






import argparse
import gc
import os
import sys
from pathlib import Path
from typing import List  # noqa: F401

import pytorch_lightning as pl
import torch
from finetune import SummarizationModule, TranslationModule
from finetune import main as ft_main
from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
from torch import nn

from transformers import AutoModelForSeqaSeqLM, MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import generic_train  # noqa
class 	a_(		lowercase__	):



     """simple docstring"""



     __snake_case		:				Tuple  					=['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']





     def __init__(		self					:	Optional[int]  ,  lowerCAmelCase__					:	Optional[Any])							->	int:

      """simple docstring"""
      assert Path(hparams.data_dir).exists()
      SCREAMING_SNAKE_CASE				    =		Path(hparams.output_dir)
      self.output_dir.mkdir(exist_ok=lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		self.output_dir.joinpath('student')

      SCREAMING_SNAKE_CASE				    =		str(lowerCAmelCase__)  # Tell lightning we are training the student
      SCREAMING_SNAKE_CASE				    =		AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher).eval()
      use_task_specific_params(lowerCAmelCase__  ,  hparams.task)  # We copy good generation parameters to student by default
      if hparams.student is not None:
       SCREAMING_SNAKE_CASE				    =		AutoModelForSeqaSeqLM.from_pretrained(hparams.student)
       use_task_specific_params(lowerCAmelCase__  ,  hparams.task)
       SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		None, None
      else:
       SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		create_student_by_copying_alternating_layers(
           lowerCAmelCase__  ,  e=hparams.student_encoder_layers  ,  d=hparams.student_decoder_layers  ,  save_path=lowerCAmelCase__)

      if hparams.length_penalty != -1:
       SCREAMING_SNAKE_CASE				    =		hparams.length_penalty
      SCREAMING_SNAKE_CASE				    =		hparams.teacher  # Use teacher's tokenizer
      super().__init__(lowerCAmelCase__  ,  model=lowerCAmelCase__  ,  config=student.config)
      assert student.config.model_type == teacher.config.model_type, (
          f'''teacher, student model types should be the same, got {student.config.model_type} !='''
          f''' {teacher.config.model_type}'''
      )

      if student.config.model_type == "t5":
       SCREAMING_SNAKE_CASE				    =		len(student.get_encoder().block)
       SCREAMING_SNAKE_CASE				    =		len(student.get_decoder().block)
       SCREAMING_SNAKE_CASE				    =		len(teacher.get_encoder().block)
       SCREAMING_SNAKE_CASE				    =		len(teacher.get_decoder().block)
      else:
       SCREAMING_SNAKE_CASE				    =		student.config.encoder_layers
       SCREAMING_SNAKE_CASE				    =		student.config.decoder_layers
       SCREAMING_SNAKE_CASE				    =		teacher.config.encoder_layers
       SCREAMING_SNAKE_CASE				    =		teacher.config.decoder_layers

      SCREAMING_SNAKE_CASE				    =		not (hparams.student is None or hparams.teacher == hparams.student)
      SCREAMING_SNAKE_CASE				    =		(not self.different_base_models) and hparams.alpha_hid > 0
      SCREAMING_SNAKE_CASE				    =		self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
      # self.different_encoder determines whether we need to run the teacher encoder
      SCREAMING_SNAKE_CASE				    =		teacher
      freeze_params(self.teacher)

      if not self.different_encoder:  # To save RAM, delete teacher encoder and freeze student encoder.
       try:
        del self.teacher.model.encoder
       except AttributeError:  # T5
        del self.teacher.encoder

      if e_layer_ids is None:
       SCREAMING_SNAKE_CASE				    =		list(range(lowerCAmelCase__))
      if d_layer_ids is None:
       SCREAMING_SNAKE_CASE				    =		list(range(lowerCAmelCase__))

      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		e_layer_ids, d_layer_ids  # type: List[int], List[int]

      if self.do_calc_hidden_loss:  # Intermediate supervision: Decide which layers to supervise
       if hparams.supervise_forward:
        SCREAMING_SNAKE_CASE				    =		get_layers_to_supervise(
            n_student=len(self.e_layer_ids)  ,  n_teacher=lowerCAmelCase__)
        SCREAMING_SNAKE_CASE				    =		get_layers_to_supervise(
            n_student=len(self.d_layer_ids)  ,  n_teacher=lowerCAmelCase__)
       else:  # student layer should emulate hidden states of the teacher layer it was copied from
        SCREAMING_SNAKE_CASE				    =		self.e_layer_ids
        SCREAMING_SNAKE_CASE				    =		self.d_layer_ids
      else:
       SCREAMING_SNAKE_CASE				    =		None
       SCREAMING_SNAKE_CASE				    =		None

      SCREAMING_SNAKE_CASE				    =		nn.KLDivLoss(reduction='batchmean')
      SCREAMING_SNAKE_CASE				    =		2.0
      SCREAMING_SNAKE_CASE				    =		hparams.alpha_mlm
      SCREAMING_SNAKE_CASE				    =		hparams.alpha_ce
      SCREAMING_SNAKE_CASE				    =		hparams.alpha_hid
      gc.collect()
      torch.cuda.empty_cache()





     def     __UpperCamelCase						(		self					:	Union[str, Any]  ,  lowerCAmelCase__					:	Union[str, Any]  ,  lowerCAmelCase__					:	List[Any]  ,  lowerCAmelCase__					:	List[Any])							->	int:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		mask[:, :, None].expand_as(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		s_logits.size(-1)
      SCREAMING_SNAKE_CASE				    =		torch.masked_select(lowerCAmelCase__  ,  lowerCAmelCase__)  # (bs * seq_length * voc_size) modulo the 1s in mask
      SCREAMING_SNAKE_CASE				    =		torch.masked_select(lowerCAmelCase__  ,  lowerCAmelCase__)  # (bs * seq_length * voc_size) modulo the 1s in mask
      SCREAMING_SNAKE_CASE				    =		s_logits_slct.view(-1  ,  lowerCAmelCase__)  # (bs * seq_length, voc_size) modulo the 1s in mask
      SCREAMING_SNAKE_CASE				    =		t_logits_slct.view(-1  ,  lowerCAmelCase__)  # (bs * seq_length, voc_size) modulo the 1s in mask
      assert t_logits_slct.size() == s_logits_slct.size()
      SCREAMING_SNAKE_CASE				    =		(
          self.ce_loss_fct(
              nn.functional.log_softmax(s_logits_slct / self.temperature  ,  dim=-1)  ,  nn.functional.softmax(t_logits_slct / self.temperature  ,  dim=-1)  ,  )
          * (self.temperature) ** 2
      )
      return loss_ce





     @staticmethod
     def     __UpperCamelCase						(		lowerCAmelCase__					:	Tuple  ,  lowerCAmelCase__					:	Any)							->	List[str]:

      """simple docstring"""
      SummarizationModule.add_model_specific_args(lowerCAmelCase__  ,  lowerCAmelCase__)
      add_distill_args(lowerCAmelCase__)
      return parser





     def     __UpperCamelCase						(		self					:	Optional[int]  ,  lowerCAmelCase__					:	dict)							->	tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.tokenizer.pad_token_id
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		batch['input_ids'], batch['attention_mask'], batch['labels']
      if isinstance(self.model  ,  lowerCAmelCase__):
       SCREAMING_SNAKE_CASE				    =		self.model._shift_right(lowerCAmelCase__)
      else:
       SCREAMING_SNAKE_CASE				    =		shift_tokens_right(lowerCAmelCase__  ,  lowerCAmelCase__)

      # noinspection PyCallingNonCallable
      SCREAMING_SNAKE_CASE				    =		self(
          lowerCAmelCase__  ,  attention_mask=lowerCAmelCase__  ,  decoder_input_ids=lowerCAmelCase__  ,  output_hidden_states=self.do_calc_hidden_loss  ,  output_attentions=lowerCAmelCase__  ,  use_cache=lowerCAmelCase__  ,  )
      SCREAMING_SNAKE_CASE				    =		student_outputs['logits']

      # Same cross entropy vs. label smoothing logic as finetune.py
      assert lm_logits.shape[-1] == self.model.config.vocab_size
      if self.hparams.label_smoothing == 0:
       # Same behavior as modeling_bart.py, besides ignoring pad_token_id
       SCREAMING_SNAKE_CASE				    =		nn.CrossEntropyLoss(ignore_index=lowerCAmelCase__)
       SCREAMING_SNAKE_CASE				    =		loss_fct(lm_logits.view(-1  ,  lm_logits.shape[-1])  ,  labels.view(-1))
      else:
       SCREAMING_SNAKE_CASE				    =		nn.functional.log_softmax(lowerCAmelCase__  ,  dim=-1)
       SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		label_smoothed_nll_loss(
           lowerCAmelCase__  ,  lowerCAmelCase__  ,  self.hparams.label_smoothing  ,  ignore_index=lowerCAmelCase__)

      def zero_tensor():
       return torch.tensor(0.0).type_as(lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		student_outputs[
          'encoder_last_hidden_state'
      ]  # use this unless self.different_base_models
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		zero_tensor(), zero_tensor()
      if self.different_encoder:  # compute encoder hidden state loss
       SCREAMING_SNAKE_CASE				    =		self.teacher.get_encoder()(
           lowerCAmelCase__  ,  attention_mask=lowerCAmelCase__  ,  output_hidden_states=self.do_calc_hidden_loss  ,  )
       if self.different_base_models:
        SCREAMING_SNAKE_CASE				    =		all_teacher_encoder_outputs['last_hidden_state']
       elif self.do_calc_hidden_loss:
        SCREAMING_SNAKE_CASE				    =		self.calc_hidden_loss(
            lowerCAmelCase__  ,  student_outputs['encoder_hidden_states']  ,  all_teacher_encoder_outputs['hidden_states']  ,  self.e_matches  ,  normalize_hidden=self.hparams.normalize_hidden  ,  )

      SCREAMING_SNAKE_CASE				    =		self.teacher(
          lowerCAmelCase__  ,  attention_mask=lowerCAmelCase__  ,  encoder_outputs=(teacher_enc_outputs,)  ,  decoder_input_ids=lowerCAmelCase__  ,  output_hidden_states=self.do_calc_hidden_loss  ,  use_cache=lowerCAmelCase__  ,  )
      SCREAMING_SNAKE_CASE				    =		decoder_input_ids.ne(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		self.calc_ce_loss(lowerCAmelCase__  ,  lowerCAmelCase__  ,  teacher_outputs['logits'])
      if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
       SCREAMING_SNAKE_CASE				    =		self.calc_hidden_loss(
           lowerCAmelCase__  ,  student_outputs['decoder_hidden_states']  ,  teacher_outputs['decoder_hidden_states']  ,  self.d_matches  ,  normalize_hidden=self.hparams.normalize_hidden  ,  )

      SCREAMING_SNAKE_CASE				    =		(
          self.alpha_ce * loss_ce
          + self.alpha_mlm * student_lm_loss
          + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
      )
      return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec





     @staticmethod
     def     __UpperCamelCase						(		lowerCAmelCase__					:	List[str]  ,  lowerCAmelCase__					:	Dict  ,  lowerCAmelCase__					:	Dict  ,  lowerCAmelCase__					:	List[Any]  ,  lowerCAmelCase__					:	Optional[Any])							->	Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		'expected list or tuple for hidden_states, got tensor of shape: '
      assert not isinstance(lowerCAmelCase__  ,  torch.Tensor), f'''{msg}{hidden_states.shape}'''
      assert not isinstance(lowerCAmelCase__  ,  torch.Tensor), f'''{msg}{hidden_states_T.shape}'''
      SCREAMING_SNAKE_CASE				    =		attention_mask.to(hidden_states[0])
      SCREAMING_SNAKE_CASE				    =		mask.sum() * hidden_states[0].size(-1)
      SCREAMING_SNAKE_CASE				    =		torch.stack([hidden_states[i] for i in range(len(lowerCAmelCase__))])
      SCREAMING_SNAKE_CASE				    =		torch.stack([hidden_states_T[j] for j in matches])
      assert student_states.shape == teacher_states.shape, f'''{student_states.shape} != {teacher_states.shape}'''
      if normalize_hidden:
       SCREAMING_SNAKE_CASE				    =		nn.functional.layer_norm(lowerCAmelCase__  ,  student_states.shape[1:])
       SCREAMING_SNAKE_CASE				    =		nn.functional.layer_norm(lowerCAmelCase__  ,  teacher_states.shape[1:])
      SCREAMING_SNAKE_CASE				    =		nn.functional.mse_loss(lowerCAmelCase__  ,  lowerCAmelCase__  ,  reduction='none')
      SCREAMING_SNAKE_CASE				    =		(mse * mask.unsqueeze(0).unsqueeze(-1)).sum() / valid_count
      return masked_mse





def 						A_					(						lowercase_			)				->int:







 """simple docstring"""

 parser.add_argument('--teacher'	,  type=lowercase_			)
 parser.add_argument('--alpha_ce'	,  default=0.8	,  type=lowercase_			)
 parser.add_argument('--alpha_mlm'	,  default=0.2	,  type=lowercase_			)
 parser.add_argument('--alpha_hid'	,  default=0.0	,  type=lowercase_	,  required=lowercase_			)
 parser.add_argument('--student'	,  type=lowercase_	,  required=lowercase_			)
 parser.add_argument('--student_decoder_layers'	,  default=1_2	,  type=lowercase_	,  required=lowercase_			)
 parser.add_argument('--student_encoder_layers'	,  default=1_2	,  type=lowercase_	,  required=lowercase_			)
 parser.add_argument('--no_teacher'	,  action='store_true'	,  default=lowercase_			)
 parser.add_argument('--length_penalty'	,  type=lowercase_	,  default=-1			)
 parser.add_argument('--supervise_forward'	,  action='store_true'	,  default=lowercase_			)
 parser.add_argument('--normalize_hidden'	,  action='store_true'	,  default=lowercase_			)
class 	a_(		lowercase__	):



     """simple docstring"""



     __snake_case		:				Any  					='''translation'''
     __snake_case		:				Dict  					=['''bleu''']
     __snake_case		:				Optional[Any]  					='''bleu'''





     def __init__(		self					:	Union[str, Any]  ,  lowerCAmelCase__					:	int  ,  **lowerCAmelCase__					:	List[str])							->	List[Any]:

      """simple docstring"""
      super().__init__(lowerCAmelCase__  ,  **lowerCAmelCase__)
      assert hparams.src_lang is not None
      assert hparams.tgt_lang is not None
      SCREAMING_SNAKE_CASE				    =		hparams.src_lang
      SCREAMING_SNAKE_CASE				    =		hparams.tgt_lang
      if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer  ,  lowerCAmelCase__):
       SCREAMING_SNAKE_CASE				    =		self.tokenizer.lang_code_to_id[hparams.tgt_lang]





     def     __UpperCamelCase						(		self					:	str  ,  lowerCAmelCase__					:	Optional[Any]  ,  lowerCAmelCase__					:	List[Any])							->	dict:

      """simple docstring"""
      return calculate_bleu(lowerCAmelCase__  ,  lowerCAmelCase__)





     @staticmethod
     def     __UpperCamelCase						(		lowerCAmelCase__					:	Optional[Any]  ,  lowerCAmelCase__					:	Tuple)							->	Optional[int]:

      """simple docstring"""
      TranslationModule.add_model_specific_args(lowerCAmelCase__  ,  lowerCAmelCase__)
      add_distill_args(lowerCAmelCase__)
      return parser





def 						A_					(						lowercase_			)				->Optional[int]:







 """simple docstring"""

 if args.no_teacher:
  SCREAMING_SNAKE_CASE				    =		TranslationModule if 'translation' in args.task else SummarizationModule
 else:  # DISTILL WITH TEACHER
  SCREAMING_SNAKE_CASE				    =		TranslationDistiller if 'translation' in args.task else SummarizationDistiller
 SCREAMING_SNAKE_CASE				    =		module_cls.__name__
 print(f'''using module {args.setup_cls}'''			)
 SCREAMING_SNAKE_CASE				    =		module_cls(lowercase_			)
 return model





def 						A_					(						lowercase_			)				->Tuple:







 """simple docstring"""

 Path(args.output_dir			).mkdir(exist_ok=lowercase_			)
 check_output_dir(lowercase_	,  expected_items=3			)

 SCREAMING_SNAKE_CASE				    =		create_module(lowercase_			)
 return ft_main(lowercase_	,  model=lowercase_			)


if __name__ == "__main__":
      __UpperCAmelCase      =				argparse.ArgumentParser()
      __UpperCAmelCase      =				pl.Trainer.add_argparse_args(parser)
      __UpperCAmelCase      =				SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
      __UpperCAmelCase      =				parser.parse_args()

      distill_main(args)









"""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,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

A     =      1_6
A     =      3_2

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__ = 16      )			->				List[Any]:
							A    =       AutoTokenizer.from_pretrained('bert-base-cased'      )
							A    =       load_dataset('glue'					,   'mrpc'      )

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

							def collate_fn(lowerCamelCase__      ):
														# 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(
														    lowerCamelCase__					,   padding='longest'					,   max_length=lowerCamelCase__					,   pad_to_multiple_of=lowerCamelCase__					,   return_tensors='pt'					,   )

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

							return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
							from accelerate.test_utils.training import mocked_dataloaders

							A     =      mocked_dataloaders  # noqa: F811

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__      )			->				List[str]:
							# For testing only
							if os.environ.get('TESTING_MOCKED_DATALOADERS'					,   lowerCamelCase__      ) == "1":
														A    =       2
							# Initialize accelerator
							A    =       Accelerator(cpu=args.cpu					,   mixed_precision=args.mixed_precision      )
							# 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']      )

							# New Code #
							# Parse out whether we are saving every epoch or after a certain number of batches
							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

							set_seed(lowerCamelCase__      )

							A   ,	A    =       get_dataloaders(lowerCamelCase__					,   lowerCamelCase__      )
							A    =       evaluate.load('glue'					,   'mrpc'      )

							# 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

							# 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=lowerCamelCase__      )

							# 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=lowerCamelCase__      )

							# Instantiate scheduler
							A    =       get_linear_schedule_with_warmup(
							    optimizer=lowerCamelCase__					,   num_warmup_steps=100					,   num_training_steps=(len(lowerCamelCase__      ) * 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(
							    lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )

							# New Code #
							# 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

							# We need to load the checkpoint back in before training here with `load_state`
							# The total number of epochs is adjusted based on where the state is being loaded from,
							# as we assume continuation of the same training script
							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(lowerCamelCase__      )[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(lowerCamelCase__      )
																					resume_step -= starting_epoch * len(lowerCamelCase__      )

    # Now we train the model
							for epoch in range(lowerCamelCase__					,   lowerCamelCase__      ):
														model.train()
														# New Code #
														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(lowerCamelCase__					,   lowerCamelCase__      )
																					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(lowerCamelCase__      ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device      )
																					A    =       model(**lowerCamelCase__      )
																					A    =       outputs.loss
																					A    =       loss / gradient_accumulation_steps
																					accelerator.backward(lowerCamelCase__      )
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()
																					# New Code #
																					overall_step += 1

																					# New Code #
																					# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
																					# These are saved to folders named `step_{overall_step}`
																					# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
																					# If mixed precision was used, will also save a "scalar.bin" file
																					if isinstance(lowerCamelCase__					,   lowerCamelCase__      ):
																												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					,   lowerCamelCase__      )
																																			accelerator.save_state(lowerCamelCase__      )

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

														A    =       metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(f"""epoch {epoch}:"""					,   lowerCamelCase__      )

														# New Code #
														# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
														# These are saved to folders named `epoch_{epoch}`
														# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
														# If mixed precision was used, will also save a "scalar.bin" file
														if checkpointing_steps == "epoch":
																					A    =       f"""epoch_{epoch}"""
																					if args.output_dir is not None:
																												A    =       os.path.join(args.output_dir					,   lowerCamelCase__      )
																					accelerator.save_state(lowerCamelCase__      )

def 	lowerCAmelCase__      (  )			->				Optional[int]:
							A    =       argparse.ArgumentParser(description='Simple example of training script.'      )
							parser.add_argument(
							    '--mixed_precision'					,   type=lowerCamelCase__					,   default=lowerCamelCase__					,   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=lowerCamelCase__					,   default=lowerCamelCase__					,   help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'					,   )
							parser.add_argument(
							    '--output_dir'					,   type=lowerCamelCase__					,   default='.'					,   help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'					,   )
							parser.add_argument(
							    '--resume_from_checkpoint'					,   type=lowerCamelCase__					,   default=lowerCamelCase__					,   help='If the training should continue from a checkpoint folder.'					,   )
							A    =       parser.parse_args()
							A    =       {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(lowerCamelCase__					,   lowerCamelCase__      )


if __name__ == "__main__":
							main()



