






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(					__UpperCamelCase      ):
 lowercase :       Tuple		      =		['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']



 def __init__(	self   :   Dict ,		UpperCAmelCase__   :   Optional[Any]	):

    """simple docstring"""






    assert Path(hparams.data_dir	).exists()
    lowercase__						:	str         = Path(hparams.output_dir	)
    self.output_dir.mkdir(exist_ok=UpperCAmelCase__	)

    lowercase__						:	Dict         = self.output_dir.joinpath("""student"""	)

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

    lowercase__						:	Optional[int]         = not (hparams.student is None or hparams.teacher == hparams.student)
    lowercase__						:	Optional[Any]         = (not self.different_base_models) and hparams.alpha_hid > 0
    lowercase__						:	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
    lowercase__						:	List[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:
       lowercase__						:	Dict         = list(range(UpperCAmelCase__	)	)
    if d_layer_ids is None:
       lowercase__						:	Any         = list(range(UpperCAmelCase__	)	)

    lowercase__     ,					lowercase__						:	Optional[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:
          lowercase__						:	Any         = get_layers_to_supervise(
              n_student=len(self.e_layer_ids	) ,		n_teacher=UpperCAmelCase__	)
          lowercase__						:	Optional[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
          lowercase__						:	str         = self.e_layer_ids
          lowercase__						:	Dict         = self.d_layer_ids
    else:
       lowercase__						:	Optional[int]         = None
       lowercase__						:	List[Any]         = None

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



 def 						UpperCAmelCase    (	self   :   List[str] ,		UpperCAmelCase__   :   Dict ,		UpperCAmelCase__   :   Tuple ,		UpperCAmelCase__   :   List[Any]	):

    """simple docstring"""






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

    """simple docstring"""






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



 def 						UpperCAmelCase    (	self   :   Optional[Any] ,		UpperCAmelCase__   :   dict	):

    """simple docstring"""






    lowercase__						:	Tuple         = self.tokenizer.pad_token_id
    lowercase__     ,					lowercase__     ,					lowercase__						:	Union[str, Any]         = batch["""input_ids"""], batch["""attention_mask"""], batch["""labels"""]
    if isinstance(self.model ,		UpperCAmelCase__	):
       lowercase__						:	Optional[int]         = self.model._shift_right(UpperCAmelCase__	)
    else:
       lowercase__						:	Optional[Any]         = shift_tokens_right(UpperCAmelCase__ ,		UpperCAmelCase__	)

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

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

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

    lowercase__						:	Union[str, Any]         = 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__ ,		)
    lowercase__						:	Optional[int]         = decoder_input_ids.ne(UpperCAmelCase__	)
    lowercase__						:	Union[str, Any]         = self.calc_ce_loss(UpperCAmelCase__ ,		UpperCAmelCase__ ,		teacher_outputs["""logits"""]	)
    if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
       lowercase__						:	Optional[int]         = self.calc_hidden_loss(
           UpperCAmelCase__ ,		student_outputs["""decoder_hidden_states"""] ,		teacher_outputs["""decoder_hidden_states"""] ,		self.d_matches ,		normalize_hidden=self.hparams.normalize_hidden ,		)

    lowercase__						:	Tuple         = (
        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    (	UpperCAmelCase__   :   Optional[Any] ,		UpperCAmelCase__   :   int ,		UpperCAmelCase__   :   Tuple ,		UpperCAmelCase__   :   List[str] ,		UpperCAmelCase__   :   Tuple	):

    """simple docstring"""






    lowercase__						:	Optional[Any]         = """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}'''
    lowercase__						:	Dict         = attention_mask.to(hidden_states[0]	)
    lowercase__						:	List[str]         = mask.sum() * hidden_states[0].size(-1	)
    lowercase__						:	Optional[Any]         = torch.stack([hidden_states[i] for i in range(len(UpperCAmelCase__	)	)]	)
    lowercase__						:	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:
       lowercase__						:	Union[str, Any]         = nn.functional.layer_norm(UpperCAmelCase__ ,		student_states.shape[1:]	)
       lowercase__						:	List[Any]         = nn.functional.layer_norm(UpperCAmelCase__ ,		teacher_states.shape[1:]	)
    lowercase__						:	Union[str, Any]         = nn.functional.mse_loss(UpperCAmelCase__ ,		UpperCAmelCase__ ,		reduction="""none"""	)
    lowercase__						:	Tuple         = (mse * mask.unsqueeze(0	).unsqueeze(-1	)).sum() / valid_count
    return masked_mse
def       a__						(  A					):
   # 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=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        __snake_case(					__UpperCamelCase      ):
 lowercase :       Tuple		      =		'''translation'''
 lowercase :       int		      =		['''bleu''']
 lowercase :       List[Any]		      =		'''bleu'''



 def __init__(	self   :   List[Any] ,		UpperCAmelCase__   :   Any ,		**UpperCAmelCase__   :   str	):

    """simple docstring"""






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



 def 						UpperCAmelCase    (	self   :   Tuple ,		UpperCAmelCase__   :   Dict ,		UpperCAmelCase__   :   int	):

    """simple docstring"""






    return calculate_bleu(UpperCAmelCase__ ,		UpperCAmelCase__	)







 @staticmethod
 def 						UpperCAmelCase    (	UpperCAmelCase__   :   Union[str, Any] ,		UpperCAmelCase__   :   Optional[Any]	):

    """simple docstring"""






    TranslationModule.add_model_specific_args(UpperCAmelCase__ ,		UpperCAmelCase__	)
    add_distill_args(UpperCAmelCase__	)
    return parser
def       a__						(  A					):
   if args.no_teacher:
      lowercase__						:	Tuple         = TranslationModule if """translation""" in args.task else SummarizationModule
   else:  # DISTILL WITH TEACHER
      lowercase__						:	List[Any]         = TranslationDistiller if """translation""" in args.task else SummarizationDistiller
   lowercase__						:	str         = module_cls.__name__
   print(F'''using module {args.setup_cls}'''					)
   lowercase__						:	List[str]         = module_cls(A					)
   return model
def       a__						(  A					):
   Path(args.output_dir					).mkdir(exist_ok=A					)
   check_output_dir(A		,				expected_items=3					)

   lowercase__						:	List[str]         = create_module(A					)
   return ft_main(A		,				model=A					)


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

 distill_main(args)










a    =       10**8
def       a__						(  A					):
   if n % 10 == 0:
      return False
   lowercase__						:	Optional[Any]         = str(A					)
   return s == s[::-1]
def       a__						(  ):
   lowercase__						:	Union[str, Any]         = set()
   lowercase__						:	Dict         = 1
   lowercase__						:	List[str]         = 5
   while sum_squares < LIMIT:
      lowercase__						:	Tuple         = first_square + 1
      while sum_squares < LIMIT:
         if is_palindrome(A					):
            answer.add(A					)
         last_square += 1
         sum_squares += last_square**2
      first_square += 1
      lowercase__						:	Any         = first_square**2 + (first_square + 1) ** 2

   return sum(A					)


if __name__ == "__main__":
 print(solution())


