
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	(		A_				):
   A__      :	Any		  =  ["loss", "ce_loss", "mlm_loss", "hid_loss_enc", "hid_loss_dec"]



   def __init__(self	:      int					,		snake_case__	:      Tuple				)	->			Tuple:


          '''simple docstring'''


          assert Path(hparams.data_dir				).exists()
          snake_case :     Dict           =   Path(hparams.output_dir				)
          self.output_dir.mkdir(exist_ok=snake_case__				)

          snake_case :     Any           =   self.output_dir.joinpath("student"				)

          snake_case :     Union[str, Any]           =   str(snake_case__				)  # Tell lightning we are training the student
          snake_case :     Union[str, Any]           =   AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher				).eval()
          use_task_specific_params(snake_case__					,		hparams.task				)  # We copy good generation parameters to student by default
          if hparams.student is not None:
                 snake_case :     Optional[Any]           =   AutoModelForSeqaSeqLM.from_pretrained(hparams.student				)
                 use_task_specific_params(snake_case__					,		hparams.task				)
                 snake_case       ,      snake_case :     Tuple           =   None, None
          else:
                 snake_case       ,      snake_case       ,      snake_case :     int           =   create_student_by_copying_alternating_layers(
                     snake_case__					,		e=hparams.student_encoder_layers					,		d=hparams.student_decoder_layers					,		save_path=snake_case__				)

          if hparams.length_penalty != -1:
                 snake_case :     Union[str, Any]           =   hparams.length_penalty
          snake_case :     str           =   hparams.teacher  # Use teacher's tokenizer
          super().__init__(snake_case__					,		model=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":
                 snake_case :     Dict           =   len(student.get_encoder().block				)
                 snake_case :     Tuple           =   len(student.get_decoder().block				)
                 snake_case :     List[str]           =   len(teacher.get_encoder().block				)
                 snake_case :     int           =   len(teacher.get_decoder().block				)
          else:
                 snake_case :     List[Any]           =   student.config.encoder_layers
                 snake_case :     str           =   student.config.decoder_layers
                 snake_case :     List[str]           =   teacher.config.encoder_layers
                 snake_case :     List[str]           =   teacher.config.decoder_layers

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

          snake_case       ,      snake_case :     List[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:
                        snake_case :     str           =   get_layers_to_supervise(
                            n_student=len(self.e_layer_ids				)					,		n_teacher=snake_case__				)
                        snake_case :     Optional[Any]           =   get_layers_to_supervise(
                            n_student=len(self.d_layer_ids				)					,		n_teacher=snake_case__				)
                 else:  # student layer should emulate hidden states of the teacher layer it was copied from
                        snake_case :     Tuple           =   self.e_layer_ids
                        snake_case :     Tuple           =   self.d_layer_ids
          else:
                 snake_case :     Tuple           =   None
                 snake_case :     int           =   None

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



   def        _SCREAMING_SNAKE_CASE     (self	:      Dict					,		snake_case__	:      int					,		snake_case__	:      Optional[int]					,		snake_case__	:      Any				)	->			List[Any]:


          '''simple docstring'''


          snake_case :     Union[str, Any]           =   mask[:, :, None].expand_as(snake_case__				)
          snake_case :     str           =   s_logits.size(-1				)
          snake_case :     Tuple           =   torch.masked_select(snake_case__					,		snake_case__				)  # (bs * seq_length * voc_size) modulo the 1s in mask
          snake_case :     Optional[Any]           =   torch.masked_select(snake_case__					,		snake_case__				)  # (bs * seq_length * voc_size) modulo the 1s in mask
          snake_case :     str           =   s_logits_slct.view(-1					,		snake_case__				)  # (bs * seq_length, voc_size) modulo the 1s in mask
          snake_case :     Union[str, Any]           =   t_logits_slct.view(-1					,		snake_case__				)  # (bs * seq_length, voc_size) modulo the 1s in mask
          assert t_logits_slct.size() == s_logits_slct.size()
          snake_case :     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        _SCREAMING_SNAKE_CASE     (snake_case__	:      Optional[int]					,		snake_case__	:      Dict				)	->			int:


          '''simple docstring'''


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



   def        _SCREAMING_SNAKE_CASE     (self	:      List[Any]					,		snake_case__	:      dict				)	->			tuple:


          '''simple docstring'''


          snake_case :     Tuple           =   self.tokenizer.pad_token_id
          snake_case       ,      snake_case       ,      snake_case :     Dict           =   batch["input_ids"], batch["attention_mask"], batch["labels"]
          if isinstance(self.model					,		snake_case__				):
                 snake_case :     Optional[int]           =   self.model._shift_right(snake_case__				)
          else:
                 snake_case :     int           =   shift_tokens_right(snake_case__					,		snake_case__				)

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

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

          snake_case :     Optional[int]           =   student_outputs[
              "encoder_last_hidden_state"
          ]  # use this unless self.different_base_models
          snake_case       ,      snake_case :     List[str]           =   zero_tensor(), zero_tensor()
          if self.different_encoder:  # compute encoder hidden state loss
                 snake_case :     Dict           =   self.teacher.get_encoder()(
                     snake_case__					,		attention_mask=snake_case__					,		output_hidden_states=self.do_calc_hidden_loss					,		)
                 if self.different_base_models:
                        snake_case :     Optional[int]           =   all_teacher_encoder_outputs["last_hidden_state"]
                 elif self.do_calc_hidden_loss:
                        snake_case :     Dict           =   self.calc_hidden_loss(
                            snake_case__					,		student_outputs["encoder_hidden_states"]					,		all_teacher_encoder_outputs["hidden_states"]					,		self.e_matches					,		normalize_hidden=self.hparams.normalize_hidden					,		)

          snake_case :     str           =   self.teacher(
              snake_case__					,		attention_mask=snake_case__					,		encoder_outputs=(teacher_enc_outputs,)					,		decoder_input_ids=snake_case__					,		output_hidden_states=self.do_calc_hidden_loss					,		use_cache=snake_case__					,		)
          snake_case :     List[Any]           =   decoder_input_ids.ne(snake_case__				)
          snake_case :     Optional[Any]           =   self.calc_ce_loss(snake_case__					,		snake_case__					,		teacher_outputs["logits"]				)
          if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
                 snake_case :     Optional[Any]           =   self.calc_hidden_loss(
                     snake_case__					,		student_outputs["decoder_hidden_states"]					,		teacher_outputs["decoder_hidden_states"]					,		self.d_matches					,		normalize_hidden=self.hparams.normalize_hidden					,		)

          snake_case :     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        _SCREAMING_SNAKE_CASE     (snake_case__	:      Dict					,		snake_case__	:      int					,		snake_case__	:      List[str]					,		snake_case__	:      List[str]					,		snake_case__	:      List[Any]				)	->			Tuple:


          '''simple docstring'''


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



def     UpperCamelCase      (					__lowerCamelCase  :				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=__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	(		A_				):
   A__      :	List[str]		  =  "translation"
   A__      :	Union[str, Any]		  =  ["bleu"]
   A__      :	Dict		  =  "bleu"



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


          '''simple docstring'''


          super().__init__(snake_case__					,		**snake_case__				)
          assert hparams.src_lang is not None
          assert hparams.tgt_lang is not None
          snake_case :     str           =   hparams.src_lang
          snake_case :     Dict           =   hparams.tgt_lang
          if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer					,		snake_case__				):
                 snake_case :     List[str]           =   self.tokenizer.lang_code_to_id[hparams.tgt_lang]



   def        _SCREAMING_SNAKE_CASE     (self	:      List[Any]					,		snake_case__	:      int					,		snake_case__	:      Dict				)	->			dict:


          '''simple docstring'''


          return calculate_bleu(snake_case__					,		snake_case__				)



   @staticmethod
   def        _SCREAMING_SNAKE_CASE     (snake_case__	:      str					,		snake_case__	:      Dict				)	->			Tuple:


          '''simple docstring'''


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



def     UpperCamelCase      (					__lowerCamelCase  :				List[Any]					):
       if args.no_teacher:
              snake_case :     Dict           =   TranslationModule if "translation" in args.task else SummarizationModule
       else:  # DISTILL WITH TEACHER
              snake_case :     List[Any]           =   TranslationDistiller if "translation" in args.task else SummarizationDistiller
       snake_case :     str           =   module_cls.__name__
       print(f"""using module {args.setup_cls}"""					)
       snake_case :     List[str]           =   module_cls(__lowerCamelCase					)
       return model


def     UpperCamelCase      (					__lowerCamelCase  :				Optional[Any]					):
       Path(args.output_dir					).mkdir(exist_ok=__lowerCamelCase					)
       check_output_dir(__lowerCamelCase       ,      expected_items=3					)

       snake_case :     Union[str, Any]           =   create_module(__lowerCamelCase					)
       return ft_main(__lowerCamelCase       ,      model=__lowerCamelCase					)


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     UpperCamelCase      (					__lowerCamelCase  :				int = 998001					):
       snake_case :     Optional[Any]           =   0
       for i in range(999       ,      99       ,      -1					):  # 3 digit numbers range from 999 down to 100
              for j in range(999       ,      99       ,      -1					):
                     snake_case :     Tuple           =   str(i * j					)
                     if product_string == product_string[::-1] and i * j < n:
                            snake_case :     str           =   max(__lowerCamelCase       ,      i * j					)
       return answer


if __name__ == "__main__":
   print(F'{solution() = }')





