


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    A					(     A_				):
							UpperCamelCase_     :							Any =['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']


							def __init__(self   ,       lowerCAmelCase      ):
									assert Path(hparams.data_dir      ).exists()
									__lowercase=    Path(hparams.output_dir      )
									self.output_dir.mkdir(exist_ok=lowerCAmelCase      )

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

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

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

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

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

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


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


							def     _A (self   ,       lowerCAmelCase      ):
									__lowercase=    self.tokenizer.pad_token_id
									__lowercase,							__lowercase,							__lowercase=    batch['input_ids'], batch['attention_mask'], batch['labels']
									if isinstance(self.model   ,       lowerCAmelCase      ):
											__lowercase=    self.model._shift_right(lowerCAmelCase      )
									else:
											__lowercase=    shift_tokens_right(lowerCAmelCase   ,       lowerCAmelCase      )

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

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

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

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

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







def  _lowerCamelCase(			lowercase__			)		->			str:

		'''simple docstring'''







		parser.add_argument('--teacher'     ,				type=lowercase__			)
		parser.add_argument('--alpha_ce'     ,				default=0.8     ,				type=lowercase__			)
		parser.add_argument('--alpha_mlm'     ,				default=0.2     ,				type=lowercase__			)
		parser.add_argument('--alpha_hid'     ,				default=0.0     ,				type=lowercase__     ,				required=lowercase__			)
		parser.add_argument('--student'     ,				type=lowercase__     ,				required=lowercase__			)
		parser.add_argument('--student_decoder_layers'     ,				default=1_2     ,				type=lowercase__     ,				required=lowercase__			)
		parser.add_argument('--student_encoder_layers'     ,				default=1_2     ,				type=lowercase__     ,				required=lowercase__			)
		parser.add_argument('--no_teacher'     ,				action='store_true'     ,				default=lowercase__			)
		parser.add_argument('--length_penalty'     ,				type=lowercase__     ,				default=-1			)
		parser.add_argument('--supervise_forward'     ,				action='store_true'     ,				default=lowercase__			)
		parser.add_argument('--normalize_hidden'     ,				action='store_true'     ,				default=lowercase__			)





class    A					(     A_				):
							UpperCamelCase_     :							str ='''translation'''
							UpperCamelCase_     :							Union[str, Any] =['''bleu''']
							UpperCamelCase_     :							Optional[int] ='''bleu'''


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


							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase      ):
									return calculate_bleu(lowerCAmelCase   ,       lowerCAmelCase      )







							@staticmethod
							def     _A (lowerCAmelCase   ,       lowerCAmelCase      ):
									TranslationModule.add_model_specific_args(lowerCAmelCase   ,       lowerCAmelCase      )
									add_distill_args(lowerCAmelCase      )
									return parser







def  _lowerCamelCase(			lowercase__			)		->			Optional[int]:

		'''simple docstring'''







		if args.no_teacher:
				__lowercase=    TranslationModule if 'translation' in args.task else SummarizationModule
		else:  # DISTILL WITH TEACHER
				__lowercase=    TranslationDistiller if 'translation' in args.task else SummarizationDistiller
		__lowercase=    module_cls.__name__
		print(F'using module {args.setup_cls}'			)
		__lowercase=    module_cls(lowercase__			)
		return model







def  _lowerCamelCase(			lowercase__			)		->			Any:

		'''simple docstring'''







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

		__lowercase=    create_module(lowercase__			)
		return ft_main(lowercase__     ,				model=lowercase__			)


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

	distill_main(args)










def  _lowerCamelCase(			lowercase__			)		->			str:

		'''simple docstring'''







		return " ".join([word[::-1] for word in input_str.split()]			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()






