



"""simple docstring"""







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'):
 __snake_case	:				Optional[int]  					=			True
 from torch.cuda.amp import autocast


__snake_case	:				Any  					=			logging.getLogger(__name__)

@dataclass
class       UpperCamelCase  :





     """simple docstring"""






     _lowerCamelCase					: str				   =field(
         metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}				)
     _lowerCamelCase					: Optional[str]				   =field(
         default=a      , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}      , )
     _lowerCamelCase					: Optional[bool]				   =field(
         default=a      , metadata={"help": "Whether to freeze the feature extractor layers of the model."}				)
     _lowerCamelCase					: Optional[bool]				   =field(
         default=a      , metadata={"help": "Whether to log verbose messages or not."}      , )



def   a_	(			__a		,							__a      ):
     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(__a      )

@dataclass
class       UpperCamelCase  :





     """simple docstring"""






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

@dataclass
class       UpperCamelCase  :





     """simple docstring"""






     _lowerCamelCase					: bool				   =False
     _lowerCamelCase					: Optional[str]				   =None
     _lowerCamelCase					: Optional[str]				   ="|"
     _lowerCamelCase					: Optional[Dict[str, str]]				   =field(default_factory=a				)
     _lowerCamelCase					: Optional[Set[str]]				   =field(default_factory=a				)
     _lowerCamelCase					: Optional[Callable[[str], str]]				   =None





     @classmethod
     def 						A__  (					cls :							int	,      _lowerCamelCase :							str ):
          if name == "librispeech":
               return cls()
          if name == "timit":
               return cls(
                   do_lower_case=_lowerCamelCase	,      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(_lowerCamelCase ).parent.joinpath('''vocab/buckwalter.json''' )	,      word_delimiter_token='''/'''	,      translation_table=str.maketrans(_lowerCamelCase )	,      words_to_remove={'''sil'''}	,      untransliterator=arabic.buckwalter.untransliterate	,      )
          raise ValueError(F'''Unsupported orthography: \'{name}\'.''' )





     def 						A__  (					self :							Any	,      _lowerCamelCase :							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 						A__  (					self :							List[str]	,      _lowerCamelCase :							ModelArguments ):
          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(_lowerCamelCase	,      _lowerCamelCase )

@dataclass
class       UpperCamelCase  :





     """simple docstring"""






     _lowerCamelCase					: WavaVecaProcessor
     _lowerCamelCase					: Union[bool, str]				   =True
     _lowerCamelCase					: Optional[int]				   =None
     _lowerCamelCase					: Optional[int]				   =None
     _lowerCamelCase					: Optional[int]				   =None
     _lowerCamelCase					: Optional[int]				   =None





     def __call__(					self :							Dict	,      _lowerCamelCase :							List[Dict[str, Union[List[int], 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(
              _lowerCamelCase	,      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=_lowerCamelCase	,      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_0_0 )

          A__				=   labels

          return batch

class       UpperCamelCase  (					a				):





     """simple docstring"""






     def 						A__  (					self :							int	,      _lowerCamelCase :							nn.Module	,      _lowerCamelCase :							Dict[str, Union[torch.Tensor, Any]] ):
          model.train()
          A__				=   self._prepare_inputs(_lowerCamelCase )

          if self.use_amp:
               with autocast():
                    A__				=   self.compute_loss(_lowerCamelCase	,      _lowerCamelCase )
          else:
               A__				=   self.compute_loss(_lowerCamelCase	,      _lowerCamelCase )

          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(_lowerCamelCase ).backward()
          elif self.use_apex:
               with amp.scale_loss(_lowerCamelCase	,      self.optimizer ) as scaled_loss:
                    scaled_loss.backward()
          elif self.deepspeed:
               self.deepspeed.backward(_lowerCamelCase )
          else:
               loss.backward()

          return loss.detach()



def   a_	(			):
     # See all possible arguments in src/transformers/training_args.py
     # or by passing the --help flag to this script.
     # We now keep distinct sets of args, for a cleaner separation of concerns.

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

     A__	,     A__	,     A__				=   parser.parse_args_into_dataclasses()
     configure_logger(__a		,							__a      )

     A__				=   Orthography.from_name(data_args.orthography.lower()      )
     A__				=   orthography.create_processor(__a      )
     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(__a      ) == 1      )
     A__				=   re.compile(  # remove characters not in vocabulary
         rf'''[^\s{re.escape(__a      )}]'''		,							flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0		,							)
     A__				=   []

     def prepare_example(__a      ):  # TODO(elgeish) make use of multiprocessing?
          A__	,     A__				=   librosa.load(example[data_args.speech_file_column]		,							sr=__a      )
          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(''''''		,							__a      )
          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(__a		,							remove_columns=[data_args.speech_file_column]      )
     A__				=   val_dataset.map(__a		,							remove_columns=[data_args.speech_file_column]      )

     if data_args.max_duration_in_seconds is not None:

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

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

     logger.warning(f'''Updated {len(__a      )} 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(__a      ):
          # 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(__a      )
          return batch

     A__				=   train_dataset.map(
         __a		,							batch_size=training_args.per_device_train_batch_size		,							batched=__a		,							num_proc=data_args.preprocessing_num_workers		,							)
     A__				=   val_dataset.map(
         __a		,							batch_size=training_args.per_device_train_batch_size		,							batched=__a		,							num_proc=data_args.preprocessing_num_workers		,							)

     A__				=   DataCollatorCTCWithPadding(processor=__a		,							padding=__a      )

     def compute_metrics(__a      ):
          A__				=   pred.predictions
          A__				=   np.argmax(__a		,							axis=-1      )

          A__				=   processor.tokenizer.pad_token_id

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

          A__				=   wer_metric.compute(predictions=__a		,							references=__a      )

          return {"wer": wer}

     if model_args.freeze_feature_extractor:
          model.freeze_feature_extractor()

     A__				=   CTCTrainer(
         model=__a		,							data_collator=__a		,							args=__a		,							compute_metrics=__a		,							train_dataset=__a		,							eval_dataset=__a		,							tokenizer=processor.feature_extractor		,							)

     trainer.train()


if __name__ == "__main__":
 main()








"""simple docstring"""







import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__snake_case	:				Tuple  					=			16
__snake_case	:				Any  					=			32



def   a_	(			__a		,							__a      ):
     # Initialize accelerator
     if args.with_tracking:
          A__				=   Accelerator(
              cpu=args.cpu		,							mixed_precision=args.mixed_precision		,							log_with='''all'''		,							project_dir=args.project_dir      )
     else:
          A__				=   Accelerator(cpu=args.cpu		,							mixed_precision=args.mixed_precision      )

     if hasattr(args.checkpointing_steps		,							'''isdigit'''      ):
          if args.checkpointing_steps == "epoch":
               A__				=   args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               A__				=   int(args.checkpointing_steps      )
          else:
               raise ValueError(
                   f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''      )
     else:
          A__				=   None
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     A__				=   config['''lr''']
     A__				=   int(config['''num_epochs''']      )
     A__				=   int(config['''seed''']      )
     A__				=   int(config['''batch_size''']      )

     # We need to initialize the trackers we use, and also store our configuration
     if args.with_tracking:
          A__				=   os.path.split(__a      )[-1].split('''.'''      )[0]
          accelerator.init_trackers(__a		,							__a      )

     A__				=   AutoTokenizer.from_pretrained('''bert-base-cased'''      )
     A__				=   load_dataset('''glue'''		,							'''mrpc'''      )
     A__				=   evaluate.load('''glue'''		,							'''mrpc'''      )

     def tokenize_function(__a      ):
          # max_length=None => use the model max length (it's actually the default)
          A__				=   tokenizer(examples['''sentence1''']		,							examples['''sentence2''']		,							truncation=__a		,							max_length=__a      )
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     # starting with the main process first:
     with accelerator.main_process_first():
          A__				=   datasets.map(
              __a		,							batched=__a		,							remove_columns=['''idx''', '''sentence1''', '''sentence2''']		,							)

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     A__				=   tokenized_datasets.rename_column('''label'''		,							'''labels'''      )

     # If the batch size is too big we use gradient accumulation
     A__				=   1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          A__				=   batch_size // MAX_GPU_BATCH_SIZE
          A__				=   MAX_GPU_BATCH_SIZE

     def collate_fn(__a      ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          A__				=   128 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               A__				=   16
          elif accelerator.mixed_precision != "no":
               A__				=   8
          else:
               A__				=   None

          return tokenizer.pad(
              __a		,							padding='''longest'''		,							max_length=__a		,							pad_to_multiple_of=__a		,							return_tensors='''pt'''		,							)

     # Instantiate dataloaders.
     A__				=   DataLoader(
         tokenized_datasets['''train''']		,							shuffle=__a		,							collate_fn=__a		,							batch_size=__a      )
     A__				=   DataLoader(
         tokenized_datasets['''validation''']		,							shuffle=__a		,							collate_fn=__a		,							batch_size=__a      )

     set_seed(__a      )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     A__				=   AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''		,							return_dict=__a      )

     # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
     # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
     # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
     A__				=   model.to(accelerator.device      )

     # Instantiate optimizer
     A__				=   AdamW(params=model.parameters()		,							lr=__a      )

     # Instantiate scheduler
     A__				=   get_linear_schedule_with_warmup(
         optimizer=__a		,							num_warmup_steps=100		,							num_training_steps=(len(__a      ) * num_epochs) // gradient_accumulation_steps		,							)

     # Prepare everything
     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
     # prepare method.
     A__	,     A__	,     A__	,     A__	,     A__				=   accelerator.prepare(
         __a		,							__a		,							__a		,							__a		,							__a      )

     # We need to keep track of how many total steps we have iterated over
     A__				=   0
     # We also need to keep track of the stating epoch so files are named properly
     A__				=   0

     # Potentially 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      )
               A__				=   os.path.basename(args.resume_from_checkpoint      )
          else:
               # Get the most recent checkpoint
               A__				=   [f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
               dirs.sort(key=os.path.getctime      )
               A__				=   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          A__				=   os.path.splitext(__a      )[0]

          if "epoch" in training_difference:
               A__				=   int(training_difference.replace('''epoch_'''		,							''''''      )      ) + 1
               A__				=   None
          else:
               A__				=   int(training_difference.replace('''step_'''		,							''''''      )      )
               A__				=   resume_step // len(__a      )
               resume_step -= starting_epoch * len(__a      )

    # Now we train the model
     for epoch in range(__a		,							__a      ):
          model.train()
          if args.with_tracking:
               A__				=   0
          if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
               # We need to skip steps until we reach the resumed step
               A__				=   accelerator.skip_first_batches(__a		,							__a      )
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               A__				=   train_dataloader
          for step, batch in enumerate(__a      ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device      )
               A__				=   model(**__a      )
               A__				=   outputs.loss
               A__				=   loss / gradient_accumulation_steps
               # We keep track of the loss at each epoch
               if args.with_tracking:
                    total_loss += loss.detach().float()
               accelerator.backward(__a      )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

               if isinstance(__a		,							__a      ):
                    A__				=   f'''step_{overall_step}'''
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              A__				=   os.path.join(args.output_dir		,							__a      )
                         accelerator.save_state(__a      )

          model.eval()
          for step, batch in enumerate(__a      ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device      )
               with torch.no_grad():
                    A__				=   model(**__a      )
               A__				=   outputs.logits.argmax(dim=-1      )
               A__	,     A__				=   accelerator.gather_for_metrics((predictions, batch['''labels'''])      )
               metric.add_batch(
                   predictions=__a		,							references=__a		,							)

          A__				=   metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'''epoch {epoch}:'''		,							__a      )
          if args.with_tracking:
               accelerator.log(
                   {
                       '''accuracy''': eval_metric['''accuracy'''],
                       '''f1''': eval_metric['''f1'''],
                       '''train_loss''': total_loss.item() / len(__a      ),
                       '''epoch''': epoch,
                   }		,							step=__a		,							)

          if checkpointing_steps == "epoch":
               A__				=   f'''epoch_{epoch}'''
               if args.output_dir is not None:
                    A__				=   os.path.join(args.output_dir		,							__a      )
               accelerator.save_state(__a      )

     if args.with_tracking:
          accelerator.end_training()



def   a_	(			):
     A__				=   argparse.ArgumentParser(description='''Simple example of training script.'''      )
     parser.add_argument(
         '''--mixed_precision'''		,							type=__a		,							default=__a		,							choices=['''no''', '''fp16''', '''bf16''', '''fp8''']		,							help='''Whether to use mixed precision. Choose'''
         '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
         '''and an Nvidia Ampere GPU.'''		,							)
     parser.add_argument('''--cpu'''		,							action='''store_true'''		,							help='''If passed, will train on the CPU.'''      )
     parser.add_argument(
         '''--checkpointing_steps'''		,							type=__a		,							default=__a		,							help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''		,							)
     parser.add_argument(
         '''--resume_from_checkpoint'''		,							type=__a		,							default=__a		,							help='''If the training should continue from a checkpoint folder.'''		,							)
     parser.add_argument(
         '''--with_tracking'''		,							action='''store_true'''		,							help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''		,							)
     parser.add_argument(
         '''--output_dir'''		,							type=__a		,							default='''.'''		,							help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''		,							)
     parser.add_argument(
         '''--project_dir'''		,							type=__a		,							default='''logs'''		,							help='''Location on where to store experiment tracking logs` and relevent project information'''		,							)
     A__				=   parser.parse_args()
     A__				=   {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(__a		,							__a      )


if __name__ == "__main__":
 main()



