
'''simple docstring'''





import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed

class     a (					__magic_name__     ):




				def __init__(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					int=False,     SCREAMING_SNAKE_CASE_						:					Tuple=10_24,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=10_24,     SCREAMING_SNAKE_CASE_						:					List[str]=3.6,     SCREAMING_SNAKE_CASE_						:					Tuple=False,     ):
										snake_case							:  Union[str, Any]			=     tokenizer
										snake_case							:  List[Any]			=     tokenizer.bos_token_id
										snake_case							:  Optional[Any]			=     dataset
										snake_case							:  Optional[int]			=     seq_length
										snake_case							:  Optional[int]			=     0
										snake_case							:  List[Any]			=     infinite
										snake_case							:  Dict			=     0
										snake_case							:  List[str]			=     tokenized

										if self.tokenized:
																snake_case							:  Optional[Any]			=     seq_length * num_of_sequences
																snake_case							:  Dict			=     '''input_ids'''
										else:
																snake_case							:  List[str]			=     seq_length * chars_per_token * num_of_sequences
																snake_case							:  int			=     '''content'''




				def __iter__(				self						:					List[Any]   ):
										snake_case							:  List[Any]			=     iter(self.dataset   )
										snake_case							:  Union[str, Any]			=     True
										while more_examples:
																snake_case,       snake_case							:  Tuple			=     [], 0
																while True:
																						if buffer_len >= self.max_buffer_size:
																												break
																						try:
																												buffer.append(next(SCREAMING_SNAKE_CASE_   )[self.content_field]   )
																												buffer_len += len(buffer[-1]   )
																						except StopIteration:
																												if self.infinite:
																																		snake_case							:  Any			=     iter(self.dataset   )
																																		self.epoch += 1
																																		logger.info(F"""Dataset epoch: {self.epoch}"""   )
																												else:
																																		snake_case							:  int			=     False
																																		break
																if self.tokenized:
																						snake_case							:  int			=     buffer
																else:
																						snake_case							:  str			=     self.tokenizer(SCREAMING_SNAKE_CASE_,     truncation=SCREAMING_SNAKE_CASE_   )['''input_ids''']
																snake_case							:  List[str]			=     []
																for tokenized_input in tokenized_inputs:
																						all_token_ids.extend(tokenized_input + [self.concat_token_id]   )
																for i in range(0,     len(SCREAMING_SNAKE_CASE_   ),     self.seq_length   ):
																						snake_case							:  Optional[Any]			=     all_token_ids[i : i + self.seq_length]
																						if len(SCREAMING_SNAKE_CASE_   ) == self.seq_length:
																												self.current_size += 1
																												yield torch.tensor(SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					Any=10_00   ):
										return ShufflerIterDataPipe(self,     buffer_size=SCREAMING_SNAKE_CASE_   )




def   A			(							A_				:       str							):
						snake_case							:  List[Any]			=     args.model_ckpt.split('''/'''							)[-1]
						snake_case							:  Optional[int]			=     logging.getLogger(__name__							)
						snake_case							:  int			=     Path(args.save_dir							) / '''log/'''
						log_dir.mkdir(exist_ok=A_							)
						snake_case							:  int			=     F"""debug_{accelerator.process_index}.log"""
						logging.basicConfig(
						    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''     ,				datefmt='''%m/%d/%Y %H:%M:%S'''     ,				level=logging.INFO     ,				handlers=[logging.FileHandler(log_dir / filename							), logging.StreamHandler()]     ,				)
						if accelerator.is_main_process:  # we only want to setup logging once
												accelerator.init_trackers(A_     ,				vars(A_							)							)
												snake_case							:  Dict			=     accelerator.trackers[0].run.name
												logger.setLevel(logging.INFO							)
												datasets.utils.logging.set_verbosity_info()
												transformers.utils.logging.set_verbosity_info()
						else:
												snake_case							:  Any			=     ''''''
												logger.setLevel(logging.ERROR							)
												datasets.utils.logging.set_verbosity_error()
												transformers.utils.logging.set_verbosity_error()
						return logger, run_name

def   A			(							A_				:       Optional[Any]							):
						snake_case							:  Dict			=     {'''streaming''': True}
						snake_case							:  Any			=     load_dataset(args.dataset_name_train     ,				split='''train'''     ,				**A_							)
						snake_case							:  Optional[int]			=     train_data.shuffle(buffer_size=args.shuffle_buffer     ,				seed=args.seed							)
						snake_case							:  Tuple			=     load_dataset(args.dataset_name_valid     ,				split='''train'''     ,				**A_							)
						snake_case							:  Optional[int]			=     ConstantLengthDataset(
						    A_     ,				A_     ,				infinite=A_     ,				seq_length=args.seq_length     ,				tokenized=args.tokenized							)
						snake_case							:  Optional[int]			=     ConstantLengthDataset(
						    A_     ,				A_     ,				infinite=A_     ,				seq_length=args.seq_length     ,				tokenized=args.tokenized							)
						snake_case							:  Optional[int]			=     train_dataset.shuffle(buffer_size=args.shuffle_buffer							)
						snake_case							:  Any			=     DataLoader(A_     ,				batch_size=args.train_batch_size     ,				shuffle=A_							)
						snake_case							:  Optional[int]			=     DataLoader(A_     ,				batch_size=args.valid_batch_size							)
						return train_dataloader, eval_dataloader

def   A			(							A_				:       List[Any]     ,				A_				:       Dict     ,				A_				:       Optional[Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]							):
						snake_case,       snake_case							:  Optional[Any]			=     [], []
						for n, p in model.named_parameters():
												if any(nd in n for nd in no_decay							):
																		params_without_wd.append(A_							)
												else:
																		params_with_wd.append(A_							)
						return [
						    {"params": params_with_wd, "weight_decay": args.weight_decay},
						    {"params": params_without_wd, "weight_decay": 0.0},
						]

def   A			(							A_				:       Union[str, Any]     ,				A_				:       Optional[Any]							):
						logger.info(F"""Step {step}: {metrics}"""							)
						if accelerator.is_main_process:
												accelerator.log(A_     ,				A_							)

def   A			(							A_				:       Optional[int]     ,				A_				:       Optional[int]     ,				A_				:       Dict							):
						# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
						snake_case							:  Dict			=     accelerator.unwrap_model(A_							).config
						snake_case							:  Any			=     4 if args.gradient_checkpointing else 3
						snake_case							:  List[Any]			=     args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
						snake_case							:  str			=     24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
						snake_case							:  Dict			=     factor * (
						    1.0
						    + (args.seq_length / (6.0 * config_model.n_embd))
						    + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
						)
						snake_case							:  Dict			=     flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
						return tflops

def   A			(							A_				:       Optional[Any]							):
						model.eval()
						snake_case							:  Optional[int]			=     []
						for step, batch in enumerate(A_							):
												with torch.no_grad():
																		snake_case							:  str			=     model(A_     ,				labels=A_							)
												snake_case							:  Optional[Any]			=     outputs.loss.repeat(args.valid_batch_size							)
												losses.append(accelerator.gather(A_							)							)
												if args.max_eval_steps > 0 and step >= args.max_eval_steps:
																		break
						snake_case							:  int			=     torch.cat(A_							)
						snake_case							:  Optional[int]			=     losses[: eval_dataloader.dataset.current_size].mean()
						try:
												snake_case							:  List[str]			=     torch.exp(A_							)
						except OverflowError:
												snake_case							:  Tuple			=     float('''inf'''							)
						return loss.item(), perplexity.item()


# Settings
UpperCAmelCase            = HfArgumentParser(TrainingArguments)
UpperCAmelCase            = parser.parse_args()

# Accelerator
UpperCAmelCase            = ProjectConfiguration(project_dir=args.save_dir, logging_dir="log")
UpperCAmelCase            = Accelerator(log_with=["wandb", "tensorboard"], project_config=config)
UpperCAmelCase            = {str(k): str(v) for k, v in accelerator.state.__dict__.items()}

UpperCAmelCase            = Namespace(**vars(args), **acc_state)
UpperCAmelCase            = accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
						UpperCAmelCase            = Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
UpperCAmelCase       ,						UpperCAmelCase            = setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
						hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
UpperCAmelCase            = AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
						model.gradient_checkpointing_enable()
UpperCAmelCase            = AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
UpperCAmelCase       ,						UpperCAmelCase            = create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
UpperCAmelCase            = AdamW(get_grouped_params(model, args), lr=args.learning_rate)
UpperCAmelCase            = get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)

def   A			(							):
						return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
UpperCAmelCase       ,						UpperCAmelCase       ,						UpperCAmelCase       ,						UpperCAmelCase            = accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# load in the weights and states from a previous save
if args.resume_from_checkpoint:
						if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
												accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}''')
												accelerator.load_state(args.resume_from_checkpoint)
												UpperCAmelCase            = os.path.basename(args.resume_from_checkpoint)
						else:
												# Get the most recent checkpoint
												UpperCAmelCase            = [f.name for f in os.scandir(args.save_dir) if f.is_dir() and "step" in str(f)]
												dirs.sort(key=os.path.getctime)
												UpperCAmelCase            = dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract the step of the checkpoint to continue from there
						UpperCAmelCase            = os.path.splitext(path)[0]
						UpperCAmelCase            = int(training_difference.replace("step_", ""))

# Train model
model.train()
UpperCAmelCase            = 0
UpperCAmelCase            = time.time()
UpperCAmelCase            = 0
for step, batch in enumerate(train_dataloader, start=1):
						if args.resume_from_checkpoint and step < resume_step:
												continue  # we need to skip steps until we reach the resumed step
						UpperCAmelCase            = model(batch, labels=batch, use_cache=False).loss
						UpperCAmelCase            = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
						loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
						log_metrics(step, {"samples": step * samples_per_step, "loss_per_step/train": loss.item()})
						UpperCAmelCase            = loss / args.gradient_accumulation_steps
						if step % args.gradient_accumulation_steps != 0:
												# Prevent backward from doing gradient all_reduce in every step
												if accelerator.distributed_type == DistributedType.MULTI_GPU:
																		with model.no_sync():
																								accelerator.backward(loss)
												else:
																		accelerator.backward(loss)
						else:
												UpperCAmelCase            = get_lr()
												accelerator.backward(loss)
												accelerator.clip_grad_norm_(model.parameters(), 1.0)
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()
												UpperCAmelCase            = time.time() - t_start
												UpperCAmelCase            = compute_tflops(elapsed_time, accelerator, args)
												log_metrics(
												    step,
												    {
												        "steps": completed_steps,
												        "loss/train": loss_tracking,
												        "lr": lr,
												        "tflops": tflops,
												        "time_per_iteration": elapsed_time,
												    },
												)
												UpperCAmelCase            = time.time()
												UpperCAmelCase            = 0
												completed_steps += 1
						if step % args.save_checkpoint_steps == 0:
												logger.info("Evaluating and saving model checkpoint")
												UpperCAmelCase       ,						UpperCAmelCase            = evaluate(args)
												log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
												accelerator.wait_for_everyone()
												UpperCAmelCase            = os.path.join(args.save_dir, f'''step_{step}''')
												accelerator.save_state(save_dir)
												if accelerator.is_main_process:
																		hf_repo.push_to_hub(commit_message=f'''step {step}''')
												model.train()
						if completed_steps >= args.max_train_steps:
												break

# Evaluate and save the last checkpoint
logger.info("Evaluating and saving model after training")
UpperCAmelCase       ,						UpperCAmelCase            = evaluate(args)
log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
accelerator.wait_for_everyone()
UpperCAmelCase            = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
UpperCAmelCase            = os.path.join(args.save_dir, f'''step_{step}''')
accelerator.save_state(save_dir)
if accelerator.is_main_process:
						hf_repo.push_to_hub(commit_message="final model")








import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union

import datasets
import librosa
import numpy as np
import torch
from lang_trans import arabic
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    trainer_utils,
)


if is_apex_available():
    from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("""1.6"""):
    _UpperCamelCase       :				List[Any]           =			True
    from torch.cuda.amp import autocast


_UpperCamelCase       :				Optional[Any]           =			logging.getLogger(__name__)







@dataclass
class 		_lowerCAmelCase:

  """simple docstring"""







  lowerCamelCase__     	=       field(
      metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''})
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}						,       )
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''})
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''Whether to log verbose messages or not.'''}						,       )
def  __UpperCamelCase    (						snake_case							,			snake_case      )    ->		int:


   '''simple docstring'''







   logging.basicConfig(
       format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''							,			datefmt='''%m/%d/%Y %H:%M:%S'''							,			handlers=[logging.StreamHandler(sys.stdout      )]							,			)
   __A    =  logging.WARNING
   if model_args.verbose_logging:
      __A    =  logging.DEBUG
   elif trainer_utils.is_main_process(training_args.local_rank      ):
      __A    =  logging.INFO
   logger.setLevel(snake_case      )







@dataclass
class 		_lowerCAmelCase:

  """simple docstring"""







  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''})
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''})
  lowerCamelCase__     	=       field(
      default='''train'''						,       metadata={
          '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
      }						,       )
  lowerCamelCase__     	=       field(
      default='''validation'''						,       metadata={
          '''help''': (
              '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
          )
      }						,       )
  lowerCamelCase__     	=       field(
      default='''text'''						,       metadata={'''help''': '''Column in the dataset that contains label (target text). Defaults to \'text\''''}						,       )
  lowerCamelCase__     	=       field(
      default='''file'''						,       metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''}						,       )
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''Resample loaded audio to target feature extractor\'s sampling rate or not.'''}						,       )
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''Filters out examples longer than specified. Defaults to no filtering.'''}						,       )
  lowerCamelCase__     	=       field(
      default='''librispeech'''						,       metadata={
          '''help''': (
              '''Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or'''
              ''' \'buckwalter\'.'''
          )
      }						,       )
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''})
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''The number of processes to use for the preprocessing.'''}						,       )







@dataclass
class 		_lowerCAmelCase:

  """simple docstring"""







  lowerCamelCase__     	=       False
  lowerCamelCase__     	=       None
  lowerCamelCase__     	=       "|"
  lowerCamelCase__     	=       field(default_factory=_a)
  lowerCamelCase__     	=       field(default_factory=_a)
  lowerCamelCase__     	=       None

  @classmethod
  def 						SCREAMING_SNAKE_CASE__			(   cls    ,							UpperCAmelCase      )->   Optional[int]:
     if name == "librispeech":
        return cls()
     if name == "timit":
        return cls(
            do_lower_case=UpperCAmelCase    ,							translation_table=str.maketrans({'''-''': ''' '''}      )    ,							)
     if name == "buckwalter":
        __A    =  {
            '''-''': ''' ''',  # sometimes used to represent pauses
            '''^''': '''v''',  # fixing "tha" in arabic_speech_corpus dataset
        }
        return cls(
            vocab_file=pathlib.Path(UpperCAmelCase      ).parent.joinpath('''vocab/buckwalter.json'''      )    ,							word_delimiter_token='''/'''    ,							translation_table=str.maketrans(UpperCAmelCase      )    ,							words_to_remove={'''sil'''}    ,							untransliterator=arabic.buckwalter.untransliterate    ,							)
     raise ValueError(f"Unsupported orthography: '{name}'."      )

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   str:
     # TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
     if len(self.translation_table      ) > 0:
        __A    =  text.translate(self.translation_table      )
     if len(self.words_to_remove      ) == 0:
        __A    =  ''' '''.join(text.split()      )  # clean up whitespaces
     else:
        __A    =  ''' '''.join(w for w in text.split() if w not in self.words_to_remove      )  # and clean up whilespaces
     return text





  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   WavaVecaProcessor:
     __A    =  WavaVecaFeatureExtractor.from_pretrained(
         model_args.model_name_or_path    ,							cache_dir=model_args.cache_dir      )
     if self.vocab_file:
        __A    =  WavaVecaCTCTokenizer(
            self.vocab_file    ,							cache_dir=model_args.cache_dir    ,							do_lower_case=self.do_lower_case    ,							word_delimiter_token=self.word_delimiter_token    ,							)
     else:
        __A    =  WavaVecaCTCTokenizer.from_pretrained(
            model_args.model_name_or_path    ,							cache_dir=model_args.cache_dir    ,							do_lower_case=self.do_lower_case    ,							word_delimiter_token=self.word_delimiter_token    ,							)
     return WavaVecaProcessor(UpperCAmelCase    ,							UpperCAmelCase      )







@dataclass
class 		_lowerCAmelCase:

  """simple docstring"""







  lowerCamelCase__     	=       42
  lowerCamelCase__     	=       True
  lowerCamelCase__     	=       None
  lowerCamelCase__     	=       None
  lowerCamelCase__     	=       None
  lowerCamelCase__     	=       None

  def __call__(   self    ,							UpperCAmelCase      )->   Dict[str, torch.Tensor]:
     # split inputs and labels since they have to be of different lenghts and need
     # different padding methods
     __A    =  [{'''input_values''': feature['''input_values''']} for feature in features]
     __A    =  [{'''input_ids''': feature['''labels''']} for feature in features]

     __A    =  self.processor.pad(
         UpperCAmelCase    ,							padding=self.padding    ,							max_length=self.max_length    ,							pad_to_multiple_of=self.pad_to_multiple_of    ,							return_tensors='''pt'''    ,							)
     __A    =  self.processor.pad(
         labels=UpperCAmelCase    ,							padding=self.padding    ,							max_length=self.max_length_labels    ,							pad_to_multiple_of=self.pad_to_multiple_of_labels    ,							return_tensors='''pt'''    ,							)

     # replace padding with -100 to ignore loss correctly
     __A    =  labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1      )    ,							-1_00      )

     __A    =  labels

     return batch







class 		_lowerCAmelCase(		_a):

  """simple docstring"""







  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   torch.Tensor:
     model.train()
     __A    =  self._prepare_inputs(UpperCAmelCase      )

     if self.use_amp:
        with autocast():
           __A    =  self.compute_loss(UpperCAmelCase    ,							UpperCAmelCase      )
     else:
        __A    =  self.compute_loss(UpperCAmelCase    ,							UpperCAmelCase      )

     if self.args.n_gpu > 1:
        if model.module.config.ctc_loss_reduction == "mean":
           __A    =  loss.mean()
        elif model.module.config.ctc_loss_reduction == "sum":
           __A    =  loss.sum() / (inputs['''labels'''] >= 0).sum()
        else:
           raise ValueError(f"{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']"      )

     if self.args.gradient_accumulation_steps > 1:
        __A    =  loss / self.args.gradient_accumulation_steps

     if self.use_amp:
        self.scaler.scale(UpperCAmelCase      ).backward()
     elif self.use_apex:
        with amp.scale_loss(UpperCAmelCase    ,							self.optimizer      ) as scaled_loss:
           scaled_loss.backward()
     elif self.deepspeed:
        self.deepspeed.backward(UpperCAmelCase      )
     else:
        loss.backward()

     return loss.detach()
def  __UpperCamelCase    (						)    ->		Any:


   '''simple docstring'''







   __A    =  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )

   __A   ,		__A   ,		__A    =  parser.parse_args_into_dataclasses()
   configure_logger(snake_case							,			snake_case      )

   __A    =  Orthography.from_name(data_args.orthography.lower()      )
   __A    =  orthography.create_processor(snake_case      )
   __A    =  WavaVecaForCTC.from_pretrained(
       model_args.model_name_or_path							,			cache_dir=model_args.cache_dir							,			gradient_checkpointing=training_args.gradient_checkpointing							,			vocab_size=len(processor.tokenizer      )							,			)

   __A    =  datasets.load_dataset(
       data_args.dataset_name							,			data_args.dataset_config_name							,			split=data_args.train_split_name      )
   __A    =  datasets.load_dataset(
       data_args.dataset_name							,			data_args.dataset_config_name							,			split=data_args.validation_split_name      )

   __A    =  datasets.load_metric('''wer'''      )
   __A    =  processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
   __A    =  ''''''.join(t for t in processor.tokenizer.get_vocab().keys() if len(snake_case      ) == 1      )
   __A    =  re.compile(  # remove characters not in vocabulary
       RF"[^\s{re.escape(snake_case      )}]"							,			flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0							,			)
   __A    =  []

   def prepare_example(snake_case      ):  # TODO(elgeish) make use of multiprocessing?
      __A   ,		__A    =  librosa.load(example[data_args.speech_file_column]							,			sr=snake_case      )
      if data_args.max_duration_in_seconds is not None:
         __A    =  len(example['''speech''']      ) / example['''sampling_rate''']
      # Normalize and clean up text; order matters!
      __A    =  orthography.preprocess_for_training(example[data_args.target_text_column]      )
      __A    =  vocabulary_text_cleaner.sub(''''''							,			snake_case      )
      if updated_text != example[data_args.target_text_column]:
         text_updates.append((example[data_args.target_text_column], updated_text)      )
         __A    =  updated_text
      return example

   __A    =  train_dataset.map(snake_case							,			remove_columns=[data_args.speech_file_column]      )
   __A    =  val_dataset.map(snake_case							,			remove_columns=[data_args.speech_file_column]      )

   if data_args.max_duration_in_seconds is not None:

      def filter_by_max_duration(snake_case      ):
         return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

      __A    =  len(snake_case      )
      __A    =  len(snake_case      )
      __A    =  train_dataset.filter(snake_case							,			remove_columns=['''duration_in_seconds''']      )
      __A    =  val_dataset.filter(snake_case							,			remove_columns=['''duration_in_seconds''']      )
      if len(snake_case      ) > old_train_size:
         logger.warning(
             F"Filtered out {len(snake_case      ) - old_train_size} train example(s) longer than"
             F" {data_args.max_duration_in_seconds} second(s)."      )
      if len(snake_case      ) > old_val_size:
         logger.warning(
             F"Filtered out {len(snake_case      ) - old_val_size} validation example(s) longer than"
             F" {data_args.max_duration_in_seconds} second(s)."      )
   logger.info(F"Split sizes: {len(snake_case      )} train and {len(snake_case      )} validation."      )

   logger.warning(F"Updated {len(snake_case      )} transcript(s) using '{data_args.orthography}' orthography rules."      )
   if logger.isEnabledFor(logging.DEBUG      ):
      for original_text, updated_text in text_updates:
         logger.debug(F"Updated text: \"{original_text}\" -> \"{updated_text}\""      )
   __A    =  None

   def prepare_dataset(snake_case      ):
      # check that all files have the correct sampling rate
      assert (
          len(set(batch['''sampling_rate''']      )      ) == 1
      ), F"Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."

      __A    =  processor(
          audio=batch['''speech''']							,			text=batch[data_args.target_text_column]							,			sampling_rate=batch['''sampling_rate'''][0]      )
      batch.update(snake_case      )
      return batch

   __A    =  train_dataset.map(
       snake_case							,			batch_size=training_args.per_device_train_batch_size							,			batched=snake_case							,			num_proc=data_args.preprocessing_num_workers							,			)
   __A    =  val_dataset.map(
       snake_case							,			batch_size=training_args.per_device_train_batch_size							,			batched=snake_case							,			num_proc=data_args.preprocessing_num_workers							,			)

   __A    =  DataCollatorCTCWithPadding(processor=snake_case							,			padding=snake_case      )

   def compute_metrics(snake_case      ):
      __A    =  pred.predictions
      __A    =  np.argmax(snake_case							,			axis=-1      )

      __A    =  processor.tokenizer.pad_token_id

      __A    =  processor.batch_decode(snake_case      )
      # we do not want to group tokens when computing the metrics
      __A    =  processor.batch_decode(pred.label_ids							,			group_tokens=snake_case      )
      if logger.isEnabledFor(logging.DEBUG      ):
         for reference, predicted in zip(snake_case							,			snake_case      ):
            logger.debug(F"reference: \"{reference}\""      )
            logger.debug(F"predicted: \"{predicted}\""      )
            if orthography.untransliterator is not None:
               logger.debug(F"reference (untransliterated): \"{orthography.untransliterator(snake_case      )}\""      )
               logger.debug(F"predicted (untransliterated): \"{orthography.untransliterator(snake_case      )}\""      )

      __A    =  wer_metric.compute(predictions=snake_case							,			references=snake_case      )

      return {"wer": wer}

   if model_args.freeze_feature_extractor:
      model.freeze_feature_extractor()

   __A    =  CTCTrainer(
       model=snake_case							,			data_collator=snake_case							,			args=snake_case							,			compute_metrics=snake_case							,			train_dataset=snake_case							,			eval_dataset=snake_case							,			tokenizer=processor.feature_extractor							,			)

   trainer.train()


if __name__ == "__main__":
    main()

