
"""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
#
########################################################################

SCREAMING_SNAKE_CASE_  					= 16
SCREAMING_SNAKE_CASE_  					= 32
def       __UpperCAmelCase		(		a_      :		Accelerator     ,	a_      :		int = 16					)  ->							Optional[int]:
   _UpperCAmelCase 						=       AutoTokenizer.from_pretrained("bert-base-cased"					)
   _UpperCAmelCase 						=       load_dataset("glue"     ,	"mrpc"					)

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

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

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

   def collate_fn(a_      :		Tuple					):
      # 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(
          a_     ,	padding="longest"     ,	max_length=a_     ,	pad_to_multiple_of=a_     ,	return_tensors="pt"     ,	)

   # Instantiate dataloaders.
   _UpperCAmelCase 						=       DataLoader(
       tokenized_datasets["train"]     ,	shuffle=a_     ,	collate_fn=a_     ,	batch_size=a_					)
   _UpperCAmelCase 						=       DataLoader(
       tokenized_datasets["validation"]     ,	shuffle=a_     ,	collate_fn=a_     ,	batch_size=a_					)

   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

  SCREAMING_SNAKE_CASE_  					= mocked_dataloaders  # noqa: F811
def       __UpperCAmelCase		(		a_      :		int     ,	a_      :		Tuple					)  ->							Union[str, Any]:
   # For testing only
   if os.environ.get("TESTING_MOCKED_DATALOADERS"     ,	a_					) == "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(a_					)

   _UpperCAmelCase						,				_UpperCAmelCase 						=       get_dataloaders(a_     ,	a_					)
   _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=a_					)

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

   # Instantiate optimizer
   _UpperCAmelCase 						=       AdamW(params=model.parameters()     ,	lr=a_					)

   # Instantiate scheduler
   _UpperCAmelCase 						=       get_linear_schedule_with_warmup(
       optimizer=a_     ,	num_warmup_steps=1_00     ,	num_training_steps=(len(a_					) * num_epochs) // gradient_accumulation_steps     ,	)

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

   # 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(a_					)[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(a_					)
         resume_step -= starting_epoch * len(a_					)

    # Now we train the model
   for epoch in range(a_     ,	a_					):
      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(a_     ,	a_					)
         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(a_					):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device					)
         _UpperCAmelCase 						=       model(**a_					)
         _UpperCAmelCase 						=       outputs.loss
         _UpperCAmelCase 						=       loss / gradient_accumulation_steps
         accelerator.backward(a_					)
         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(a_     ,	a_					):
            _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     ,	a_					)
               accelerator.save_state(a_					)

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

      _UpperCAmelCase 						=       metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'''epoch {epoch}:'''     ,	a_					)

      # 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     ,	a_					)
         accelerator.save_state(a_					)
def       __UpperCAmelCase		(		)  ->							List[str]:
   _UpperCAmelCase 						=       argparse.ArgumentParser(description="Simple example of training script."					)
   parser.add_argument(
       "--mixed_precision"     ,	type=a_     ,	default=a_     ,	choices=["no", "fp16", "bf16", "fp8"]     ,	help="Whether to use mixed precision. Choose"
       "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
       "and an Nvidia Ampere GPU."     ,	)
   parser.add_argument("--cpu"     ,	action="store_true"     ,	help="If passed, will train on the CPU."					)
   parser.add_argument(
       "--checkpointing_steps"     ,	type=a_     ,	default=a_     ,	help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."     ,	)
   parser.add_argument(
       "--output_dir"     ,	type=a_     ,	default="."     ,	help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."     ,	)
   parser.add_argument(
       "--resume_from_checkpoint"     ,	type=a_     ,	default=a_     ,	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(a_     ,	a_					)


if __name__ == "__main__":
  main()



"""simple docstring"""







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      (       lowerCAmelCase_      ):
       _lowerCamelCase				:				str   			=      ['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']







       def __init__(       self				,lowerCamelCase_		):



          '''simple docstring'''

          assert Path(hparams.data_dir		).exists()
          _UpperCAmelCase 						=       Path(hparams.output_dir		)
          self.output_dir.mkdir(exist_ok=lowerCamelCase_		)

          _UpperCAmelCase 						=       self.output_dir.joinpath("student"		)

          _UpperCAmelCase 						=       str(lowerCamelCase_		)  # Tell lightning we are training the student
          _UpperCAmelCase 						=       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:
             _UpperCAmelCase 						=       AutoModelForSeqaSeqLM.from_pretrained(hparams.student		)
             use_task_specific_params(lowerCamelCase_				,hparams.task		)
             _UpperCAmelCase						,				_UpperCAmelCase 						=       None, None
          else:
             _UpperCAmelCase						,				_UpperCAmelCase						,				_UpperCAmelCase 						=       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:
             _UpperCAmelCase 						=       hparams.length_penalty
          _UpperCAmelCase 						=       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":
             _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(lowerCamelCase_		)		)
          if d_layer_ids is None:
             _UpperCAmelCase 						=       list(range(lowerCamelCase_		)		)

          _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=lowerCamelCase_		)
                _UpperCAmelCase 						=       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
                _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				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       mask[:, :, None].expand_as(lowerCamelCase_		)
          _UpperCAmelCase 						=       s_logits.size(-1		)
          _UpperCAmelCase 						=       torch.masked_select(lowerCamelCase_				,lowerCamelCase_		)  # (bs * seq_length * voc_size) modulo the 1s in mask
          _UpperCAmelCase 						=       torch.masked_select(lowerCamelCase_				,lowerCamelCase_		)  # (bs * seq_length * voc_size) modulo the 1s in mask
          _UpperCAmelCase 						=       s_logits_slct.view(-1				,lowerCamelCase_		)  # (bs * seq_length, voc_size) modulo the 1s in mask
          _UpperCAmelCase 						=       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()
          _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      (       lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

          SummarizationModule.add_model_specific_args(lowerCamelCase_				,lowerCamelCase_		)
          add_distill_args(lowerCamelCase_		)
          return parser







       def  _snake_case      (       self				,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       self.tokenizer.pad_token_id
          _UpperCAmelCase						,				_UpperCAmelCase						,				_UpperCAmelCase 						=       batch["input_ids"], batch["attention_mask"], batch["labels"]
          if isinstance(self.model				,lowerCamelCase_		):
             _UpperCAmelCase 						=       self.model._shift_right(lowerCamelCase_		)
          else:
             _UpperCAmelCase 						=       shift_tokens_right(lowerCamelCase_				,lowerCamelCase_		)

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

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

          _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()(
                 lowerCamelCase_				,attention_mask=lowerCamelCase_				,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(
                    lowerCamelCase_				,student_outputs["encoder_hidden_states"]				,all_teacher_encoder_outputs["hidden_states"]				,self.e_matches				,normalize_hidden=self.hparams.normalize_hidden				,)

          _UpperCAmelCase 						=       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_				,)
          _UpperCAmelCase 						=       decoder_input_ids.ne(lowerCamelCase_		)
          _UpperCAmelCase 						=       self.calc_ce_loss(lowerCamelCase_				,lowerCamelCase_				,teacher_outputs["logits"]		)
          if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
             _UpperCAmelCase 						=       self.calc_hidden_loss(
                 lowerCamelCase_				,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      (       lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       "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}'''
          _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(lowerCamelCase_		)		)]		)
          _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(lowerCamelCase_				,student_states.shape[1:]		)
             _UpperCAmelCase 						=       nn.functional.layer_norm(lowerCamelCase_				,teacher_states.shape[1:]		)
          _UpperCAmelCase 						=       nn.functional.mse_loss(lowerCamelCase_				,lowerCamelCase_				,reduction="none"		)
          _UpperCAmelCase 						=       (mse * mask.unsqueeze(0		).unsqueeze(-1		)).sum() / valid_count
          return masked_mse



def       __UpperCAmelCase		(		a_      :		List[str]					)  ->							Tuple:
   # NOTE: if --student argument was specified and the teacher and student base models
   # are different, the models still have to have the same tokenizer, specified by
   # --tokenizer_name. So, for example, you can distill from t5_large to t5_small but not
   # from bart to t5. This s because if the tokenizers are different, the output space
   # for the two models is also different and their logits are not comparable.
   parser.add_argument("--teacher"     ,	type=a_					)
   parser.add_argument("--alpha_ce"     ,	default=0.8     ,	type=a_					)
   parser.add_argument("--alpha_mlm"     ,	default=0.2     ,	type=a_					)
   parser.add_argument("--alpha_hid"     ,	default=0.0     ,	type=a_     ,	required=a_					)
   parser.add_argument("--student"     ,	type=a_     ,	required=a_					)
   parser.add_argument("--student_decoder_layers"     ,	default=12     ,	type=a_     ,	required=a_					)
   parser.add_argument("--student_encoder_layers"     ,	default=12     ,	type=a_     ,	required=a_					)
   parser.add_argument("--no_teacher"     ,	action="store_true"     ,	default=a_					)
   parser.add_argument("--length_penalty"     ,	type=a_     ,	default=-1					)
   parser.add_argument("--supervise_forward"     ,	action="store_true"     ,	default=a_					)
   parser.add_argument("--normalize_hidden"     ,	action="store_true"     ,	default=a_					)






class      __a      (       lowerCAmelCase_      ):
       _lowerCamelCase				:				Union[str, Any]   			=      'translation'
       _lowerCamelCase				:				Dict   			=      ['bleu']
       _lowerCamelCase				:				Any   			=      'bleu'







       def __init__(       self				,lowerCamelCase_				,**lowerCamelCase_		):



          '''simple docstring'''

          super().__init__(lowerCamelCase_				,**lowerCamelCase_		)
          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				,lowerCamelCase_		):
             _UpperCAmelCase 						=       self.tokenizer.lang_code_to_id[hparams.tgt_lang]







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

          return calculate_bleu(lowerCamelCase_				,lowerCamelCase_		)







       @staticmethod
       def  _snake_case      (       lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

          TranslationModule.add_model_specific_args(lowerCamelCase_				,lowerCamelCase_		)
          add_distill_args(lowerCamelCase_		)
          return parser



def       __UpperCAmelCase		(		a_      :		str					)  ->							Any:
   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(a_					)
   return model
def       __UpperCAmelCase		(		a_      :		List[str]					)  ->							str:
   Path(args.output_dir					).mkdir(exist_ok=a_					)
   check_output_dir(a_     ,	expected_items=3					)

   _UpperCAmelCase 						=       create_module(a_					)
   return ft_main(a_     ,	model=a_					)


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

  distill_main(args)

