
'''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 						__snake_case (						_a  ):
		a_        =				["loss", "ce_loss", "mlm_loss", "hid_loss_enc", "hid_loss_dec"]




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






								'''simple docstring'''






								assert Path(hparams.data_dir).exists()
								UpperCamelCase_    :  Union[str, Any]				    =    Path(hparams.output_dir)
								self.output_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE)

								UpperCamelCase_    :  List[Any]				    =    self.output_dir.joinpath("""student""")

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

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

								UpperCamelCase_    ,UpperCamelCase_    :  Union[str, 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_    :  Optional[int]				    =    get_layers_to_supervise(
																				    n_student=len(self.e_layer_ids) ,						n_teacher=_SCREAMING_SNAKE_CASE)
																				UpperCamelCase_    :  Optional[Any]				    =    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_    :  Any				    =    self.e_layer_ids
																				UpperCamelCase_    :  Tuple				    =    self.d_layer_ids
								else:
														UpperCamelCase_    :  int				    =    None
														UpperCamelCase_    :  Optional[int]				    =    None

								UpperCamelCase_    :  Dict				    =    nn.KLDivLoss(reduction="""batchmean""")
								UpperCamelCase_    :  str				    =    2.0
								UpperCamelCase_    :  int				    =    hparams.alpha_mlm
								UpperCamelCase_    :  Dict				    =    hparams.alpha_ce
								UpperCamelCase_    :  Any				    =    hparams.alpha_hid
								gc.collect()
								torch.cuda.empty_cache()




		def   _snake_case  (     self				:Union[str, Any] ,						_SCREAMING_SNAKE_CASE				:str ,						_SCREAMING_SNAKE_CASE				:Any ,						_SCREAMING_SNAKE_CASE				:Dict)				-> Dict:






								'''simple docstring'''






								UpperCamelCase_    :  List[str]				    =    mask[:, :, None].expand_as(_SCREAMING_SNAKE_CASE)
								UpperCamelCase_    :  Dict				    =    s_logits.size(-1)
								UpperCamelCase_    :  Optional[int]				    =    torch.masked_select(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)  # (bs * seq_length * voc_size) modulo the 1s in mask
								UpperCamelCase_    :  Optional[Any]				    =    torch.masked_select(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)  # (bs * seq_length * voc_size) modulo the 1s in mask
								UpperCamelCase_    :  Optional[int]				    =    s_logits_slct.view(-1 ,						_SCREAMING_SNAKE_CASE)  # (bs * seq_length, voc_size) modulo the 1s in mask
								UpperCamelCase_    :  str				    =    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_    :  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   _snake_case  (     _SCREAMING_SNAKE_CASE				:str ,						_SCREAMING_SNAKE_CASE				:Any)				-> Optional[Any]:






								'''simple docstring'''






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




		def   _snake_case  (     self				:Tuple ,						_SCREAMING_SNAKE_CASE				:dict)				-> tuple:






								'''simple docstring'''






								UpperCamelCase_    :  Dict				    =    self.tokenizer.pad_token_id
								UpperCamelCase_    ,UpperCamelCase_    ,UpperCamelCase_    :  Any				    =    batch["""input_ids"""], batch["""attention_mask"""], batch["""labels"""]
								if isinstance(self.model ,						_SCREAMING_SNAKE_CASE):
														UpperCamelCase_    :  int				    =    self.model._shift_right(_SCREAMING_SNAKE_CASE)
								else:
														UpperCamelCase_    :  str				    =    shift_tokens_right(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)

								# noinspection PyCallingNonCallable
								UpperCamelCase_    :  Union[str, Any]				    =    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_    :  Union[str, 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
														UpperCamelCase_    :  Dict				    =    nn.CrossEntropyLoss(ignore_index=_SCREAMING_SNAKE_CASE)
														UpperCamelCase_    :  str				    =    loss_fct(lm_logits.view(-1 ,						lm_logits.shape[-1]) ,						labels.view(-1))
								else:
														UpperCamelCase_    :  List[str]				    =    nn.functional.log_softmax(_SCREAMING_SNAKE_CASE ,						dim=-1)
														UpperCamelCase_    ,UpperCamelCase_    :  Dict				    =    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_    :  Tuple				    =    student_outputs[
								    """encoder_last_hidden_state"""
								]  # use this unless self.different_base_models
								UpperCamelCase_    ,UpperCamelCase_    :  int				    =    zero_tensor(), zero_tensor()
								if self.different_encoder:  # compute encoder hidden state loss
														UpperCamelCase_    :  List[str]				    =    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_    :  List[Any]				    =    all_teacher_encoder_outputs["""last_hidden_state"""]
														elif self.do_calc_hidden_loss:
																				UpperCamelCase_    :  Union[str, Any]				    =    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_    :  Tuple				    =    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_    :  Any				    =    decoder_input_ids.ne(_SCREAMING_SNAKE_CASE)
								UpperCamelCase_    :  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_    :  str				    =    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_    :  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   _snake_case  (     _SCREAMING_SNAKE_CASE				:Optional[int] ,						_SCREAMING_SNAKE_CASE				:List[Any] ,						_SCREAMING_SNAKE_CASE				:Union[str, Any] ,						_SCREAMING_SNAKE_CASE				:str ,						_SCREAMING_SNAKE_CASE				:Optional[Any])				-> Optional[int]:






								'''simple docstring'''






								UpperCamelCase_    :  List[Any]				    =    """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_    :  List[Any]				    =    attention_mask.to(hidden_states[0])
								UpperCamelCase_    :  Union[str, Any]				    =    mask.sum() * hidden_states[0].size(-1)
								UpperCamelCase_    :  List[Any]				    =    torch.stack([hidden_states[i] for i in range(len(_SCREAMING_SNAKE_CASE))])
								UpperCamelCase_    :  Union[str, Any]				    =    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_    :  Dict				    =    nn.functional.layer_norm(_SCREAMING_SNAKE_CASE ,						student_states.shape[1:])
														UpperCamelCase_    :  str				    =    nn.functional.layer_norm(_SCREAMING_SNAKE_CASE ,						teacher_states.shape[1:])
								UpperCamelCase_    :  Optional[Any]				    =    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 				__magic_name__(					UpperCAmelCase__   :					List[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=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 						__snake_case (						_a  ):
		a_        =				"translation"
		a_        =				["bleu"]
		a_        =				"bleu"




		def __init__(     self				:Union[str, Any] ,						_SCREAMING_SNAKE_CASE				:Tuple ,						**_SCREAMING_SNAKE_CASE				:Tuple)				-> str:






								'''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_    :  Dict				    =    hparams.src_lang
								UpperCamelCase_    :  List[Any]				    =    hparams.tgt_lang
								if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer ,						_SCREAMING_SNAKE_CASE):
														UpperCamelCase_    :  Tuple				    =    self.tokenizer.lang_code_to_id[hparams.tgt_lang]




		def   _snake_case  (     self				:Union[str, Any] ,						_SCREAMING_SNAKE_CASE				:str ,						_SCREAMING_SNAKE_CASE				:Tuple)				-> dict:






								'''simple docstring'''






								return calculate_bleu(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)





		@staticmethod
		def   _snake_case  (     _SCREAMING_SNAKE_CASE				:Optional[Any] ,						_SCREAMING_SNAKE_CASE				:List[str])				-> int:






								'''simple docstring'''






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







def 				__magic_name__(					UpperCAmelCase__   :					List[str]  ):
						if args.no_teacher:
												UpperCamelCase_    :  str				    =    TranslationModule if """translation""" in args.task else SummarizationModule
						else:  # DISTILL WITH TEACHER
												UpperCamelCase_    :  Tuple				    =    TranslationDistiller if """translation""" in args.task else SummarizationDistiller
						UpperCamelCase_    :  str				    =    module_cls.__name__
						print(f"""using module {args.setup_cls}"""  )
						UpperCamelCase_    :  Dict				    =    module_cls(UpperCAmelCase__  )
						return model
def 				__magic_name__(					UpperCAmelCase__   :					Any  ):
						Path(args.output_dir  ).mkdir(exist_ok=UpperCAmelCase__  )
						check_output_dir(UpperCAmelCase__       ,							expected_items=3  )

						UpperCamelCase_    :  int				    =    create_module(UpperCAmelCase__  )
						return ft_main(UpperCAmelCase__       ,							model=UpperCAmelCase__  )


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)






'''simple docstring'''


from __future__ import annotations

from typing import Any




class 						__snake_case :




		def __init__(     self				:int ,						_SCREAMING_SNAKE_CASE				:Any ,						_SCREAMING_SNAKE_CASE				:Any)				-> None:  # noqa: A002






								'''simple docstring'''






								UpperCamelCase_    :  Optional[Any]				    =    item
								UpperCamelCase_    :  int				    =    next







class 						__snake_case :




		def __init__(     self				:Optional[Any])				-> None:






								'''simple docstring'''






								UpperCamelCase_    :  Node | None				    =    None
								UpperCamelCase_    :  Tuple				    =    0




		def   _snake_case  (     self				:List[Any] ,						_SCREAMING_SNAKE_CASE				:Any)				-> None:






								'''simple docstring'''






								UpperCamelCase_    :  List[Any]				    =    Node(_SCREAMING_SNAKE_CASE ,						self.head)
								self.size += 1




		def   _snake_case  (     self				:int)				-> Any:






								'''simple docstring'''






								if self.head is None:
														return None
								else:
														UpperCamelCase_    :  Any				    =    self.head.item
														UpperCamelCase_    :  List[str]				    =    self.head.next
														self.size -= 1
														return item




		def   _snake_case  (     self				:int)				-> bool:






								'''simple docstring'''






								return self.head is None




		def __str__(     self				:Any)				-> str:






								'''simple docstring'''






								if self.is_empty():
														return ""
								else:
														UpperCamelCase_    :  List[str]				    =    self.head
														UpperCamelCase_    :  Tuple				    =    """"""
														UpperCamelCase_    :  list[str]				    =    []
														while iterate:
																				item_list.append(str(iterate.item))
																				UpperCamelCase_    :  Tuple				    =    iterate.next

														UpperCamelCase_    :  Optional[int]				    =    """ --> """.join(_SCREAMING_SNAKE_CASE)

														return item_str





		def __len__(     self				:List[Any])				-> int:






								'''simple docstring'''






								return self.size




