




'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


a       :						Dict								=		logging.get_logger(__name__)



class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):
			__SCREAMING_SNAKE_CASE    =			["""input_features""", """attention_mask"""]





			def __init__(      self						:    Any      ,		a_						:    Optional[Any]=80      ,		a_						:    Optional[Any]=16_000      ,		a_						:    Tuple=80      ,		a_						:    int=0.0      ,		a_						:    List[Any]=True      ,		a_						:    Union[str, Any]=True      ,		a_						:    List[Any]=True      ,		**a_						:    List[Any]      ,		):



						"""simple docstring"""




						super().__init__(feature_size=a_      ,		sampling_rate=a_      ,		padding_value=a_      ,		**a_							)
						__snake_case			=		num_mel_bins
						__snake_case			=		do_ceptral_normalize
						__snake_case			=		normalize_means
						__snake_case			=		normalize_vars
						__snake_case			=		True





			def 							A (      self						:    Tuple      ,		a_						:    np.ndarray      ,		):



						"""simple docstring"""




						__snake_case			=		waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
						__snake_case			=		torch.from_numpy(a_							).unsqueeze(0							)
						__snake_case			=		ta_kaldi.fbank(a_      ,		num_mel_bins=self.num_mel_bins      ,		sample_frequency=self.sampling_rate							)
						return features.numpy()





			@staticmethod
			def 							A (      a_						:    np.ndarray      ,		a_						:    int      ,		a_						:    Optional[bool] = True      ,		a_						:    Optional[bool] = True      ,		a_						:    float = 0.0      ,		):



						"""simple docstring"""




						if normalize_means:
									__snake_case			=		x[:input_length].mean(axis=0							)
									__snake_case			=		np.subtract(a_      ,		a_							)
						if normalize_vars:
									__snake_case			=		x[:input_length].std(axis=0							)
									__snake_case			=		np.divide(a_      ,		a_							)

						if input_length < x.shape[0]:
									__snake_case			=		padding_value

						# make sure array is in float32
						__snake_case			=		x.astype(np.floataa							)

						return x





			def 							A (      self						:    Union[str, Any]      ,		a_						:    List[np.ndarray]      ,		a_						:    Optional[np.ndarray] = None							):



						"""simple docstring"""




						__snake_case			=		attention_mask.sum(-1							) if attention_mask is not None else [x.shape[0] for x in input_features]
						return [
						    self.utterance_cmvn(a_      ,		a_      ,		self.normalize_means      ,		self.normalize_vars      ,		self.padding_value							)
						    for x, n in zip(a_      ,		a_							)
						]






			def __call__(      self						:    List[Any]      ,		a_						:    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,		a_						:    Union[bool, str, PaddingStrategy] = False      ,		a_						:    Optional[int] = None      ,		a_						:    bool = False      ,		a_						:    Optional[int] = None      ,		a_						:    Optional[Union[str, TensorType]] = None      ,		a_						:    Optional[int] = None      ,		a_						:    Optional[bool] = None      ,		**a_						:    Union[str, Any]      ,		):



						"""simple docstring"""




						if sampling_rate is not None:
									if sampling_rate != self.sampling_rate:
												raise ValueError(
												    f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
												    f''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
												    f''' {self.sampling_rate} and not {sampling_rate}.'''							)
						else:
									logger.warning(
									    "It is strongly recommended to pass the `sampling_rate` argument to this function. "
									    "Failing to do so can result in silent errors that might be hard to debug."							)

						__snake_case			=		isinstance(a_      ,		np.ndarray							) and len(raw_speech.shape							) > 1
						if is_batched_numpy and len(raw_speech.shape							) > 2:
									raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''							)
						__snake_case			=		is_batched_numpy or (
						    isinstance(a_      ,		(list, tuple)							) and (isinstance(raw_speech[0]      ,		(np.ndarray, tuple, list)							))
						)

						if is_batched:
									__snake_case			=		[np.asarray(a_      ,		dtype=np.floataa							) for speech in raw_speech]
						elif not is_batched and not isinstance(a_      ,		np.ndarray							):
									__snake_case			=		np.asarray(a_      ,		dtype=np.floataa							)
						elif isinstance(a_      ,		np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
									__snake_case			=		raw_speech.astype(np.floataa							)

						# always return batch
						if not is_batched:
									__snake_case			=		[raw_speech]

						# extract fbank features
						__snake_case			=		[self._extract_fbank_features(a_							) for waveform in raw_speech]

						# convert into correct format for padding
						__snake_case			=		BatchFeature({"input_features": features}							)

						__snake_case			=		self.pad(
						    a_      ,		padding=a_      ,		max_length=a_      ,		truncation=a_      ,		pad_to_multiple_of=a_      ,		return_attention_mask=a_      ,		**a_      ,		)

						# make sure list is in array format
						__snake_case			=		padded_inputs.get("input_features"							)
						if isinstance(input_features[0]      ,		a_							):
									__snake_case			=		[np.asarray(a_      ,		dtype=np.floataa							) for feature in input_features]

						__snake_case			=		padded_inputs.get("attention_mask"							)
						if attention_mask is not None:
									__snake_case			=		[np.asarray(a_      ,		dtype=np.intaa							) for array in attention_mask]

						# Utterance-level cepstral mean and variance normalization
						if self.do_ceptral_normalize:
									__snake_case			=		(
									    np.array(a_      ,		dtype=np.intaa							)
									    if self._get_padding_strategies(a_      ,		max_length=a_							) is not PaddingStrategy.DO_NOT_PAD
									    else None
									)
									__snake_case			=		self.normalize(
									    padded_inputs["input_features"]      ,		attention_mask=a_							)

						if return_tensors is not None:
									__snake_case			=		padded_inputs.convert_to_tensors(a_							)

						return padded_inputs






"""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


_UpperCamelCase								=		logging.getLogger(__name__)

_UpperCamelCase								=		"pytorch_model.bin"
@dataclasses.dataclass
class 						__UpperCAmelCase    :







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





@dataclasses.dataclass
class 						__UpperCAmelCase    :







			'''simple docstring'''
			_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=__A							,   metadata={'help': 'A csv or a json file containing the validation data.'}     )
			_UpperCamelCase  :						Optional[str]					     =				dataclasses.field(
			    default=__A							,   metadata={'help': 'The name of the task to train on.'}							,   )
			_UpperCamelCase  :						Optional[List[str]]					     =				dataclasses.field(
			    default=__A							,   metadata={'help': 'The list of labels for the task.'}     )





@dataclasses.dataclass
class 						__UpperCAmelCase    :







			'''simple docstring'''
			_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=__A							,   metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'}							,   )
			_UpperCamelCase  :						Optional[bool]					     =				dataclasses.field(
			    default=__A							,   metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'}							,   )
			_UpperCamelCase  :						Optional[bool]					     =				dataclasses.field(
			    default=__A							,   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=__A							,   metadata={'help': 'Random seed for initialization.'}							,   )





def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Optional[int]								=			datasets.concatenate_datasets([infer_input, infer_output]			,  axis=1							)

							if args.do_filter_by_confidence:
														A__       :     Union[str, Any]								=			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
														A__       :     Dict								=			int(eval_result * len(lowerCAmelCase							)							)
														print(lowerCAmelCase							)
														A__       :     Dict								=			dataset.sort("""probability"""			,  reverse=lowerCAmelCase							)
														A__       :     Tuple								=			dataset.select(range(lowerCAmelCase							)							)

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

							A__       :     Any								=			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							):
							A__       :     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()

							A__       :     Union[str, Any]								=			STModelArguments(model_name_or_path=lowerCAmelCase							)
							A__       :     Tuple								=			STDataArguments(train_file=lowerCAmelCase			,  infer_file=lowerCAmelCase							)
							A__       :     List[str]								=			STTrainingArguments(output_dir=lowerCAmelCase							)
							A__       :     Tuple								=			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
							A__       :     Optional[int]								=			{}
							A__       :     Tuple								=			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__       :     Tuple								=			args.train_file
							A__       :     int								=			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__       :     List[str]								=			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__       :     int								=			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__       :     Union[str, Any]								=			F'''{args.output_dir}/self-train_iter-{{}}'''.format
							A__       :     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()

							A__       :     Union[str, Any]								=			None
							A__       :     str								=			None
							A__       :     Union[str, Any]								=			0
							A__       :     List[Any]								=			False
							# Show the progress bar
							A__       :     List[Any]								=			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__       :     List[str]								=			data_dir_format(lowerCAmelCase							)
														assert os.path.exists(lowerCAmelCase							)

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

														A__       :     Union[str, 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
																					A__       :     Union[str, Any]								=			os.path.join(lowerCAmelCase			,  """best-checkpoint"""							)
																					A__       :     str								=			os.path.join(lowerCAmelCase			,  """stage-2"""							)
																					# Update arguments_dict
																					A__       :     Dict								=			model_path
																					A__       :     Optional[Any]								=			data_files["""train"""]
																					A__       :     Optional[Any]								=			current_output_dir

																					A__       :     Union[str, 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							)

														A__       :     List[str]								=			iteration
														A__       :     List[str]								=			data_dir_format(iteration + 1							)

														A__       :     List[Any]								=			AutoConfig.from_pretrained(os.path.join(lowerCAmelCase			,  """best-checkpoint"""							)							)
														A__       :     int								=			config.idalabel
														A__       :     Tuple								=			os.path.join(lowerCAmelCase			,  """eval_results_best-checkpoint.json"""							)
														A__       :     str								=			os.path.join(lowerCAmelCase			,  """test_results_best-checkpoint.json"""							)
														assert os.path.exists(lowerCAmelCase							)

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

														A__       :     int								=			os.path.join(lowerCAmelCase			,  F'''train_pseudo.{args.data_file_extension}'''							)

														if args.evaluation_strategy != IntervalStrategy.NO.value:
																					A__       :     Optional[int]								=			eval_result

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

																												if early_stopping_patience_counter >= args.early_stopping_patience:
																																			A__       :     Tuple								=			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"""							)			,  )





