
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_				(  snake_case__     ):




      '''simple docstring'''







      a		:Union[str, Any]						 =      ['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']







      def __init__( self					,					_UpperCAmelCase):
            assert Path(hparams.data_dir).exists()
            lowerCAmelCase_						=      Path(hparams.output_dir)
            self.output_dir.mkdir(exist_ok=__SCREAMING_SNAKE_CASE)

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

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

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

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

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

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







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            lowerCAmelCase_						=      mask[:, :, None].expand_as(__SCREAMING_SNAKE_CASE)
            lowerCAmelCase_						=      s_logits.size(-1)
            lowerCAmelCase_						=      torch.masked_select(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE)  # (bs * seq_length * voc_size) modulo the 1s in mask
            lowerCAmelCase_						=      torch.masked_select(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE)  # (bs * seq_length * voc_size) modulo the 1s in mask
            lowerCAmelCase_						=      s_logits_slct.view(-1					,					__SCREAMING_SNAKE_CASE)  # (bs * seq_length, voc_size) modulo the 1s in mask
            lowerCAmelCase_						=      t_logits_slct.view(-1					,					__SCREAMING_SNAKE_CASE)  # (bs * seq_length, voc_size) modulo the 1s in mask
            assert t_logits_slct.size() == s_logits_slct.size()
            lowerCAmelCase_						=      (
                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 lowercase__		( _UpperCAmelCase					,					_UpperCAmelCase):
            SummarizationModule.add_model_specific_args(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE)
            add_distill_args(__SCREAMING_SNAKE_CASE)
            return parser







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

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

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

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

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

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




      """simple docstring"""
      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 			UpperCamelCase_				(  snake_case__     ):




      '''simple docstring'''







      a		:Union[str, Any]						 =      'translation'
      a		:Optional[Any]						 =      ['bleu']
      a		:Optional[int]						 =      'bleu'







      def __init__( self					,					_UpperCAmelCase					,					**_UpperCAmelCase):
            super().__init__(__SCREAMING_SNAKE_CASE					,					**__SCREAMING_SNAKE_CASE)
            assert hparams.src_lang is not None
            assert hparams.tgt_lang is not None
            lowerCAmelCase_						=      hparams.src_lang
            lowerCAmelCase_						=      hparams.tgt_lang
            if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer					,					__SCREAMING_SNAKE_CASE):
                  lowerCAmelCase_						=      self.tokenizer.lang_code_to_id[hparams.tgt_lang]







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase):
            return calculate_bleu(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE)







      @staticmethod
      def lowercase__		( _UpperCAmelCase					,					_UpperCAmelCase):
            TranslationModule.add_model_specific_args(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE)
            add_distill_args(__SCREAMING_SNAKE_CASE)
            return parser
def lowerCamelCase_       (			A			:      str						):




      """simple docstring"""






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




      """simple docstring"""
      Path(args.output_dir						).mkdir(exist_ok=A_						)
      check_output_dir(A_		,					expected_items=3						)

      lowerCAmelCase_						=      create_module(A_						)
      return ft_main(A_		,					model=A_						)


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

 distill_main(args)







import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image
def 	_lowerCamelCase  (		A_  :	Optional[int]							,  A_  :	List[Any]=2_5_6      )			->			Tuple:

   '''simple docstring'''






   UpperCamelCase__ :			Optional[Any] 						=min(img.size      )

   if s < target_image_size:
      raise ValueError(f'''min dim for image {s} < {target_image_size}'''      )

   UpperCamelCase__ :			Optional[Any] 						=target_image_size / s
   UpperCamelCase__ :			Optional[Any] 						=(round(r * img.size[1]      ), round(r * img.size[0]      ))
   UpperCamelCase__ :			List[Any] 						=TF.resize(A_							,  A_							,  interpolation=PIL.Image.LANCZOS      )
   UpperCamelCase__ :			Optional[int] 						=TF.center_crop(A_							,  output_size=2 * [target_image_size]      )
   UpperCamelCase__ :			Union[str, Any] 						=torch.unsqueeze(T.ToTensor()(A_      )							,  0      )
   return img
def 	_lowerCamelCase  (		A_  :	Optional[int]      )			->			Optional[Any]:

   '''simple docstring'''






   UpperCamelCase__ :			List[Any] 						=2.0 * x - 1.0
   return x
def 	_lowerCamelCase  (		A_  :	Union[str, Any]							,  A_  :	Any=True							,  A_  :	int="RGB"      )			->			int:

   '''simple docstring'''






   UpperCamelCase__ :			Dict 						=x.detach().cpu()
   if process:
      UpperCamelCase__ :			Optional[int] 						=post_process_tensor(A_      )
   UpperCamelCase__ :			Optional[int] 						=x.numpy()
   if process:
      UpperCamelCase__ :			Dict 						=(2_5_5 * x).astype(np.uinta      )
   UpperCamelCase__ :			int 						=Image.fromarray(A_      )
   if not x.mode == mode:
      UpperCamelCase__ :			Tuple 						=x.convert(A_      )
   return x
def 	_lowerCamelCase  (		A_  :	Dict      )			->			List[str]:

   '''simple docstring'''






   UpperCamelCase__ :			List[str] 						=torch.clamp(A_							,  -1.0							,  1.0      )
   UpperCamelCase__ :			Union[str, Any] 						=(x + 1.0) / 2.0
   UpperCamelCase__ :			Union[str, Any] 						=x.permute(1							,  2							,  0      )
   return x
def 	_lowerCamelCase  (		A_  :	Tuple      )			->			List[str]:

   '''simple docstring'''






   UpperCamelCase__ :			List[str] 						=post_process_tensor(x.squeeze()      )
   return x.permute(2							,  0							,  1      ).unsqueeze(0      )

