

'''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    :       Tuple      				=   ['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']
   def __init__(	self     : Dict       ,SCREAMING_SNAKE_CASE__     : int							):
     assert Path(hparams.data_dir							).exists()
     SCREAMING_SNAKE_CASE:str   =   Path(hparams.output_dir							)
     self.output_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Optional[Any]   =   self.output_dir.joinpath("student"							)

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

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

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

     SCREAMING_SNAKE_CASE:Optional[Any]   =   nn.KLDivLoss(reduction="batchmean"							)
     SCREAMING_SNAKE_CASE:Optional[Any]   =   2.0
     SCREAMING_SNAKE_CASE:Optional[int]   =   hparams.alpha_mlm
     SCREAMING_SNAKE_CASE:List[Any]   =   hparams.alpha_ce
     SCREAMING_SNAKE_CASE:List[Any]   =   hparams.alpha_hid
     gc.collect()
     torch.cuda.empty_cache()
   def       __UpperCamelCase  (	self     : List[str]       ,SCREAMING_SNAKE_CASE__     : Optional[int]       ,SCREAMING_SNAKE_CASE__     : int       ,SCREAMING_SNAKE_CASE__     : Union[str, Any]							):
     SCREAMING_SNAKE_CASE:str   =   mask[:, :, None].expand_as(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   s_logits.size(-1							)
     SCREAMING_SNAKE_CASE:Optional[int]   =   torch.masked_select(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							)  # (bs * seq_length * voc_size) modulo the 1s in mask
     SCREAMING_SNAKE_CASE:Tuple   =   torch.masked_select(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							)  # (bs * seq_length * voc_size) modulo the 1s in mask
     SCREAMING_SNAKE_CASE:Tuple   =   s_logits_slct.view(-1       ,SCREAMING_SNAKE_CASE__							)  # (bs * seq_length, voc_size) modulo the 1s in mask
     SCREAMING_SNAKE_CASE:int   =   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()
     SCREAMING_SNAKE_CASE:str   =   (
         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  (	SCREAMING_SNAKE_CASE__     : Optional[Any]       ,SCREAMING_SNAKE_CASE__     : str							):
     SummarizationModule.add_model_specific_args(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							)
     add_distill_args(SCREAMING_SNAKE_CASE__							)
     return parser
   def       __UpperCamelCase  (	self     : int       ,SCREAMING_SNAKE_CASE__     : dict							):
     SCREAMING_SNAKE_CASE:Optional[int]   =   self.tokenizer.pad_token_id
     SCREAMING_SNAKE_CASE:List[str]   =   batch["input_ids"], batch["attention_mask"], batch["labels"]
     if isinstance(self.model       ,SCREAMING_SNAKE_CASE__							):
       SCREAMING_SNAKE_CASE:Union[str, Any]   =   self.model._shift_right(SCREAMING_SNAKE_CASE__							)
     else:
       SCREAMING_SNAKE_CASE:int   =   shift_tokens_right(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							)

     # noinspection PyCallingNonCallable
     SCREAMING_SNAKE_CASE:Dict   =   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__       ,)
     SCREAMING_SNAKE_CASE: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
       SCREAMING_SNAKE_CASE:int   =   nn.CrossEntropyLoss(ignore_index=SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:Dict   =   loss_fct(lm_logits.view(-1       ,lm_logits.shape[-1]							)       ,labels.view(-1							)							)
     else:
       SCREAMING_SNAKE_CASE:str   =   nn.functional.log_softmax(SCREAMING_SNAKE_CASE__       ,dim=-1							)
       SCREAMING_SNAKE_CASE:int   =   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__							)

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

     SCREAMING_SNAKE_CASE:List[str]   =   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__       ,)
     SCREAMING_SNAKE_CASE:Dict   =   decoder_input_ids.ne(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[Any]   =   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
       SCREAMING_SNAKE_CASE:Optional[int]   =   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       ,)

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





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





class 							_snake_case	(    _a  ):
   _A    :       str      				=   '''translation'''
   _A    :       List[Any]      				=   ['''bleu''']
   _A    :       Union[str, Any]      				=   '''bleu'''
   def __init__(	self     : Tuple       ,SCREAMING_SNAKE_CASE__     : Union[str, Any]       ,**SCREAMING_SNAKE_CASE__     : Dict							):
     super().__init__(SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__							)
     assert hparams.src_lang is not None
     assert hparams.tgt_lang is not None
     SCREAMING_SNAKE_CASE:str   =   hparams.src_lang
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   hparams.tgt_lang
     if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer       ,SCREAMING_SNAKE_CASE__							):
       SCREAMING_SNAKE_CASE:int   =   self.tokenizer.lang_code_to_id[hparams.tgt_lang]
   def       __UpperCamelCase  (	self     : List[str]       ,SCREAMING_SNAKE_CASE__     : List[Any]       ,SCREAMING_SNAKE_CASE__     : Any							):
     return calculate_bleu(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							)







   @staticmethod
   def       __UpperCamelCase  (	SCREAMING_SNAKE_CASE__     : int       ,SCREAMING_SNAKE_CASE__     : Any							):
     TranslationModule.add_model_specific_args(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							)
     add_distill_args(SCREAMING_SNAKE_CASE__							)
     return parser





def 				A_					(	snake_case	):
  if args.no_teacher:
    SCREAMING_SNAKE_CASE:Union[str, Any]   =   TranslationModule if "translation" in args.task else SummarizationModule
  else:  # DISTILL WITH TEACHER
    SCREAMING_SNAKE_CASE:int   =   TranslationDistiller if "translation" in args.task else SummarizationDistiller
  SCREAMING_SNAKE_CASE:str   =   module_cls.__name__
  print(F'''using module {args.setup_cls}'''	)
  SCREAMING_SNAKE_CASE:List[str]   =   module_cls(snake_case	)
  return model





def 				A_					(	snake_case	):
  Path(args.output_dir	).mkdir(exist_ok=snake_case	)
  check_output_dir(snake_case ,    expected_items=3	)

  SCREAMING_SNAKE_CASE:Any   =   create_module(snake_case	)
  return ft_main(snake_case ,    model=snake_case	)


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)









'''simple docstring'''

from collections import Counter





def 				A_					(	snake_case	):
  return sum(socks_by_color // 2 for socks_by_color in Counter(snake_case	).values()	)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    A_      =							[int(x) for x in input("Enter socks by color :").rstrip().split()]
    print(f'''sock_merchant({colors}) = {sock_merchant(colors)}''')






