




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 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE				):

      """simple docstring"""

      lowerCAmelCase__     :	Any      =   ["loss", "ce_loss", "mlm_loss", "hid_loss_enc", "hid_loss_dec"]







      def __init__(       self   :	int	,    snake_case   :	List[str]			):
          assert Path(hparams.data_dir			).exists()
          __UpperCamelCase											=  Path(hparams.output_dir			)
          self.output_dir.mkdir(exist_ok=snake_case			)

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

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

          if hparams.length_penalty != -1:
              __UpperCamelCase											=  hparams.length_penalty
          __UpperCamelCase											=  hparams.teacher  # Use teacher's tokenizer
          super().__init__(snake_case	,    model=snake_case	,    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":
              __UpperCamelCase											=  len(student.get_encoder().block			)
              __UpperCamelCase											=  len(student.get_decoder().block			)
              __UpperCamelCase											=  len(teacher.get_encoder().block			)
              __UpperCamelCase											=  len(teacher.get_decoder().block			)
          else:
              __UpperCamelCase											=  student.config.encoder_layers
              __UpperCamelCase											=  student.config.decoder_layers
              __UpperCamelCase											=  teacher.config.encoder_layers
              __UpperCamelCase											=  teacher.config.decoder_layers

          __UpperCamelCase											=  not (hparams.student is None or hparams.teacher == hparams.student)
          __UpperCamelCase											=  (not self.different_base_models) and hparams.alpha_hid > 0
          __UpperCamelCase											=  self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
          # self.different_encoder determines whether we need to run the teacher encoder
          __UpperCamelCase											=  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:
              __UpperCamelCase											=  list(range(snake_case			)			)
          if d_layer_ids is None:
              __UpperCamelCase											=  list(range(snake_case			)			)

          __UpperCamelCase						, __UpperCamelCase											=  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:
                  __UpperCamelCase											=  get_layers_to_supervise(
                      n_student=len(self.e_layer_ids			)	,    n_teacher=snake_case			)
                  __UpperCamelCase											=  get_layers_to_supervise(
                      n_student=len(self.d_layer_ids			)	,    n_teacher=snake_case			)
              else:  # student layer should emulate hidden states of the teacher layer it was copied from
                  __UpperCamelCase											=  self.e_layer_ids
                  __UpperCamelCase											=  self.d_layer_ids
          else:
              __UpperCamelCase											=  None
              __UpperCamelCase											=  None

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







      def  snake_case		(       self   :	Tuple	,    snake_case   :	Optional[int]	,    snake_case   :	Any	,    snake_case   :	Dict			):
          __UpperCamelCase											=  mask[:, :, None].expand_as(snake_case			)
          __UpperCamelCase											=  s_logits.size(-1			)
          __UpperCamelCase											=  torch.masked_select(snake_case	,    snake_case			)  # (bs * seq_length * voc_size) modulo the 1s in mask
          __UpperCamelCase											=  torch.masked_select(snake_case	,    snake_case			)  # (bs * seq_length * voc_size) modulo the 1s in mask
          __UpperCamelCase											=  s_logits_slct.view(-1	,    snake_case			)  # (bs * seq_length, voc_size) modulo the 1s in mask
          __UpperCamelCase											=  t_logits_slct.view(-1	,    snake_case			)  # (bs * seq_length, voc_size) modulo the 1s in mask
          assert t_logits_slct.size() == s_logits_slct.size()
          __UpperCamelCase											=  (
              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  snake_case		(       snake_case   :	str	,    snake_case   :	List[str]			):
          SummarizationModule.add_model_specific_args(snake_case	,    snake_case			)
          add_distill_args(snake_case			)
          return parser







      def  snake_case		(       self   :	Tuple	,    snake_case   :	dict			):
          __UpperCamelCase											=  self.tokenizer.pad_token_id
          __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase											=  batch['''input_ids'''], batch['''attention_mask'''], batch['''labels''']
          if isinstance(self.model	,    snake_case			):
              __UpperCamelCase											=  self.model._shift_right(snake_case			)
          else:
              __UpperCamelCase											=  shift_tokens_right(snake_case	,    snake_case			)

          # noinspection PyCallingNonCallable
          __UpperCamelCase											=  self(
              snake_case	,    attention_mask=snake_case	,    decoder_input_ids=snake_case	,    output_hidden_states=self.do_calc_hidden_loss	,    output_attentions=snake_case	,    use_cache=snake_case	,    )
          __UpperCamelCase											=  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
              __UpperCamelCase											=  nn.CrossEntropyLoss(ignore_index=snake_case			)
              __UpperCamelCase											=  loss_fct(lm_logits.view(-1	,    lm_logits.shape[-1]			)	,    labels.view(-1			)			)
          else:
              __UpperCamelCase											=  nn.functional.log_softmax(snake_case	,    dim=-1			)
              __UpperCamelCase						, __UpperCamelCase											=  label_smoothed_nll_loss(
                  snake_case	,    snake_case	,    self.hparams.label_smoothing	,    ignore_index=snake_case			)

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

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

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

          __UpperCamelCase											=  (
              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  snake_case		(       snake_case   :	Optional[int]	,    snake_case   :	Optional[Any]	,    snake_case   :	List[Any]	,    snake_case   :	List[Any]	,    snake_case   :	str			):
          __UpperCamelCase											=  '''expected list or tuple for hidden_states, got tensor of shape: '''
          assert not isinstance(snake_case	,    torch.Tensor			), F"{msg}{hidden_states.shape}"
          assert not isinstance(snake_case	,    torch.Tensor			), F"{msg}{hidden_states_T.shape}"
          __UpperCamelCase											=  attention_mask.to(hidden_states[0]			)
          __UpperCamelCase											=  mask.sum() * hidden_states[0].size(-1			)
          __UpperCamelCase											=  torch.stack([hidden_states[i] for i in range(len(snake_case			)			)]			)
          __UpperCamelCase											=  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:
              __UpperCamelCase											=  nn.functional.layer_norm(snake_case	,    student_states.shape[1:]			)
              __UpperCamelCase											=  nn.functional.layer_norm(snake_case	,    teacher_states.shape[1:]			)
          __UpperCamelCase											=  nn.functional.mse_loss(snake_case	,    snake_case	,    reduction='''none'''			)
          __UpperCamelCase											=  (mse * mask.unsqueeze(0			).unsqueeze(-1			)).sum() / valid_count
          return masked_mse







def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		List[str]:

    """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=12   ,       type=lowercase_   ,       required=lowercase_ )
    parser.add_argument('''--student_encoder_layers'''   ,       default=12   ,       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 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE				):

      """simple docstring"""

      lowerCAmelCase__     :	Dict      =   "translation"
      lowerCAmelCase__     :	Union[str, Any]      =   ["bleu"]
      lowerCAmelCase__     :	Optional[Any]      =   "bleu"







      def __init__(       self   :	int	,    snake_case   :	str	,    **snake_case   :	Optional[int]			):
          super().__init__(snake_case	,    **snake_case			)
          assert hparams.src_lang is not None
          assert hparams.tgt_lang is not None
          __UpperCamelCase											=  hparams.src_lang
          __UpperCamelCase											=  hparams.tgt_lang
          if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer	,    snake_case			):
              __UpperCamelCase											=  self.tokenizer.lang_code_to_id[hparams.tgt_lang]







      def  snake_case		(       self   :	str	,    snake_case   :	List[str]	,    snake_case   :	List[str]			):
          return calculate_bleu(snake_case	,    snake_case			)







      @staticmethod
      def  snake_case		(       snake_case   :	Any	,    snake_case   :	Tuple			):
          TranslationModule.add_model_specific_args(snake_case	,    snake_case			)
          add_distill_args(snake_case			)
          return parser







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

    """simple docstring"""


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




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		List[str]:

    """simple docstring"""


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

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


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

 distill_main(args)












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_										=       16
a_										=       32




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ = 16 )    ->		Tuple:

    """simple docstring"""


    __UpperCamelCase											=  AutoTokenizer.from_pretrained('''bert-base-cased''' )
    __UpperCamelCase											=  load_dataset('''glue'''   ,       '''mrpc''' )

    def tokenize_function(lowercase_ ):
        # max_length=None => use the model max length (it's actually the default)
        __UpperCamelCase											=  tokenizer(examples['''sentence1''']   ,       examples['''sentence2''']   ,       truncation=lowercase_   ,       max_length=lowercase_ )
        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():
        __UpperCamelCase											=  datasets.map(
            lowercase_   ,       batched=lowercase_   ,       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
    __UpperCamelCase											=  tokenized_datasets.rename_column('''label'''   ,       '''labels''' )

    def collate_fn(lowercase_ ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        __UpperCamelCase											=  1_28 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            __UpperCamelCase											=  16
        elif accelerator.mixed_precision != "no":
            __UpperCamelCase											=  8
        else:
            __UpperCamelCase											=  None

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

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

    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    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		Tuple:

    """simple docstring"""


    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''   ,       lowercase_ ) == "1":
        __UpperCamelCase											=  2
    # Initialize accelerator
    __UpperCamelCase											=  Accelerator(cpu=args.cpu   ,       mixed_precision=args.mixed_precision )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    __UpperCamelCase											=  config['''lr''']
    __UpperCamelCase											=  int(config['''num_epochs'''] )
    __UpperCamelCase											=  int(config['''seed'''] )
    __UpperCamelCase											=  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":
            __UpperCamelCase											=  args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            __UpperCamelCase											=  int(args.checkpointing_steps )
        else:
            raise ValueError(
                F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed." )
    else:
        __UpperCamelCase											=  None

    set_seed(lowercase_ )

    __UpperCamelCase						, __UpperCamelCase											=  get_dataloaders(lowercase_   ,       lowercase_ )
    __UpperCamelCase											=  evaluate.load('''glue'''   ,       '''mrpc''' )

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

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

    # 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).
    __UpperCamelCase											=  model.to(accelerator.device )

    # Instantiate optimizer
    __UpperCamelCase											=  AdamW(params=model.parameters()   ,       lr=lowercase_ )

    # Instantiate scheduler
    __UpperCamelCase											=  get_linear_schedule_with_warmup(
        optimizer=lowercase_   ,       num_warmup_steps=1_00   ,       num_training_steps=(len(lowercase_ ) * 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.
    __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase											=  accelerator.prepare(
        lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ )

    # New Code #
    # We need to keep track of how many total steps we have iterated over
    __UpperCamelCase											=  0
    # We also need to keep track of the stating epoch so files are named properly
    __UpperCamelCase											=  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 )
            __UpperCamelCase											=  os.path.basename(args.resume_from_checkpoint )
        else:
            # Get the most recent checkpoint
            __UpperCamelCase											=  [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
            dirs.sort(key=os.path.getctime )
            __UpperCamelCase											=  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        __UpperCamelCase											=  os.path.splitext(lowercase_ )[0]

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

    # Now we train the model
    for epoch in range(lowercase_   ,       lowercase_ ):
        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
            __UpperCamelCase											=  accelerator.skip_first_batches(lowercase_   ,       lowercase_ )
            overall_step += resume_step
        else:
            # After the first iteration though, we need to go back to the original dataloader
            __UpperCamelCase											=  train_dataloader
        for step, batch in enumerate(lowercase_ ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device )
            __UpperCamelCase											=  model(**lowercase_ )
            __UpperCamelCase											=  outputs.loss
            __UpperCamelCase											=  loss / gradient_accumulation_steps
            accelerator.backward(lowercase_ )
            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(lowercase_   ,       lowercase_ ):
                __UpperCamelCase											=  F"step_{overall_step}"
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        __UpperCamelCase											=  os.path.join(args.output_dir   ,       lowercase_ )
                    accelerator.save_state(lowercase_ )

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

        __UpperCamelCase											=  metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F"epoch {epoch}:"   ,       lowercase_ )

        # 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":
            __UpperCamelCase											=  F"epoch_{epoch}"
            if args.output_dir is not None:
                __UpperCamelCase											=  os.path.join(args.output_dir   ,       lowercase_ )
            accelerator.save_state(lowercase_ )




def    __SCREAMING_SNAKE_CASE				(					)    ->		int:

    """simple docstring"""


    __UpperCamelCase											=  argparse.ArgumentParser(description='''Simple example of training script.''' )
    parser.add_argument(
        '''--mixed_precision'''   ,       type=lowercase_   ,       default=lowercase_   ,       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=lowercase_   ,       default=lowercase_   ,       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=lowercase_   ,       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=lowercase_   ,       default=lowercase_   ,       help='''If the training should continue from a checkpoint folder.'''   ,       )
    __UpperCamelCase											=  parser.parse_args()
    __UpperCamelCase											=  {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(lowercase_   ,       lowercase_ )


if __name__ == "__main__":
 main()






