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					:	Optional[int]       =  logging.getLogger(__name__)

__lowerCamelCase					:	str       =  "pytorch_model.bin"






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







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







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







def       _snake_case	(   lowerCAmelCase							:   Optional[int]   , lowerCAmelCase							:   Optional[int]   , lowerCAmelCase							:   Tuple   , lowerCAmelCase							:   Optional[Any]   , lowerCAmelCase							:   List[str]   , lowerCAmelCase							:   str	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]				      =    datasets.concatenate_datasets([infer_input, infer_output]   , axis=1	)

		if args.do_filter_by_confidence:
				SCREAMING_SNAKE_CASE_  :		Dict				      =    dataset.filter(lambda lowerCAmelCase	: example["probability"] > args.confidence_threshold	)

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

		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    dataset.remove_columns(["label", "probability"]	)
		SCREAMING_SNAKE_CASE_  :		Any				      =    dataset.rename_column("prediction"   , "label"	)
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    dataset.map(lambda lowerCAmelCase	: {"label": idalabel[example["label"]]}	)
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    dataset.shuffle(seed=args.seed	)

		SCREAMING_SNAKE_CASE_  :		Tuple				      =    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       _snake_case	(   lowerCAmelCase							:   List[str]   , lowerCAmelCase							:   Dict   , lowerCAmelCase							:   Any   , lowerCAmelCase							:   Dict   , **lowerCAmelCase							:   Union[str, Any]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Dict				      =    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()

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    STModelArguments(model_name_or_path=__lowerCamelCase	)
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    STDataArguments(train_file=__lowerCamelCase   , infer_file=__lowerCamelCase	)
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    STTrainingArguments(output_dir=__lowerCamelCase	)
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    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
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    {}
		SCREAMING_SNAKE_CASE_  :		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
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    args.train_file
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    args.infer_file

		if args.evaluation_strategy != IntervalStrategy.NO.value:
				assert args.eval_file is not None
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    args.eval_file

		for key in data_files:
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    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:
						SCREAMING_SNAKE_CASE_  :		Any				      =    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..."	)
		SCREAMING_SNAKE_CASE_  :		Dict				      =    f'{args.output_dir}/self-train_iter-{{}}'.format
		SCREAMING_SNAKE_CASE_  :		Any				      =    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()

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

				# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
				# iteration > 0
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    os.path.join(__lowerCamelCase   , "stage-1"	)
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    {
				    """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}	)

				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    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
						SCREAMING_SNAKE_CASE_  :		int				      =    os.path.join(__lowerCamelCase   , "best-checkpoint"	)
						SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    os.path.join(__lowerCamelCase   , "stage-2"	)
						# Update arguments_dict
						SCREAMING_SNAKE_CASE_  :		str				      =    model_path
						SCREAMING_SNAKE_CASE_  :		str				      =    data_files["""train"""]
						SCREAMING_SNAKE_CASE_  :		List[Any]				      =    current_output_dir

						SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    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	)

				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    iteration
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    data_dir_format(iteration + 1	)

				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    AutoConfig.from_pretrained(os.path.join(__lowerCamelCase   , "best-checkpoint"	)	)
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    config.idalabel
				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    os.path.join(__lowerCamelCase   , "eval_results_best-checkpoint.json"	)
				SCREAMING_SNAKE_CASE_  :		int				      =    os.path.join(__lowerCamelCase   , "test_results_best-checkpoint.json"	)
				assert os.path.exists(__lowerCamelCase	)

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

				SCREAMING_SNAKE_CASE_  :		int				      =    os.path.join(__lowerCamelCase   , f'train_pseudo.{args.data_file_extension}'	)

				if args.evaluation_strategy != IntervalStrategy.NO.value:
						SCREAMING_SNAKE_CASE_  :		List[Any]				      =    eval_result

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

								if early_stopping_patience_counter >= args.early_stopping_patience:
										SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    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"	)   , )








from ...configuration_utils import PretrainedConfig
from ...utils import logging


a		:       Optional[int]			     = logging.get_logger(__name__)

a		:       List[Any]			     = {
    "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : List[Any]							       =	'xglm'
      a  : str							       =	['past_key_values']

      a  : Any							       =	{
          'num_attention_heads': 'attention_heads',
          'hidden_size': 'd_model',
          'num_hidden_layers': 'num_layers',
      }
      def __init__(      self					:			Optional[int]		,      __lowercase					:			int=256008		,      __lowercase					:			Tuple=2048		,      __lowercase					:			List[Any]=1024		,      __lowercase					:			str=4096		,      __lowercase					:			Optional[Any]=24		,      __lowercase					:			Optional[int]=16		,      __lowercase					:			List[Any]="gelu"		,      __lowercase					:			str=0.1		,      __lowercase					:			Dict=0.1		,      __lowercase					:			Tuple=0.0		,      __lowercase					:			Optional[int]=0.0		,      __lowercase					:			Dict=0.02		,      __lowercase					:			Optional[int]=True		,      __lowercase					:			Any=True		,      __lowercase					:			Dict=2		,      __lowercase					:			Optional[Any]=1		,      __lowercase					:			List[Any]=0		,      __lowercase					:			Optional[Any]=2		,      **__lowercase					:			List[str]		,      )							->      Optional[int]:
        __UpperCAmelCase							:	List[str]           =  vocab_size
        __UpperCAmelCase							:	Optional[Any]           =  max_position_embeddings
        __UpperCAmelCase							:	Optional[Any]           =  d_model
        __UpperCAmelCase							:	str           =  ffn_dim
        __UpperCAmelCase							:	List[str]           =  num_layers
        __UpperCAmelCase							:	Dict           =  attention_heads
        __UpperCAmelCase							:	str           =  activation_function
        __UpperCAmelCase							:	Optional[Any]           =  dropout
        __UpperCAmelCase							:	Any           =  attention_dropout
        __UpperCAmelCase							:	int           =  activation_dropout
        __UpperCAmelCase							:	Tuple           =  layerdrop
        __UpperCAmelCase							:	Tuple           =  init_std
        __UpperCAmelCase							:	List[str]           =  scale_embedding  # scale factor will be sqrt(d_model) if True
        __UpperCAmelCase							:	Union[str, Any]           =  use_cache

        super().__init__(
            pad_token_id=__lowercase		,      bos_token_id=__lowercase		,      eos_token_id=__lowercase		,      decoder_start_token_id=__lowercase		,      **__lowercase		,      )


