'}      ,	)






def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     str			,							UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     List[str]			,							UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     Dict  ):






	"""simple docstring"""


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

	if args.do_filter_by_confidence:
		__lowerCAmelCase         =							dataset.filter(lambda UpperCamelCase  : 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(UpperCamelCase  )  )
		print(UpperCamelCase  )
		__lowerCAmelCase         =							dataset.sort("probability"			,							reverse=UpperCamelCase  )
		__lowerCAmelCase         =							dataset.select(range(UpperCamelCase  )  )

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

	__lowerCAmelCase         =							os.path.join(UpperCamelCase			,							F"train_pseudo.{args.data_file_extension}"  )
	if args.data_file_extension == "csv":
		dataset.to_csv(UpperCamelCase			,							index=UpperCamelCase  )
	else:
		dataset.to_json(UpperCamelCase  )







def 		_UpperCAmelCase   (	UpperCamelCase:     List[Any]			,							UpperCamelCase:     List[str]			,							UpperCamelCase:     List[str]			,							UpperCamelCase:     Any			,							**UpperCamelCase:     str  ):






	"""simple docstring"""


	__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=UpperCamelCase  )
	__lowerCAmelCase         =							STDataArguments(train_file=UpperCamelCase			,							infer_file=UpperCamelCase  )
	__lowerCAmelCase         =							STTrainingArguments(output_dir=UpperCamelCase  )
	__lowerCAmelCase         =							argparse.Namespace()

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

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

    # 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=UpperCamelCase  )
			os.makedirs(UpperCamelCase			,							exist_ok=UpperCamelCase  )
	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(UpperCamelCase  )
		assert os.path.exists(UpperCamelCase  )

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

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

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

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

		__lowerCAmelCase         =							iteration
		__lowerCAmelCase         =							data_dir_format(iteration + 1  )

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

		with open(UpperCamelCase			,							"r"  ) as f:
			__lowerCAmelCase         =							float(json.load(UpperCamelCase  )[args.eval_metric]  )
		__lowerCAmelCase         =							os.path.join(UpperCamelCase			,							"infer_output_best-checkpoint.csv"  )
		assert os.path.exists(UpperCamelCase  )
		# 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(UpperCamelCase			,							exist_ok=UpperCamelCase  )
			shutil.copy(UpperCamelCase			,							os.path.join(UpperCamelCase			,							F"eval_results_iter-{iteration}.json"  )  )
			if os.path.exists(UpperCamelCase  ):
				shutil.copy(UpperCamelCase			,							os.path.join(UpperCamelCase			,							F"test_results_iter-{iteration}.json"  )  )
			create_pseudo_labeled_data(UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase  )
		accelerator.wait_for_everyone()

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








import math
import sys






def 		_UpperCAmelCase   (	UpperCamelCase:     str  ):






	"""simple docstring"""


	__lowerCAmelCase         =							""
	try:
		with open(UpperCamelCase			,							"rb"  ) as binary_file:
			__lowerCAmelCase         =							binary_file.read()
		for dat in data:
			__lowerCAmelCase         =							F"{dat:08b}"
			result += curr_byte
		return result
	except OSError:
		print("File not accessible"  )
		sys.exit()






def 		_UpperCAmelCase   (	UpperCamelCase:     str  ):






	"""simple docstring"""


	__lowerCAmelCase         =							{"0": "0", "1": "1"}
	__lowerCAmelCase   ,						__lowerCAmelCase         =							"", ""
	__lowerCAmelCase         =							len(UpperCamelCase  )

	for i in range(len(UpperCamelCase  )  ):
		curr_string += data_bits[i]
		if curr_string not in lexicon:
			continue

		__lowerCAmelCase         =							lexicon[curr_string]
		result += last_match_id
		__lowerCAmelCase         =							last_match_id + "0"

		if math.loga(UpperCamelCase  ).is_integer():
			__lowerCAmelCase         =							{}
			for curr_key in list(UpperCamelCase  ):
				__lowerCAmelCase         =							lexicon.pop(UpperCamelCase  )
			__lowerCAmelCase         =							new_lex

		__lowerCAmelCase         =							last_match_id + "1"
		index += 1
		__lowerCAmelCase         =							""
	return result






def 		_UpperCAmelCase   (	UpperCamelCase:     str			,							UpperCamelCase:     str  ):






	"""simple docstring"""


	__lowerCAmelCase         =							8
	try:
		with open(UpperCamelCase			,							"wb"  ) as opened_file:
			__lowerCAmelCase         =							[
			    to_write[i : i + byte_length]
			    for i in range(0			,							len(UpperCamelCase  )			,							UpperCamelCase  )
			]

			if len(result_byte_array[-1]  ) % byte_length == 0:
				result_byte_array.append("10000000"  )
			else:
				result_byte_array[-1] += "1" + "0" * (
				    byte_length - len(result_byte_array[-1]  ) - 1
				)

			for elem in result_byte_array[:-1]:
				opened_file.write(int(UpperCamelCase			,							2  ).to_bytes(1			,							byteorder="big"  )  )
	except OSError:
		print("File not accessible"  )
		sys.exit()






def 		_UpperCAmelCase   (	UpperCamelCase:     str  ):






	"""simple docstring"""


	__lowerCAmelCase         =							0
	for letter in data_bits:
		if letter == "1":
			break
		counter += 1

	__lowerCAmelCase         =							data_bits[counter:]
	__lowerCAmelCase         =							data_bits[counter + 1 :]
	return data_bits






def 		_UpperCAmelCase   (	UpperCamelCase:     str			,							UpperCamelCase:     str  ):






	"""simple docstring"""


	__lowerCAmelCase         =							read_file_binary(UpperCamelCase  )
	__lowerCAmelCase         =							remove_prefix(UpperCamelCase  )
	__lowerCAmelCase         =							decompress_data(UpperCamelCase  )
	write_file_binary(UpperCamelCase			,							UpperCamelCase  )


if __name__ == "__main__":
					compress(sys.argv[1], sys.argv[2])






