


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 							lowercase__(						snake_case__			):






      '''simple docstring'''







      snake_case__       =					['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']




      def __init__(		self   ,			__SCREAMING_SNAKE_CASE)					->						Optional[int]:







         """simple docstring"""







         assert Path(hparams.data_dir).exists()
         UpperCamelCase__ :			str 						=Path(hparams.output_dir)
         self.output_dir.mkdir(exist_ok=__SCREAMING_SNAKE_CASE)

         UpperCamelCase__ :			List[str] 						=self.output_dir.joinpath("student")

         UpperCamelCase__ :			str 						=str(__SCREAMING_SNAKE_CASE)  # Tell lightning we are training the student
         UpperCamelCase__ :			int 						=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:
            UpperCamelCase__ :			Any 						=AutoModelForSeqaSeqLM.from_pretrained(hparams.student)
            use_task_specific_params(__SCREAMING_SNAKE_CASE   ,			hparams.task)
            UpperCamelCase__						,	UpperCamelCase__ :			List[str] 						=None, None
         else:
            UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__ :			List[str] 						=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:
            UpperCamelCase__ :			Dict 						=hparams.length_penalty
         UpperCamelCase__ :			List[Any] 						=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":
            UpperCamelCase__ :			List[str] 						=len(student.get_encoder().block)
            UpperCamelCase__ :			Tuple 						=len(student.get_decoder().block)
            UpperCamelCase__ :			int 						=len(teacher.get_encoder().block)
            UpperCamelCase__ :			Any 						=len(teacher.get_decoder().block)
         else:
            UpperCamelCase__ :			Any 						=student.config.encoder_layers
            UpperCamelCase__ :			Optional[Any] 						=student.config.decoder_layers
            UpperCamelCase__ :			str 						=teacher.config.encoder_layers
            UpperCamelCase__ :			Dict 						=teacher.config.decoder_layers

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

         UpperCamelCase__						,	UpperCamelCase__ :			Any 						=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__ :			Tuple 						=get_layers_to_supervise(
                   n_student=len(self.e_layer_ids)   ,			n_teacher=__SCREAMING_SNAKE_CASE)
               UpperCamelCase__ :			int 						=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
               UpperCamelCase__ :			str 						=self.e_layer_ids
               UpperCamelCase__ :			Union[str, Any] 						=self.d_layer_ids
         else:
            UpperCamelCase__ :			List[Any] 						=None
            UpperCamelCase__ :			Dict 						=None

         UpperCamelCase__ :			Union[str, Any] 						=nn.KLDivLoss(reduction="batchmean")
         UpperCamelCase__ :			List[str] 						=2.0
         UpperCamelCase__ :			List[str] 						=hparams.alpha_mlm
         UpperCamelCase__ :			Any 						=hparams.alpha_ce
         UpperCamelCase__ :			str 						=hparams.alpha_hid
         gc.collect()
         torch.cuda.empty_cache()




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						str:







         """simple docstring"""







         UpperCamelCase__ :			List[str] 						=mask[:, :, None].expand_as(__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			Union[str, Any] 						=s_logits.size(-1)
         UpperCamelCase__ :			List[Any] 						=torch.masked_select(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)  # (bs * seq_length * voc_size) modulo the 1s in mask
         UpperCamelCase__ :			int 						=torch.masked_select(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)  # (bs * seq_length * voc_size) modulo the 1s in mask
         UpperCamelCase__ :			List[str] 						=s_logits_slct.view(-1   ,			__SCREAMING_SNAKE_CASE)  # (bs * seq_length, voc_size) modulo the 1s in mask
         UpperCamelCase__ :			List[Any] 						=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()
         UpperCamelCase__ :			Dict 						=(
             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    UpperCAmelCase							(		__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						Dict:







         """simple docstring"""







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




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						tuple:







         """simple docstring"""







         UpperCamelCase__ :			List[str] 						=self.tokenizer.pad_token_id
         UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__ :			str 						=batch["input_ids"], batch["attention_mask"], batch["labels"]
         if isinstance(self.model   ,			__SCREAMING_SNAKE_CASE):
            UpperCamelCase__ :			Optional[Any] 						=self.model._shift_right(__SCREAMING_SNAKE_CASE)
         else:
            UpperCamelCase__ :			Optional[int] 						=shift_tokens_right(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)

         # noinspection PyCallingNonCallable
         UpperCamelCase__ :			Tuple 						=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   ,			)
         UpperCamelCase__ :			Optional[int] 						=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__ :			Dict 						=nn.CrossEntropyLoss(ignore_index=__SCREAMING_SNAKE_CASE)
            UpperCamelCase__ :			Optional[int] 						=loss_fct(lm_logits.view(-1   ,			lm_logits.shape[-1])   ,			labels.view(-1))
         else:
            UpperCamelCase__ :			Dict 						=nn.functional.log_softmax(__SCREAMING_SNAKE_CASE   ,			dim=-1)
            UpperCamelCase__						,	UpperCamelCase__ :			Union[str, Any] 						=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)

         UpperCamelCase__ :			List[Any] 						=student_outputs[
             "encoder_last_hidden_state"
         ]  # use this unless self.different_base_models
         UpperCamelCase__						,	UpperCamelCase__ :			Tuple 						=zero_tensor(), zero_tensor()
         if self.different_encoder:  # compute encoder hidden state loss
            UpperCamelCase__ :			Union[str, Any] 						=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:
               UpperCamelCase__ :			Any 						=all_teacher_encoder_outputs["last_hidden_state"]
            elif self.do_calc_hidden_loss:
               UpperCamelCase__ :			Optional[int] 						=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   ,			)

         UpperCamelCase__ :			Optional[Any] 						=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   ,			)
         UpperCamelCase__ :			List[str] 						=decoder_input_ids.ne(__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			Optional[int] 						=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
            UpperCamelCase__ :			Tuple 						=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   ,			)

         UpperCamelCase__ :			List[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    UpperCAmelCase							(		__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						int:







         """simple docstring"""







         UpperCamelCase__ :			Optional[int] 						="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}'''
         UpperCamelCase__ :			Dict 						=attention_mask.to(hidden_states[0])
         UpperCamelCase__ :			str 						=mask.sum() * hidden_states[0].size(-1)
         UpperCamelCase__ :			List[str] 						=torch.stack([hidden_states[i] for i in range(len(__SCREAMING_SNAKE_CASE))])
         UpperCamelCase__ :			Tuple 						=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__ :			Tuple 						=nn.functional.layer_norm(__SCREAMING_SNAKE_CASE   ,			student_states.shape[1:])
            UpperCamelCase__ :			Optional[Any] 						=nn.functional.layer_norm(__SCREAMING_SNAKE_CASE   ,			teacher_states.shape[1:])
         UpperCamelCase__ :			int 						=nn.functional.mse_loss(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			reduction="none")
         UpperCamelCase__ :			Tuple 						=(mse * mask.unsqueeze(0).unsqueeze(-1)).sum() / valid_count
         return masked_mse




def 	_lowerCamelCase  (		A_  :	List[Any]      )			->			Union[str, 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=1_2							,  type=A_							,  required=A_      )
   parser.add_argument("--student_encoder_layers"							,  default=1_2							,  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 							lowercase__(						snake_case__			):






      '''simple docstring'''







      snake_case__       =					'''translation'''
      snake_case__       =					['''bleu''']
      snake_case__       =					'''bleu'''




      def __init__(		self   ,			__SCREAMING_SNAKE_CASE   ,			**__SCREAMING_SNAKE_CASE)					->						Any:







         """simple docstring"""







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




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						dict:







         """simple docstring"""







         return calculate_bleu(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)




      @staticmethod
      def    UpperCAmelCase							(		__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						int:







         """simple docstring"""







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




def 	_lowerCamelCase  (		A_  :	str      )			->			List[str]:

   '''simple docstring'''






   if args.no_teacher:
      UpperCamelCase__ :			Optional[Any] 						=TranslationModule if "translation" in args.task else SummarizationModule
   else:  # DISTILL WITH TEACHER
      UpperCamelCase__ :			Union[str, Any] 						=TranslationDistiller if "translation" in args.task else SummarizationDistiller
   UpperCamelCase__ :			str 						=module_cls.__name__
   print(f'''using module {args.setup_cls}'''      )
   UpperCamelCase__ :			Optional[Any] 						=module_cls(A_      )
   return model
def 	_lowerCamelCase  (		A_  :	Tuple      )			->			Optional[Any]:

   '''simple docstring'''






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

   UpperCamelCase__ :			Dict 						=create_module(A_      )
   return ft_main(A_							,  model=A_      )


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

     distill_main(args)





from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap



@total_ordering
class 							lowercase__:






      '''simple docstring'''







      def __init__(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						None:







         """simple docstring"""







         UpperCamelCase__ :			Optional[int] 						=word
         UpperCamelCase__ :			Dict 						=count




      def __eq__(		self   ,			__SCREAMING_SNAKE_CASE)					->						bool:







         """simple docstring"""







         if not isinstance(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE):
            return NotImplemented
         return self.count == other.count




      def __lt__(		self   ,			__SCREAMING_SNAKE_CASE)					->						bool:







         """simple docstring"""







         if not isinstance(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE):
            return NotImplemented
         return self.count < other.count




def 	_lowerCamelCase  (		A_  :	list[str]							,  A_  :	int      )			->			list[str]:

   '''simple docstring'''






   UpperCamelCase__ :			Heap[WordCount] 						=Heap()
   UpperCamelCase__ :			Any 						=Counter(A_      )
   heap.build_max_heap(
       [WordCount(A_							,  A_      ) for word, count in count_by_word.items()]      )
   return [heap.extract_max().word for _ in range(min(A_							,  len(A_      )      )      )]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

