

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     					=  True
  from torch.cuda.amp import autocast


__UpperCAmelCase     					=  logging.getLogger(__name__)





@dataclass
class A__     :



 """simple docstring"""







 _lowercase  :       Dict  			=    field(
     metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}			)
 _lowercase  :       List[Any]  			=    field(
     default=lowercase__	,		metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}	,		)
 _lowercase  :       str  			=    field(
     default=lowercase__	,		metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}			)
 _lowercase  :       str  			=    field(
     default=lowercase__	,		metadata={'''help''': '''Whether to log verbose messages or not.'''}	,		)







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   str:

     """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					)]		,				)
     _lowerCAmelCase :       Any	   =     logging.WARNING
     if model_args.verbose_logging:
          _lowerCAmelCase :       List[Any]	   =     logging.DEBUG
     elif trainer_utils.is_main_process(training_args.local_rank					):
          _lowerCAmelCase :       str	   =     logging.INFO
     logger.setLevel(_lowerCamelCase					)





@dataclass
class A__     :



 """simple docstring"""







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





@dataclass
class A__     :



 """simple docstring"""







 _lowercase  :       Any  			=    False
 _lowercase  :       Any  			=    None
 _lowercase  :       List[str]  			=    '''|'''
 _lowercase  :       List[str]  			=    field(default_factory=lowercase__			)
 _lowercase  :       Dict  			=    field(default_factory=lowercase__			)
 _lowercase  :       Optional[int]  			=    None

 @classmethod
 def  __magic_name__							(		cls			:						str  ,    A_			:						str							):






      '''simple docstring'''




      if name == "librispeech":
           return cls()
      if name == "timit":
           return cls(
               do_lower_case=A_  ,    translation_table=str.maketrans({"-": " "}							)  ,    )
      if name == "buckwalter":
           _lowerCAmelCase :       int	   =     {
               "-": " ",  # sometimes used to represent pauses
               "^": "v",  # fixing "tha" in arabic_speech_corpus dataset
           }
           return cls(
               vocab_file=pathlib.Path(A_							).parent.joinpath("vocab/buckwalter.json"							)  ,    word_delimiter_token="/"  ,    translation_table=str.maketrans(A_							)  ,    words_to_remove={"sil"}  ,    untransliterator=arabic.buckwalter.untransliterate  ,    )
      raise ValueError(F'''Unsupported orthography: \'{name}\'.'''							)

 def  __magic_name__							(		self			:						Union[str, Any]  ,    A_			:						str							):






      '''simple docstring'''




      if len(self.translation_table							) > 0:
           _lowerCAmelCase :       List[Any]	   =     text.translate(self.translation_table							)
      if len(self.words_to_remove							) == 0:
           _lowerCAmelCase :       List[str]	   =     " ".join(text.split()							)  # clean up whitespaces
      else:
           _lowerCAmelCase :       Dict	   =     " ".join(w for w in text.split() if w not in self.words_to_remove							)  # and clean up whilespaces
      return text


 def  __magic_name__							(		self			:						Optional[Any]  ,    A_			:						ModelArguments							):






      '''simple docstring'''




      _lowerCAmelCase :       List[Any]	   =     WavaVecaFeatureExtractor.from_pretrained(
          model_args.model_name_or_path  ,    cache_dir=model_args.cache_dir							)
      if self.vocab_file:
           _lowerCAmelCase :       Union[str, Any]	   =     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:
           _lowerCAmelCase :       Union[str, Any]	   =     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(A_  ,    A_							)





@dataclass
class A__     :



 """simple docstring"""







 _lowercase  :       List[Any]  			=    42
 _lowercase  :       List[Any]  			=    True
 _lowercase  :       Optional[Any]  			=    None
 _lowercase  :       int  			=    None
 _lowercase  :       List[str]  			=    None
 _lowercase  :       int  			=    None

 def __call__(		self			:						List[str]  ,    A_			:						List[Dict[str, Union[List[int], torch.Tensor]]]							):






      '''simple docstring'''




      _lowerCAmelCase :       Optional[int]	   =     [{"input_values": feature["input_values"]} for feature in features]
      _lowerCAmelCase :       Tuple	   =     [{"input_ids": feature["labels"]} for feature in features]

      _lowerCAmelCase :       Tuple	   =     self.processor.pad(
          A_  ,    padding=self.padding  ,    max_length=self.max_length  ,    pad_to_multiple_of=self.pad_to_multiple_of  ,    return_tensors="pt"  ,    )
      _lowerCAmelCase :       List[str]	   =     self.processor.pad(
          labels=A_  ,    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
      _lowerCAmelCase :       List[str]	   =     labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1							)  ,    -1_0_0							)

      _lowerCAmelCase :       Any	   =     labels

      return batch





class A__     (			lowercase__			):



 """simple docstring"""







 def  __magic_name__							(		self			:						Any  ,    A_			:						nn.Module  ,    A_			:						Dict[str, Union[torch.Tensor, Any]]							):






      '''simple docstring'''




      model.train()
      _lowerCAmelCase :       List[str]	   =     self._prepare_inputs(A_							)

      if self.use_amp:
           with autocast():
                _lowerCAmelCase :       Optional[int]	   =     self.compute_loss(A_  ,    A_							)
      else:
           _lowerCAmelCase :       str	   =     self.compute_loss(A_  ,    A_							)

      if self.args.n_gpu > 1:
           if model.module.config.ctc_loss_reduction == "mean":
                _lowerCAmelCase :       Optional[Any]	   =     loss.mean()
           elif model.module.config.ctc_loss_reduction == "sum":
                _lowerCAmelCase :       Tuple	   =     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:
           _lowerCAmelCase :       Dict	   =     loss / self.args.gradient_accumulation_steps

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

      return loss.detach()







def 						_snake_case		(     )							->   Union[str, Any]:

     """simple docstring"""







     _lowerCAmelCase :       Any	   =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)

     _lowerCAmelCase :       List[Any]	   =     parser.parse_args_into_dataclasses()
     configure_logger(_lowerCamelCase		,				_lowerCamelCase					)

     _lowerCAmelCase :       List[str]	   =     Orthography.from_name(data_args.orthography.lower()					)
     _lowerCAmelCase :       Any	   =     orthography.create_processor(_lowerCamelCase					)
     _lowerCAmelCase :       List[Any]	   =     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					)		,				)

     _lowerCAmelCase :       Optional[int]	   =     datasets.load_dataset(
         data_args.dataset_name		,				data_args.dataset_config_name		,				split=data_args.train_split_name					)
     _lowerCAmelCase :       Optional[int]	   =     datasets.load_dataset(
         data_args.dataset_name		,				data_args.dataset_config_name		,				split=data_args.validation_split_name					)

     _lowerCAmelCase :       int	   =     datasets.load_metric("wer"					)
     _lowerCAmelCase :       int	   =     processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
     _lowerCAmelCase :       Union[str, Any]	   =     "".join(t for t in processor.tokenizer.get_vocab().keys() if len(_lowerCamelCase					) == 1					)
     _lowerCAmelCase :       Optional[int]	   =     re.compile(  # remove characters not in vocabulary
         rf'''[^\s{re.escape(_lowerCamelCase					)}]'''		,				flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0		,				)
     _lowerCAmelCase :       Dict	   =     []

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

     _lowerCAmelCase :       Tuple	   =     train_dataset.map(_lowerCamelCase		,				remove_columns=[data_args.speech_file_column]					)
     _lowerCAmelCase :       Dict	   =     val_dataset.map(_lowerCamelCase		,				remove_columns=[data_args.speech_file_column]					)

     if data_args.max_duration_in_seconds is not None:

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

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

     logger.warning(f'''Updated {len(_lowerCamelCase					)} 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}\"'''					)
     _lowerCAmelCase :       Tuple	   =     None

     def prepare_dataset(SCREAMING_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}.'''

          _lowerCAmelCase :       Tuple	   =     processor(
              audio=batch["speech"]		,				text=batch[data_args.target_text_column]		,				sampling_rate=batch["sampling_rate"][0]					)
          batch.update(_lowerCamelCase					)
          return batch

     _lowerCAmelCase :       List[Any]	   =     train_dataset.map(
         _lowerCamelCase		,				batch_size=training_args.per_device_train_batch_size		,				batched=_lowerCamelCase		,				num_proc=data_args.preprocessing_num_workers		,				)
     _lowerCAmelCase :       Optional[Any]	   =     val_dataset.map(
         _lowerCamelCase		,				batch_size=training_args.per_device_train_batch_size		,				batched=_lowerCamelCase		,				num_proc=data_args.preprocessing_num_workers		,				)

     _lowerCAmelCase :       Optional[int]	   =     DataCollatorCTCWithPadding(processor=_lowerCamelCase		,				padding=_lowerCamelCase					)

     def compute_metrics(SCREAMING_SNAKE_CASE					):
          _lowerCAmelCase :       Dict	   =     pred.predictions
          _lowerCAmelCase :       int	   =     np.argmax(_lowerCamelCase		,				axis=-1					)

          _lowerCAmelCase :       Any	   =     processor.tokenizer.pad_token_id

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

          _lowerCAmelCase :       List[Any]	   =     wer_metric.compute(predictions=_lowerCamelCase		,				references=_lowerCamelCase					)

          return {"wer": wer}

     if model_args.freeze_feature_extractor:
          model.freeze_feature_extractor()

     _lowerCAmelCase :       str	   =     CTCTrainer(
         model=_lowerCamelCase		,				data_collator=_lowerCamelCase		,				args=_lowerCamelCase		,				compute_metrics=_lowerCamelCase		,				train_dataset=_lowerCamelCase		,				eval_dataset=_lowerCamelCase		,				tokenizer=processor.feature_extractor		,				)

     trainer.train()


if __name__ == "__main__":
  main()













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  (					lowercase__				):






							"""simple docstring"""

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

													if self.tokenized:
																			SCREAMING_SNAKE_CASE__  :					Tuple =       seq_length * num_of_sequences
																			SCREAMING_SNAKE_CASE__  :					Dict =       "input_ids"
													else:
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       seq_length * chars_per_token * num_of_sequences
																			SCREAMING_SNAKE_CASE__  :					Any =       "content"

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






							def   A_	(   self					:  Optional[Any]				,						a					:  Optional[Any]=10_00     )						->Union[str, Any]:
													return ShufflerIterDataPipe(self				,						buffer_size=a     )







def    UpperCAmelCase				(  _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					List[Any] =       args.model_ckpt.split("/"  )[-1]
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       logging.getLogger(__name__  )
						SCREAMING_SNAKE_CASE__  :					Tuple =       Path(args.save_dir  ) / "log/"
						log_dir.mkdir(exist_ok=_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					str =       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(_lowerCamelCase      , vars(_lowerCamelCase  )  )
												SCREAMING_SNAKE_CASE__  :					Optional[int] =       accelerator.trackers[0].run.name
												logger.setLevel(logging.INFO  )
												datasets.utils.logging.set_verbosity_info()
												transformers.utils.logging.set_verbosity_info()
						else:
												SCREAMING_SNAKE_CASE__  :					List[Any] =       ""
												logger.setLevel(logging.ERROR  )
												datasets.utils.logging.set_verbosity_error()
												transformers.utils.logging.set_verbosity_error()
						return logger, run_name







def    UpperCAmelCase				(  _lowerCamelCase   :	Dict  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					List[str] =       {"streaming": True}
						SCREAMING_SNAKE_CASE__  :					str =       load_dataset(args.dataset_name_train      , split="train"      , **_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					str =       train_data.shuffle(buffer_size=args.shuffle_buffer      , seed=args.seed  )
						SCREAMING_SNAKE_CASE__  :					Any =       load_dataset(args.dataset_name_valid      , split="train"      , **_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Any =       ConstantLengthDataset(
						    _lowerCamelCase      , _lowerCamelCase      , infinite=_lowerCamelCase      , seq_length=args.seq_length      , tokenized=args.tokenized  )
						SCREAMING_SNAKE_CASE__  :					str =       ConstantLengthDataset(
						    _lowerCamelCase      , _lowerCamelCase      , infinite=_lowerCamelCase      , seq_length=args.seq_length      , tokenized=args.tokenized  )
						SCREAMING_SNAKE_CASE__  :					Any =       train_dataset.shuffle(buffer_size=args.shuffle_buffer  )
						SCREAMING_SNAKE_CASE__  :					Dict =       DataLoader(_lowerCamelCase      , batch_size=args.train_batch_size      , shuffle=_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					List[Any] =       DataLoader(_lowerCamelCase      , batch_size=args.valid_batch_size  )
						return train_dataloader, eval_dataloader







def    UpperCAmelCase				(  _lowerCamelCase   :	Any      , _lowerCamelCase   :	Dict      , _lowerCamelCase   :	List[Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					int =       [], []
						for n, p in model.named_parameters():
												if any(nd in n for nd in no_decay  ):
																		params_without_wd.append(_lowerCamelCase  )
												else:
																		params_with_wd.append(_lowerCamelCase  )
						return [
						    {"params": params_with_wd, "weight_decay": args.weight_decay},
						    {"params": params_without_wd, "weight_decay": 0.0},
						]







def    UpperCAmelCase				(  _lowerCamelCase   :	Union[str, Any]      , _lowerCamelCase   :	List[str]  ):





						'''simple docstring'''


						logger.info(f"""Step {step}: {metrics}"""  )
						if accelerator.is_main_process:
												accelerator.log(_lowerCamelCase      , _lowerCamelCase  )







def    UpperCAmelCase				(  _lowerCamelCase   :	Tuple      , _lowerCamelCase   :	Any      , _lowerCamelCase   :	Optional[Any]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Optional[int] =       accelerator.unwrap_model(_lowerCamelCase  ).config
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       4 if args.gradient_checkpointing else 3
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       factor * (
						    1.0
						    + (args.seq_length / (6.0 * config_model.n_embd))
						    + (tokenizer.vocab_size / (1_6.0 * config_model.n_layer * config_model.n_embd))
						)
						SCREAMING_SNAKE_CASE__  :					Tuple =       flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
						return tflops







def    UpperCAmelCase				(  _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


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


# Settings
__lowercase							:List[Any]							=	HfArgumentParser(TrainingArguments)
__lowercase							:Any							=	parser.parse_args()

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

__lowercase							:List[Any]							=	Namespace(**vars(args), **acc_state)
__lowercase							:Optional[Any]							=	accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

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

# Logging
__lowercase							,  __lowercase							:Union[str, Any]							=	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
__lowercase							:Tuple							=	AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
				model.gradient_checkpointing_enable()
__lowercase							:Union[str, Any]							=	AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
__lowercase							,  __lowercase							:Any							=	create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
__lowercase							:int							=	AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__lowercase							:str							=	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    UpperCAmelCase				(  ):





						'''simple docstring'''


						return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
__lowercase							,  __lowercase							,  __lowercase							,  __lowercase							:Dict							=	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)
								__lowercase							:List[str]							=	os.path.basename(args.resume_from_checkpoint)
				else:
								# Get the most recent checkpoint
								__lowercase							:Union[str, Any]							=	[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)
								__lowercase							:Dict							=	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
				# Extract the step of the checkpoint to continue from there
				__lowercase							:Any							=	os.path.splitext(path)[0]
				__lowercase							:str							=	int(training_difference.replace("step_", ""))

# Train model
model.train()
__lowercase							:int							=	0
__lowercase							:List[Any]							=	time.time()
__lowercase							:Any							=	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
				__lowercase							:List[str]							=	model(batch, labels=batch, use_cache=False).loss
				__lowercase							:Union[str, Any]							=	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()})
				__lowercase							:List[Any]							=	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:
								__lowercase							:List[Any]							=	get_lr()
								accelerator.backward(loss)
								accelerator.clip_grad_norm_(model.parameters(), 1.0)
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()
								__lowercase							:str							=	time.time() - t_start
								__lowercase							:List[str]							=	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,
								    },
								)
								__lowercase							:str							=	time.time()
								__lowercase							:Optional[int]							=	0
								completed_steps += 1
				if step % args.save_checkpoint_steps == 0:
								logger.info("Evaluating and saving model checkpoint")
								__lowercase							,  __lowercase							:List[str]							=	evaluate(args)
								log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
								accelerator.wait_for_everyone()
								__lowercase							:int							=	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")
__lowercase							,  __lowercase							:List[str]							=	evaluate(args)
log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
accelerator.wait_for_everyone()
__lowercase							:List[str]							=	accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
__lowercase							:List[Any]							=	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")