
"""simple docstring"""





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__magic_name__			=     logging.get_logger(__name__)

__magic_name__			=     {"vocab_file": "sentencepiece.bpe.model"}

__magic_name__			=     {
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

__magic_name__			=     {
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

__magic_name__			=     "▁"
class 		SCREAMING_SNAKE_CASE_					(		_SCREAMING_SNAKE_CASE					):




		"""simple docstring"""





		__lowercase				:    Dict		  =						VOCAB_FILES_NAMES
		__lowercase				:    Optional[Any]		  =						PRETRAINED_VOCAB_FILES_MAP
		__lowercase				:    List[str]		  =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase				:    int		  =						["""input_ids""", """attention_mask"""]




		def __init__(	self				,    lowerCAmelCase__				,    lowerCAmelCase__="<s>"				,    lowerCAmelCase__="</s>"				,    lowerCAmelCase__="</s>"				,    lowerCAmelCase__="<s>"				,    lowerCAmelCase__="<unk>"				,    lowerCAmelCase__="<pad>"				,    lowerCAmelCase__="<mask>"				,    lowerCAmelCase__ = None				,    **lowerCAmelCase__				,    ):
				# Mask token behave like a normal word, i.e. include the space before it
				__SCREAMING_SNAKE_CASE				=					AddedToken(lowerCAmelCase_				,    lstrip=lowerCAmelCase_				,    rstrip=lowerCAmelCase_) if isinstance(lowerCAmelCase_				,    lowerCAmelCase_) else mask_token

				__SCREAMING_SNAKE_CASE				=					{} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    bos_token=lowerCAmelCase_				,    eos_token=lowerCAmelCase_				,    unk_token=lowerCAmelCase_				,    sep_token=lowerCAmelCase_				,    cls_token=lowerCAmelCase_				,    pad_token=lowerCAmelCase_				,    mask_token=lowerCAmelCase_				,    sp_model_kwargs=self.sp_model_kwargs				,    **lowerCAmelCase_				,    )

				__SCREAMING_SNAKE_CASE				=					vocab_file
				__SCREAMING_SNAKE_CASE				=					spm.SentencePieceProcessor(**self.sp_model_kwargs)
				self.sp_model.Load(str(lowerCAmelCase_))

				__SCREAMING_SNAKE_CASE				=					{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

				__SCREAMING_SNAKE_CASE				=					len(self.sp_model) - 1
				__SCREAMING_SNAKE_CASE				=					{v: k for k, v in self.fairseq_tokens_to_ids.items()}




		def       snake_case_	(	self				,    lowerCAmelCase__				,    lowerCAmelCase__ = None):

				if token_ids_a is None:
						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				__SCREAMING_SNAKE_CASE				=					[self.cls_token_id]
				__SCREAMING_SNAKE_CASE				=					[self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep




		def       snake_case_	(	self				,    lowerCAmelCase__				,    lowerCAmelCase__ = None				,    lowerCAmelCase__ = False):
				if already_has_special_tokens:
						return super().get_special_tokens_mask(
						    token_ids_a=lowerCAmelCase_				,    token_ids_a=lowerCAmelCase_				,    already_has_special_tokens=lowerCAmelCase_)

				if token_ids_a is None:
						return [1] + ([0] * len(lowerCAmelCase_)) + [1]
				return [1] + ([0] * len(lowerCAmelCase_)) + [1, 1] + ([0] * len(lowerCAmelCase_)) + [1]




		def       snake_case_	(	self				,    lowerCAmelCase__				,    lowerCAmelCase__ = None):
				__SCREAMING_SNAKE_CASE				=					[self.sep_token_id]
				__SCREAMING_SNAKE_CASE				=					[self.cls_token_id]

				if token_ids_a is None:
						return len(cls + token_ids_a + sep) * [0]
				return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]




		@property
		def       snake_case_	(	self):
				return len(self.sp_model)




		def       snake_case_	(	self):
				__SCREAMING_SNAKE_CASE				=					{self.convert_ids_to_tokens(lowerCAmelCase_): i for i in range(self.vocab_size)}
				vocab.update(self.added_tokens_encoder)
				return vocab




		def       snake_case_	(	self				,    lowerCAmelCase__):
				return self.sp_model.encode(lowerCAmelCase_				,    out_type=lowerCAmelCase_)




		def       snake_case_	(	self				,    lowerCAmelCase__):
				if token in self.fairseq_tokens_to_ids:
						return self.fairseq_tokens_to_ids[token]
				__SCREAMING_SNAKE_CASE				=					self.sp_model.PieceToId(lowerCAmelCase_)

				return spm_id if spm_id else self.unk_token_id




		def       snake_case_	(	self				,    lowerCAmelCase__):
				if index in self.fairseq_ids_to_tokens:
						return self.fairseq_ids_to_tokens[index]
				return self.sp_model.IdToPiece(lowerCAmelCase_)




		def       snake_case_	(	self				,    lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE				=					[]
				__SCREAMING_SNAKE_CASE				=					""""""
				__SCREAMING_SNAKE_CASE				=					False
				for token in tokens:
						# make sure that special tokens are not decoded using sentencepiece model
						if token in self.all_special_tokens:
								if not prev_is_special:
										out_string += " "
								out_string += self.sp_model.decode(lowerCAmelCase_) + token
								__SCREAMING_SNAKE_CASE				=					True
								__SCREAMING_SNAKE_CASE				=					[]
						else:
								current_sub_tokens.append(lowerCAmelCase_)
								__SCREAMING_SNAKE_CASE				=					False
				out_string += self.sp_model.decode(lowerCAmelCase_)
				return out_string.strip()




		def __getstate__(	self):
				__SCREAMING_SNAKE_CASE				=					self.__dict__.copy()
				__SCREAMING_SNAKE_CASE				=					None
				return state




		def __setstate__(	self				,    lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE				=					d

				# for backward compatibility
				if not hasattr(self				,    """sp_model_kwargs"""):
						__SCREAMING_SNAKE_CASE				=					{}

				__SCREAMING_SNAKE_CASE				=					spm.SentencePieceProcessor(**self.sp_model_kwargs)
				self.sp_model.Load(self.vocab_file)




		def       snake_case_	(	self				,    lowerCAmelCase__				,    lowerCAmelCase__ = None):
				if not os.path.isdir(lowerCAmelCase_):
						logger.error(f"Vocabulary path ({save_directory}) should be a directory")
						return
				__SCREAMING_SNAKE_CASE				=					os.path.join(
				    lowerCAmelCase_				,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

				if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCAmelCase_) and os.path.isfile(self.vocab_file):
						copyfile(self.vocab_file				,    lowerCAmelCase_)
				elif not os.path.isfile(self.vocab_file):
						with open(lowerCAmelCase_				,    """wb""") as fi:
								__SCREAMING_SNAKE_CASE				=					self.sp_model.serialized_model_proto()
								fi.write(lowerCAmelCase_)

				return (out_vocab_file,)












"""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


_lowerCAmelCase       =				logging.getLogger(__name__)

_lowerCAmelCase       =				"pytorch_model.bin"



@dataclasses.dataclass
class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	_A							:					str 			=					dataclasses.field(
	    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models."""}    )
	_A							:					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   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




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






@dataclasses.dataclass
class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




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






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__       ):
	A_		:    Tuple						     = datasets.concatenate_datasets([infer_input, infer_output] , axis=1       )

	if args.do_filter_by_confidence:
		A_		:    Tuple						     = dataset.filter(lambda snake_case__       : example["probability"] > args.confidence_threshold       )

	if args.do_filter_by_val_performance:
		assert eval_result >= 0.0 and eval_result <= 1.0
		A_		:    List[Any]						     = int(eval_result * len(snake_case__       )       )
		print(snake_case__       )
		A_		:    int						     = dataset.sort("""probability""" , reverse=snake_case__       )
		A_		:    Union[str, Any]						     = dataset.select(range(snake_case__       )       )

	A_		:    int						     = dataset.remove_columns(["""label""", """probability"""]       )
	A_		:    Any						     = dataset.rename_column("""prediction""" , """label"""       )
	A_		:    str						     = dataset.map(lambda snake_case__       : {"label": idalabel[example["label"]]}       )
	A_		:    Optional[Any]						     = dataset.shuffle(seed=args.seed       )

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






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ , snake_case__ , **snake_case__       ):
	A_		:    Tuple						     = 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()

	A_		:    Optional[int]						     = STModelArguments(model_name_or_path=snake_case__       )
	A_		:    int						     = STDataArguments(train_file=snake_case__ , infer_file=snake_case__       )
	A_		:    Dict						     = STTrainingArguments(output_dir=snake_case__       )
	A_		:    Any						     = argparse.Namespace()

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

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

    # Sanity checks
	A_		:    Any						     = {}
	A_		:    List[Any]						     = 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
	A_		:    Any						     = args.train_file
	A_		:    Optional[Any]						     = args.infer_file

	if args.evaluation_strategy != IntervalStrategy.NO.value:
		assert args.eval_file is not None
		A_		:    Optional[Any]						     = args.eval_file

	for key in data_files:
		A_		:    Union[str, Any]						     = 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:
			A_		:    str						     = 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..."""       )
	A_		:    Tuple						     = F"""{args.output_dir}/self-train_iter-{{}}""".format
	A_		:    Optional[Any]						     = data_dir_format(0       )

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

	A_		:    Dict						     = None
	A_		:    Optional[int]						     = None
	A_		:    List[Any]						     = 0
	A_		:    List[Any]						     = False
	# Show the progress bar
	A_		:    Optional[int]						     = 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       )       ):
		A_		:    int						     = data_dir_format(snake_case__       )
		assert os.path.exists(snake_case__       )

		# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
		# iteration > 0
		A_		:    str						     = os.path.join(snake_case__ , """stage-1"""       )
		A_		:    List[Any]						     = {
		    """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(snake_case__ , snake_case__       ):
				arguments_dict.update({key: value}       )

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

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

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

		A_		:    Optional[Any]						     = iteration
		A_		:    Optional[Any]						     = data_dir_format(iteration + 1       )

		A_		:    Optional[int]						     = AutoConfig.from_pretrained(os.path.join(snake_case__ , """best-checkpoint"""       )       )
		A_		:    str						     = config.idalabel
		A_		:    List[str]						     = os.path.join(snake_case__ , """eval_results_best-checkpoint.json"""       )
		A_		:    Dict						     = os.path.join(snake_case__ , """test_results_best-checkpoint.json"""       )
		assert os.path.exists(snake_case__       )

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

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

		A_		:    str						     = os.path.join(snake_case__ , F"""train_pseudo.{args.data_file_extension}"""       )

		if args.evaluation_strategy != IntervalStrategy.NO.value:
			A_		:    List[str]						     = eval_result

			if best_iteration is None:
				A_		:    Union[str, Any]						     = new_iteration
				A_		:    Optional[Any]						     = new_eval_result
			else:
				if new_eval_result - best_eval_result > args.early_stopping_threshold:
					A_		:    List[Any]						     = new_iteration
					A_		:    Any						     = new_eval_result
					A_		:    Tuple						     = 0
				else:
					if new_eval_result == best_eval_result:
						A_		:    Optional[Any]						     = new_iteration
						A_		:    List[str]						     = new_eval_result
					early_stopping_patience_counter += 1

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

