




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'''



				lowerCamelCase_   :			Union[str, Any]			  =   ["""loss""", """ce_loss""", """mlm_loss""", """hid_loss_enc""", """hid_loss_dec"""]







				def __init__(					self  ,						UpperCamelCase__  )  ->   List[str]:
											assert Path(hparams.data_dir  ).exists()
											lowerCamelCase     :   List[str]    				=      Path(hparams.output_dir  )
											self.output_dir.mkdir(exist_ok=UpperCamelCase__  )

											lowerCamelCase     :   Optional[int]    				=      self.output_dir.joinpath("student"  )

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

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

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

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







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







				def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   tuple:
											lowerCamelCase     :   Optional[Any]    				=      self.tokenizer.pad_token_id
											lowerCamelCase				,   lowerCamelCase				,   lowerCamelCase     :   Any    				=      batch["input_ids"], batch["attention_mask"], batch["labels"]
											if isinstance(self.model  ,						UpperCamelCase__  ):
																		lowerCamelCase     :   Union[str, Any]    				=      self.model._shift_right(UpperCamelCase__  )
											else:
																		lowerCamelCase     :   List[Any]    				=      shift_tokens_right(UpperCamelCase__  ,						UpperCamelCase__  )

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

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

											lowerCamelCase     :   List[Any]    				=      student_outputs[
											    "encoder_last_hidden_state"
											]  # use this unless self.different_base_models
											lowerCamelCase				,   lowerCamelCase     :   List[str]    				=      zero_tensor(), zero_tensor()
											if self.different_encoder:  # compute encoder hidden state loss
																		lowerCamelCase     :   Dict    				=      self.teacher.get_encoder()(
																		    UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						output_hidden_states=self.do_calc_hidden_loss  ,						)
																		if self.different_base_models:
																									lowerCamelCase     :   str    				=      all_teacher_encoder_outputs["last_hidden_state"]
																		elif self.do_calc_hidden_loss:
																									lowerCamelCase     :   Dict    				=      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  ,						)

											lowerCamelCase     :   Optional[int]    				=      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__  ,						)
											lowerCamelCase     :   Optional[Any]    				=      decoder_input_ids.ne(UpperCamelCase__  )
											lowerCamelCase     :   int    				=      self.calc_ce_loss(UpperCamelCase__  ,						UpperCamelCase__  ,						teacher_outputs["logits"]  )
											if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
																		lowerCamelCase     :   Union[str, 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  ,						)

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




def   A   (   _SCREAMING_SNAKE_CASE					)			->				str:
							# 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=__lowerCamelCase					)
							parser.add_argument("--alpha_ce"				,default=0.8				,type=__lowerCamelCase					)
							parser.add_argument("--alpha_mlm"				,default=0.2				,type=__lowerCamelCase					)
							parser.add_argument("--alpha_hid"				,default=0.0				,type=__lowerCamelCase				,required=__lowerCamelCase					)
							parser.add_argument("--student"				,type=__lowerCamelCase				,required=__lowerCamelCase					)
							parser.add_argument("--student_decoder_layers"				,default=12				,type=__lowerCamelCase				,required=__lowerCamelCase					)
							parser.add_argument("--student_encoder_layers"				,default=12				,type=__lowerCamelCase				,required=__lowerCamelCase					)
							parser.add_argument("--no_teacher"				,action="store_true"				,default=__lowerCamelCase					)
							parser.add_argument("--length_penalty"				,type=__lowerCamelCase				,default=-1					)
							parser.add_argument("--supervise_forward"				,action="store_true"				,default=__lowerCamelCase					)
							parser.add_argument("--normalize_hidden"				,action="store_true"				,default=__lowerCamelCase					)




class 	UpperCamelCase__			(snake_case_  ):

				'''simple docstring'''



				lowerCamelCase_   :			Tuple			  =   """translation"""
				lowerCamelCase_   :			Optional[Any]			  =   ["""bleu"""]
				lowerCamelCase_   :			Any			  =   """bleu"""







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







				def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   dict:
											return calculate_bleu(UpperCamelCase__  ,						UpperCamelCase__  )







				@staticmethod
				def 					_lowercase  (					UpperCamelCase__  ,						UpperCamelCase__  )  ->   Any:
											TranslationModule.add_model_specific_args(UpperCamelCase__  ,						UpperCamelCase__  )
											add_distill_args(UpperCamelCase__  )
											return parser




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

def   A   (   _SCREAMING_SNAKE_CASE					)			->				str:
							Path(args.output_dir					).mkdir(exist_ok=__lowerCamelCase					)
							check_output_dir(__lowerCamelCase				,expected_items=3					)

							lowerCamelCase     :   Dict    				=      create_module(__lowerCamelCase					)
							return ft_main(__lowerCamelCase				,model=__lowerCamelCase					)


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

							distill_main(args)












from __future__ import annotations

def   A   (   _SCREAMING_SNAKE_CASE					)			->				list[int]:
       assert (
           isinstance(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					) and mask > 0
       ), f'''mask needs to be positive integer, your input {mask}'''
       lowerCamelCase     :   str    				=      []
       lowerCamelCase     :   Any    				=      mask

       while submask:
              all_submasks.append(_SCREAMING_SNAKE_CASE					)
              lowerCamelCase     :   Union[str, Any]    				=      (submask - 1) & mask

       return all_submasks


if __name__ == "__main__":
       import doctest

       doctest.testmod()






