




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      UpperCAmelCase						(			A   ):
   __SCREAMING_SNAKE_CASE		:							Tuple							=					["loss", "ce_loss", "mlm_loss", "hid_loss_enc", "hid_loss_dec"]






   def __init__(	self   :       Dict						,					UpperCamelCase_   :       Union[str, Any]     )->			int:
     assert Path(hparams.data_dir     ).exists()
     _lowercase: int						     =    Path(hparams.output_dir     )
     self.output_dir.mkdir(exist_ok=UpperCamelCase_     )

     _lowercase: Optional[int]						     =    self.output_dir.joinpath('student'     )

     _lowercase: List[str]						     =    str(UpperCamelCase_     )  # Tell lightning we are training the student
     _lowercase: Tuple						     =    AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher     ).eval()
     use_task_specific_params(UpperCamelCase_						,					hparams.task     )  # We copy good generation parameters to student by default
     if hparams.student is not None:
       _lowercase: List[Any]						     =    AutoModelForSeqaSeqLM.from_pretrained(hparams.student     )
       use_task_specific_params(UpperCamelCase_						,					hparams.task     )
       _lowercase     ,					_lowercase: int						     =    None, None
     else:
       _lowercase     ,					_lowercase     ,					_lowercase: Optional[int]						     =    create_student_by_copying_alternating_layers(
           UpperCamelCase_						,					e=hparams.student_encoder_layers						,					d=hparams.student_decoder_layers						,					save_path=UpperCamelCase_     )

     if hparams.length_penalty != -1:
       _lowercase: List[str]						     =    hparams.length_penalty
     _lowercase: Union[str, Any]						     =    hparams.teacher  # Use teacher's tokenizer
     super().__init__(UpperCamelCase_						,					model=UpperCamelCase_						,					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":
       _lowercase: Any						     =    len(student.get_encoder().block     )
       _lowercase: List[Any]						     =    len(student.get_decoder().block     )
       _lowercase: Tuple						     =    len(teacher.get_encoder().block     )
       _lowercase: Optional[int]						     =    len(teacher.get_decoder().block     )
     else:
       _lowercase: Tuple						     =    student.config.encoder_layers
       _lowercase: str						     =    student.config.decoder_layers
       _lowercase: Union[str, Any]						     =    teacher.config.encoder_layers
       _lowercase: Optional[Any]						     =    teacher.config.decoder_layers

     _lowercase: Any						     =    not (hparams.student is None or hparams.teacher == hparams.student)
     _lowercase: Tuple						     =    (not self.different_base_models) and hparams.alpha_hid > 0
     _lowercase: Optional[int]						     =    self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
     # self.different_encoder determines whether we need to run the teacher encoder
     _lowercase: Dict						     =    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:
       _lowercase: str						     =    list(range(UpperCamelCase_     )     )
     if d_layer_ids is None:
       _lowercase: Optional[Any]						     =    list(range(UpperCamelCase_     )     )

     _lowercase     ,					_lowercase: int						     =    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:
         _lowercase: Optional[Any]						     =    get_layers_to_supervise(
             n_student=len(self.e_layer_ids     )						,					n_teacher=UpperCamelCase_     )
         _lowercase: int						     =    get_layers_to_supervise(
             n_student=len(self.d_layer_ids     )						,					n_teacher=UpperCamelCase_     )
       else:  # student layer should emulate hidden states of the teacher layer it was copied from
         _lowercase: Optional[Any]						     =    self.e_layer_ids
         _lowercase: Optional[int]						     =    self.d_layer_ids
     else:
       _lowercase: Dict						     =    None
       _lowercase: Dict						     =    None

     _lowercase: int						     =    nn.KLDivLoss(reduction='batchmean'     )
     _lowercase: Any						     =    2.0
     _lowercase: Any						     =    hparams.alpha_mlm
     _lowercase: Tuple						     =    hparams.alpha_ce
     _lowercase: str						     =    hparams.alpha_hid
     gc.collect()
     torch.cuda.empty_cache()






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






   def  _a						(	self   :       int						,					UpperCamelCase_   :       dict     )->			tuple:
     _lowercase: Optional[Any]						     =    self.tokenizer.pad_token_id
     _lowercase     ,					_lowercase     ,					_lowercase: Dict						     =    batch['input_ids'], batch['attention_mask'], batch['labels']
     if isinstance(self.model						,					UpperCamelCase_     ):
       _lowercase: Tuple						     =    self.model._shift_right(UpperCamelCase_     )
     else:
       _lowercase: int						     =    shift_tokens_right(UpperCamelCase_						,					UpperCamelCase_     )

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

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

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

     _lowercase: Optional[Any]						     =    self.teacher(
         UpperCamelCase_						,					attention_mask=UpperCamelCase_						,					encoder_outputs=(teacher_enc_outputs,)						,					decoder_input_ids=UpperCamelCase_						,					output_hidden_states=self.do_calc_hidden_loss						,					use_cache=UpperCamelCase_						,					)
     _lowercase: Union[str, Any]						     =    decoder_input_ids.ne(UpperCamelCase_     )
     _lowercase: Tuple						     =    self.calc_ce_loss(UpperCamelCase_						,					UpperCamelCase_						,					teacher_outputs['logits']     )
     if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
       _lowercase: List[Any]						     =    self.calc_hidden_loss(
           UpperCamelCase_						,					student_outputs['decoder_hidden_states']						,					teacher_outputs['decoder_hidden_states']						,					self.d_matches						,					normalize_hidden=self.hparams.normalize_hidden						,					)

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


def 		_lowerCamelCase				(					UpperCamelCase							)			->		Optional[Any]:





  """simple docstring"""





  parser.add_argument('--teacher'					,     type=UpperCamelCase							)
  parser.add_argument('--alpha_ce'					,     default=0.8					,     type=UpperCamelCase							)
  parser.add_argument('--alpha_mlm'					,     default=0.2					,     type=UpperCamelCase							)
  parser.add_argument('--alpha_hid'					,     default=0.0					,     type=UpperCamelCase					,     required=UpperCamelCase							)
  parser.add_argument('--student'					,     type=UpperCamelCase					,     required=UpperCamelCase							)
  parser.add_argument('--student_decoder_layers'					,     default=12					,     type=UpperCamelCase					,     required=UpperCamelCase							)
  parser.add_argument('--student_encoder_layers'					,     default=12					,     type=UpperCamelCase					,     required=UpperCamelCase							)
  parser.add_argument('--no_teacher'					,     action='store_true'					,     default=UpperCamelCase							)
  parser.add_argument('--length_penalty'					,     type=UpperCamelCase					,     default=-1							)
  parser.add_argument('--supervise_forward'					,     action='store_true'					,     default=UpperCamelCase							)
  parser.add_argument('--normalize_hidden'					,     action='store_true'					,     default=UpperCamelCase							)

class      UpperCAmelCase						(			A   ):
   __SCREAMING_SNAKE_CASE		:							List[Any]							=					"translation"
   __SCREAMING_SNAKE_CASE		:							Any							=					["bleu"]
   __SCREAMING_SNAKE_CASE		:							Tuple							=					"bleu"






   def __init__(	self   :       Optional[Any]						,					UpperCamelCase_   :       Any						,					**UpperCamelCase_   :       Dict     )->			Optional[int]:
     super().__init__(UpperCamelCase_						,					**UpperCamelCase_     )
     assert hparams.src_lang is not None
     assert hparams.tgt_lang is not None
     _lowercase: List[str]						     =    hparams.src_lang
     _lowercase: Dict						     =    hparams.tgt_lang
     if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer						,					UpperCamelCase_     ):
       _lowercase: Optional[Any]						     =    self.tokenizer.lang_code_to_id[hparams.tgt_lang]






   def  _a						(	self   :       Union[str, Any]						,					UpperCamelCase_   :       Tuple						,					UpperCamelCase_   :       Dict     )->			dict:
     return calculate_bleu(UpperCamelCase_						,					UpperCamelCase_     )






   @staticmethod
   def  _a						(	UpperCamelCase_   :       int						,					UpperCamelCase_   :       str     )->			List[Any]:
     TranslationModule.add_model_specific_args(UpperCamelCase_						,					UpperCamelCase_     )
     add_distill_args(UpperCamelCase_     )
     return parser


def 		_lowerCamelCase				(					UpperCamelCase							)			->		Tuple:





  """simple docstring"""





  if args.no_teacher:
    _lowercase: int						     =    TranslationModule if 'translation' in args.task else SummarizationModule
  else:  # DISTILL WITH TEACHER
    _lowercase: List[Any]						     =    TranslationDistiller if 'translation' in args.task else SummarizationDistiller
  _lowercase: str						     =    module_cls.__name__
  print(F'using module {args.setup_cls}'							)
  _lowercase: Optional[int]						     =    module_cls(UpperCamelCase							)
  return model


def 		_lowerCamelCase				(					UpperCamelCase							)			->		Optional[Any]:





  """simple docstring"""





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

  _lowercase: List[Any]						     =    create_module(UpperCamelCase							)
  return ft_main(UpperCamelCase					,     model=UpperCamelCase							)


if __name__ == "__main__":
   UpperCAmelCase__						:       Any							=    argparse.ArgumentParser()
   UpperCAmelCase__						:       str							=    pl.Trainer.add_argparse_args(parser)
   UpperCAmelCase__						:       Union[str, Any]							=    SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
   UpperCAmelCase__						:       str							=    parser.parse_args()

   distill_main(args)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


UpperCAmelCase__						:       Union[str, Any]							=    {'configuration_bert_generation': ['BertGenerationConfig']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:       str							=    ['BertGenerationTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:       Tuple							=    [
       'BertGenerationDecoder',
       'BertGenerationEncoder',
       'BertGenerationPreTrainedModel',
       'load_tf_weights_in_bert_generation',
   ]


if TYPE_CHECKING:
   from .configuration_bert_generation import BertGenerationConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_bert_generation import BertGenerationTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bert_generation import (
          BertGenerationDecoder,
          BertGenerationEncoder,
          BertGenerationPreTrainedModel,
          load_tf_weights_in_bert_generation,
      )

else:
   import sys

   UpperCAmelCase__						:       List[Any]							=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
