





"""simple docstring"""




import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


UpperCamelCase_	 =logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

UpperCamelCase_	 ={
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
UpperCamelCase_	 ={
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
UpperCamelCase_	 =sorted(arg_to_scheduler.keys())
UpperCamelCase_	 ="""{""" + """, """.join(arg_to_scheduler_choices) + """}"""




class 					_a		( pl.LightningModule							):






				def __init__(					self						:       Tuple,					lowerCAmelCase__						:       argparse.Namespace,					lowerCAmelCase__						:       Optional[Any]=None,					lowerCAmelCase__						:       str="base",					lowerCAmelCase__						:       Any=None,					lowerCAmelCase__						:       Any=None,					lowerCAmelCase__						:       List[Any]=None,					**lowerCAmelCase__						:       Any,					)      -> List[str]:




										'''simple docstring'''



										super().__init__()
										# TODO: move to self.save_hyperparameters()
										# self.save_hyperparameters()
										# can also expand arguments into trainer signature for easier reading

										self.save_hyperparameters(lowerCAmelCase__				)
										_UpperCamelCase     :   int      =       0
										_UpperCamelCase     :   Tuple      =       Path(self.hparams.output_dir				)
										_UpperCamelCase     :   Optional[Any]      =       self.hparams.cache_dir if self.hparams.cache_dir else None
										if config is None:
																_UpperCamelCase     :   Optional[int]      =       AutoConfig.from_pretrained(
																    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path,					**({'''num_labels''': num_labels} if num_labels is not None else {}),					cache_dir=lowerCAmelCase__,					**lowerCAmelCase__,					)
										else:
																_UpperCamelCase     :   PretrainedConfig      =       config

										_UpperCamelCase     :   Optional[Any]      =       ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
										for p in extra_model_params:
																if getattr(self.hparams,					lowerCAmelCase__,					lowerCAmelCase__				):
																						assert hasattr(self.config,					lowerCAmelCase__				), f"""model config doesn't have a `{p}` attribute"""
																						setattr(self.config,					lowerCAmelCase__,					getattr(self.hparams,					lowerCAmelCase__				)				)

										if tokenizer is None:
																_UpperCamelCase     :   Union[str, Any]      =       AutoTokenizer.from_pretrained(
																    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path,					cache_dir=lowerCAmelCase__,					)
										else:
																_UpperCamelCase     :   PreTrainedTokenizer      =       tokenizer
										_UpperCamelCase     :   List[str]      =       MODEL_MODES[mode]
										if model is None:
																_UpperCamelCase     :   Union[str, Any]      =       self.model_type.from_pretrained(
																    self.hparams.model_name_or_path,					from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path				),					config=self.config,					cache_dir=lowerCAmelCase__,					)
										else:
																_UpperCamelCase     :   Optional[Any]      =       model






				def 							snake_case      (					self						:       int,					*lowerCAmelCase__						:       List[str],					**lowerCAmelCase__						:       List[str]				)      -> List[str]:




										'''simple docstring'''



										_UpperCamelCase     :   List[str]      =       self.model_type.from_pretrained(*lowerCAmelCase__,					**lowerCAmelCase__				)






				def 							snake_case      (					self						:       Optional[int]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[Any]      =       arg_to_scheduler[self.hparams.lr_scheduler]
										_UpperCamelCase     :   Tuple      =       get_schedule_func(
										    self.opt,					num_warmup_steps=self.hparams.warmup_steps,					num_training_steps=self.total_steps()				)
										_UpperCamelCase     :   Optional[int]      =       {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
										return scheduler






				def 							snake_case      (					self						:       Optional[Any]				)      -> Dict:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       self.model
										_UpperCamelCase     :   List[str]      =       ['''bias''', '''LayerNorm.weight''']
										_UpperCamelCase     :   List[Any]      =       [
										    {
										        '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay				)],
										        '''weight_decay''': self.hparams.weight_decay,
										    },
										    {
										        '''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay				)],
										        '''weight_decay''': 0.0,
										    },
										]
										if self.hparams.adafactor:
																_UpperCamelCase     :   Union[str, Any]      =       Adafactor(
																    lowerCAmelCase__,					lr=self.hparams.learning_rate,					scale_parameter=lowerCAmelCase__,					relative_step=lowerCAmelCase__				)

										else:
																_UpperCamelCase     :   Optional[int]      =       AdamW(
																    lowerCAmelCase__,					lr=self.hparams.learning_rate,					eps=self.hparams.adam_epsilon				)
										_UpperCamelCase     :   Dict      =       optimizer

										_UpperCamelCase     :   List[Any]      =       self.get_lr_scheduler()

										return [optimizer], [scheduler]






				def 							snake_case      (					self						:       int,					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       List[Any]				)      -> str:




										'''simple docstring'''



										return self.validation_step(lowerCAmelCase__,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       Dict,					lowerCAmelCase__						:       Optional[int]				)      -> Tuple:




										'''simple docstring'''



										return self.validation_end(lowerCAmelCase__				)






				def 							snake_case      (					self						:       Dict				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   Any      =       max(1,					self.hparams.gpus				)  # TODO: consider num_tpu_cores
										_UpperCamelCase     :   Optional[Any]      =       self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
										return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs






				def 							snake_case      (					self						:       Union[str, Any],					lowerCAmelCase__						:       Dict				)      -> Optional[Any]:




										'''simple docstring'''



										if mode == "test":
																_UpperCamelCase     :   Tuple      =       len(self.test_dataloader().dataset				)
										else:
																_UpperCamelCase     :   Tuple      =       self.get_dataloader('''train''',					self.hparams.train_batch_size,					shuffle=lowerCAmelCase__				)
																_UpperCamelCase     :   Dict      =       len(self.train_dataloader().dataset				)






				def 							snake_case      (					self						:       Tuple,					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       bool = False				)      -> Union[str, Any]:




										'''simple docstring'''



										raise NotImplementedError('''You must implement this for your task'''				)






				def 							snake_case      (					self						:       Tuple				)      -> Optional[Any]:




										'''simple docstring'''



										return self.train_loader






				def 							snake_case      (					self						:       List[str]				)      -> Optional[Any]:




										'''simple docstring'''



										return self.get_dataloader('''dev''',					self.hparams.eval_batch_size,					shuffle=lowerCAmelCase__				)






				def 							snake_case      (					self						:       List[Any]				)      -> Any:




										'''simple docstring'''



										return self.get_dataloader('''test''',					self.hparams.eval_batch_size,					shuffle=lowerCAmelCase__				)






				def 							snake_case      (					self						:       Dict,					lowerCAmelCase__						:       List[Any]				)      -> Tuple:




										'''simple docstring'''



										return os.path.join(
										    self.hparams.data_dir,					'''cached_{}_{}_{}'''.format(
										        lowerCAmelCase__,					list(filter(lowerCAmelCase__,					self.hparams.model_name_or_path.split('''/'''				)				)				).pop(),					str(self.hparams.max_seq_length				),					),					)






				@pl.utilities.rank_zero_only
				def 							snake_case      (					self						:       str,					lowerCAmelCase__						:       Dict[str, Any]				)      -> None:




										'''simple docstring'''



										_UpperCamelCase     :   str      =       self.output_dir.joinpath('''best_tfmr'''				)
										_UpperCamelCase     :   Dict      =       self.step_count
										self.model.save_pretrained(lowerCAmelCase__				)
										self.tokenizer.save_pretrained(lowerCAmelCase__				)






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




										'''simple docstring'''



										parser.add_argument(
										    '''--model_name_or_path''',					default=lowerCAmelCase__,					type=lowerCAmelCase__,					required=lowerCAmelCase__,					help='''Path to pretrained model or model identifier from huggingface.co/models''',					)
										parser.add_argument(
										    '''--config_name''',					default='''''',					type=lowerCAmelCase__,					help='''Pretrained config name or path if not the same as model_name'''				)
										parser.add_argument(
										    '''--tokenizer_name''',					default=lowerCAmelCase__,					type=lowerCAmelCase__,					help='''Pretrained tokenizer name or path if not the same as model_name''',					)
										parser.add_argument(
										    '''--cache_dir''',					default='''''',					type=lowerCAmelCase__,					help='''Where do you want to store the pre-trained models downloaded from huggingface.co''',					)
										parser.add_argument(
										    '''--encoder_layerdrop''',					type=lowerCAmelCase__,					help='''Encoder layer dropout probability (Optional). Goes into model.config''',					)
										parser.add_argument(
										    '''--decoder_layerdrop''',					type=lowerCAmelCase__,					help='''Decoder layer dropout probability (Optional). Goes into model.config''',					)
										parser.add_argument(
										    '''--dropout''',					type=lowerCAmelCase__,					help='''Dropout probability (Optional). Goes into model.config''',					)
										parser.add_argument(
										    '''--attention_dropout''',					type=lowerCAmelCase__,					help='''Attention dropout probability (Optional). Goes into model.config''',					)
										parser.add_argument('''--learning_rate''',					default=5e-5,					type=lowerCAmelCase__,					help='''The initial learning rate for Adam.'''				)
										parser.add_argument(
										    '''--lr_scheduler''',					default='''linear''',					choices=lowerCAmelCase__,					metavar=lowerCAmelCase__,					type=lowerCAmelCase__,					help='''Learning rate scheduler''',					)
										parser.add_argument('''--weight_decay''',					default=0.0,					type=lowerCAmelCase__,					help='''Weight decay if we apply some.'''				)
										parser.add_argument('''--adam_epsilon''',					default=1e-8,					type=lowerCAmelCase__,					help='''Epsilon for Adam optimizer.'''				)
										parser.add_argument('''--warmup_steps''',					default=0,					type=lowerCAmelCase__,					help='''Linear warmup over warmup_steps.'''				)
										parser.add_argument('''--num_workers''',					default=4,					type=lowerCAmelCase__,					help='''kwarg passed to DataLoader'''				)
										parser.add_argument('''--num_train_epochs''',					dest='''max_epochs''',					default=3,					type=lowerCAmelCase__				)
										parser.add_argument('''--train_batch_size''',					default=3_2,					type=lowerCAmelCase__				)
										parser.add_argument('''--eval_batch_size''',					default=3_2,					type=lowerCAmelCase__				)
										parser.add_argument('''--adafactor''',					action='''store_true'''				)




class 					_a		( pl.Callback							):






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




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       trainer.lr_schedulers[0]['''scheduler''']
										_UpperCamelCase     :   Tuple      =       {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()				)}
										pl_module.logger.log_metrics(lowerCAmelCase__				)






				def 							snake_case      (					self						:       int,					lowerCAmelCase__						:       pl.Trainer,					lowerCAmelCase__						:       pl.LightningModule				)      -> Optional[int]:




										'''simple docstring'''



										rank_zero_info('''***** Validation results *****'''				)
										_UpperCamelCase     :   List[str]      =       trainer.callback_metrics
										# Log results
										for key in sorted(lowerCAmelCase__				):
																if key not in ["log", "progress_bar"]:
																						rank_zero_info('''{} = {}\n'''.format(lowerCAmelCase__,					str(metrics[key]				)				)				)






				def 							snake_case      (					self						:       Dict,					lowerCAmelCase__						:       pl.Trainer,					lowerCAmelCase__						:       pl.LightningModule				)      -> Tuple:




										'''simple docstring'''



										rank_zero_info('''***** Test results *****'''				)
										_UpperCamelCase     :   Optional[Any]      =       trainer.callback_metrics
										# Log and save results to file
										_UpperCamelCase     :   Union[str, Any]      =       os.path.join(pl_module.hparams.output_dir,					'''test_results.txt'''				)
										with open(lowerCAmelCase__,					'''w'''				) as writer:
																for key in sorted(lowerCAmelCase__				):
																						if key not in ["log", "progress_bar"]:
																												rank_zero_info('''{} = {}\n'''.format(lowerCAmelCase__,					str(metrics[key]				)				)				)
																												writer.write('''{} = {}\n'''.format(lowerCAmelCase__,					str(metrics[key]				)				)				)





def 			a_							(				_lowercase			,		_lowercase  ):
						#  To allow all pl args uncomment the following line
						#  parser = pl.Trainer.add_argparse_args(parser)
						parser.add_argument(
						    '''--output_dir'''			,		default=_lowercase			,		type=_lowercase			,		required=_lowercase			,		help='''The output directory where the model predictions and checkpoints will be written.'''			,		)
						parser.add_argument(
						    '''--fp16'''			,		action='''store_true'''			,		help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''			,		)

						parser.add_argument(
						    '''--fp16_opt_level'''			,		type=_lowercase			,		default='''O2'''			,		help=(
						        '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
						        '''See details at https://nvidia.github.io/apex/amp.html'''
						    )			,		)
						parser.add_argument('''--n_tpu_cores'''			,		dest='''tpu_cores'''			,		type=_lowercase  )
						parser.add_argument('''--max_grad_norm'''			,		dest='''gradient_clip_val'''			,		default=1.0			,		type=_lowercase			,		help='''Max gradient norm'''  )
						parser.add_argument('''--do_train'''			,		action='''store_true'''			,		help='''Whether to run training.'''  )
						parser.add_argument('''--do_predict'''			,		action='''store_true'''			,		help='''Whether to run predictions on the test set.'''  )
						parser.add_argument(
						    '''--gradient_accumulation_steps'''			,		dest='''accumulate_grad_batches'''			,		type=_lowercase			,		default=1			,		help='''Number of updates steps to accumulate before performing a backward/update pass.'''			,		)
						parser.add_argument('''--seed'''			,		type=_lowercase			,		default=42			,		help='''random seed for initialization'''  )
						parser.add_argument(
						    '''--data_dir'''			,		default=_lowercase			,		type=_lowercase			,		required=_lowercase			,		help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''			,		)






def 			a_							(				_lowercase			,		_lowercase			,		_lowercase=None			,		_lowercase=True			,		_lowercase=[]			,		_lowercase=None			,		_lowercase=None			,		**_lowercase			,		):
						pl.seed_everything(args.seed  )

						# init model
						_UpperCamelCase     :   Optional[Any]      =       Path(model.hparams.output_dir  )
						odir.mkdir(exist_ok=_lowercase  )

						# add custom checkpoints
						if checkpoint_callback is None:
												_UpperCamelCase     :   Union[str, Any]      =       pl.callbacks.ModelCheckpoint(
												    filepath=args.output_dir			,		prefix='''checkpoint'''			,		monitor='''val_loss'''			,		mode='''min'''			,		save_top_k=1  )
						if early_stopping_callback:
												extra_callbacks.append(_lowercase  )
						if logging_callback is None:
												_UpperCamelCase     :   str      =       LoggingCallback()

						_UpperCamelCase     :   Optional[Any]      =       {}

						# TODO: remove with PyTorch 1.6 since pl uses native amp
						if args.fpaa:
												_UpperCamelCase     :   List[Any]      =       16
												_UpperCamelCase     :   Dict      =       args.fpaa_opt_level

						if args.gpus > 1:
												_UpperCamelCase     :   str      =       '''ddp'''

						_UpperCamelCase     :   List[str]      =       args.accumulate_grad_batches
						_UpperCamelCase     :   int      =       extra_train_kwargs.get('''accelerator'''			,		_lowercase  )
						_UpperCamelCase     :   int      =       extra_train_kwargs.get('''profiler'''			,		_lowercase  )

						_UpperCamelCase     :   Any      =       pl.Trainer.from_argparse_args(
						    _lowercase			,		weights_summary=_lowercase			,		callbacks=[logging_callback] + extra_callbacks			,		logger=_lowercase			,		checkpoint_callback=_lowercase			,		**_lowercase			,		)

						if args.do_train:
												trainer.fit(_lowercase  )

						return trainer





"""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)

