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







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







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




def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				List[Any]:

		"""simple docstring"""






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

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

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

		_UpperCamelCase						    =	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 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  **lowerCAmelCase )		->				Union[str, Any]:

		"""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=lowerCAmelCase )
		_UpperCamelCase						    =	STDataArguments(train_file=lowerCAmelCase						,  infer_file=lowerCAmelCase )
		_UpperCamelCase						    =	STTrainingArguments(output_dir=lowerCAmelCase )
		_UpperCamelCase						    =	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
		_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=lowerCAmelCase )
						os.makedirs(lowerCAmelCase						,  exist_ok=lowerCAmelCase )
		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(lowerCAmelCase )
				assert os.path.exists(lowerCAmelCase )

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

				_UpperCamelCase						    =	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
						_UpperCamelCase						    =	os.path.join(lowerCAmelCase						,  """best-checkpoint""" )
						_UpperCamelCase						    =	os.path.join(lowerCAmelCase						,  """stage-2""" )
						# Update arguments_dict
						_UpperCamelCase						    =	model_path
						_UpperCamelCase						    =	data_files["""train"""]
						_UpperCamelCase						    =	current_output_dir

						_UpperCamelCase						    =	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 )

				_UpperCamelCase						    =	iteration
				_UpperCamelCase						    =	data_dir_format(iteration + 1 )

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

				with open(lowerCAmelCase						,  """r""" ) as f:
						_UpperCamelCase						    =	float(json.load(lowerCAmelCase )[args.eval_metric] )
				_UpperCamelCase						    =	os.path.join(lowerCAmelCase						,  """infer_output_best-checkpoint.csv""" )
				assert os.path.exists(lowerCAmelCase )
				# 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(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()

				_UpperCamelCase						    =	os.path.join(lowerCAmelCase						,  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"""						,  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 typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__	=							{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase__	=							[
		    "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "MCTCTForCTC",
		    "MCTCTModel",
		    "MCTCTPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
		from .feature_extraction_mctct import MCTCTFeatureExtractor
		from .processing_mctct import MCTCTProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
		import sys

		lowerCamelCase__	=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



