

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 		__magic_name__     (				a__				):

   '''simple docstring'''




   lowerCamelCase__					:      int        =  ['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']



   def __init__(							self,       lowercase_			)				->       Any:






     """simple docstring"""






     assert Path(hparams.data_dir			).exists()
     a__    =Path(hparams.output_dir			)
     self.output_dir.mkdir(exist_ok=UpperCamelCase_			)

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

     a__    =str(UpperCamelCase_			)  # Tell lightning we are training the student
     a__    =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:
       a__    =AutoModelForSeqaSeqLM.from_pretrained(hparams.student			)
       use_task_specific_params(UpperCamelCase_,       hparams.task			)
       a__    =None, None
     else:
       a__    =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:
       a__    =hparams.length_penalty
     a__    =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":
       a__    =len(student.get_encoder().block			)
       a__    =len(student.get_decoder().block			)
       a__    =len(teacher.get_encoder().block			)
       a__    =len(teacher.get_decoder().block			)
     else:
       a__    =student.config.encoder_layers
       a__    =student.config.decoder_layers
       a__    =teacher.config.encoder_layers
       a__    =teacher.config.decoder_layers

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

     a__    =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:
         a__    =get_layers_to_supervise(
             n_student=len(self.e_layer_ids			),       n_teacher=UpperCamelCase_			)
         a__    =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
         a__    =self.e_layer_ids
         a__    =self.d_layer_ids
     else:
       a__    =None
       a__    =None

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



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_,       lowercase_			)				->       List[Any]:






     """simple docstring"""






     a__    =mask[:, :, None].expand_as(UpperCamelCase_			)
     a__    =s_logits.size(-1			)
     a__    =torch.masked_select(UpperCamelCase_,       UpperCamelCase_			)  # (bs * seq_length * voc_size) modulo the 1s in mask
     a__    =torch.masked_select(UpperCamelCase_,       UpperCamelCase_			)  # (bs * seq_length * voc_size) modulo the 1s in mask
     a__    =s_logits_slct.view(-1,       UpperCamelCase_			)  # (bs * seq_length, voc_size) modulo the 1s in mask
     a__    =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()
     a__    =(
         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					(							lowercase_,       lowercase_			)				->       Tuple:






     """simple docstring"""






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



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       Optional[Any]:






     """simple docstring"""






     a__    =self.tokenizer.pad_token_id
     a__    =batch['''input_ids'''], batch['''attention_mask'''], batch['''labels''']
     if isinstance(self.model,       UpperCamelCase_			):
       a__    =self.model._shift_right(UpperCamelCase_			)
     else:
       a__    =shift_tokens_right(UpperCamelCase_,       UpperCamelCase_			)

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

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

     a__    =student_outputs[
         '''encoder_last_hidden_state'''
     ]  # use this unless self.different_base_models
     a__    =zero_tensor(), zero_tensor()
     if self.different_encoder:  # compute encoder hidden state loss
       a__    =self.teacher.get_encoder()(
           UpperCamelCase_,       attention_mask=UpperCamelCase_,       output_hidden_states=self.do_calc_hidden_loss,       )
       if self.different_base_models:
         a__    =all_teacher_encoder_outputs['''last_hidden_state''']
       elif self.do_calc_hidden_loss:
         a__    =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,       )

     a__    =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_,       )
     a__    =decoder_input_ids.ne(UpperCamelCase_			)
     a__    =self.calc_ce_loss(UpperCamelCase_,       UpperCamelCase_,       teacher_outputs['''logits''']			)
     if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
       a__    =self.calc_hidden_loss(
           UpperCamelCase_,       student_outputs['''decoder_hidden_states'''],       teacher_outputs['''decoder_hidden_states'''],       self.d_matches,       normalize_hidden=self.hparams.normalize_hidden,       )

     a__    =(
         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					(							lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_			)				->       Optional[int]:






     """simple docstring"""






     a__    ='''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}"""
     a__    =attention_mask.to(hidden_states[0]			)
     a__    =mask.sum() * hidden_states[0].size(-1			)
     a__    =torch.stack([hidden_states[i] for i in range(len(UpperCamelCase_			)			)]			)
     a__    =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:
       a__    =nn.functional.layer_norm(UpperCamelCase_,       student_states.shape[1:]			)
       a__    =nn.functional.layer_norm(UpperCamelCase_,       teacher_states.shape[1:]			)
     a__    =nn.functional.mse_loss(UpperCamelCase_,       UpperCamelCase_,       reduction='''none'''			)
     a__    =(mse * mask.unsqueeze(0			).unsqueeze(-1			)).sum() / valid_count
     return masked_mse





def      UpperCAmelCase__							(     _A :					Union[str, Any]				):





  '''simple docstring'''







  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 		__magic_name__     (				a__				):

   '''simple docstring'''




   lowerCamelCase__					:      int        =  'translation'
   lowerCamelCase__					:      int        =  ['bleu']
   lowerCamelCase__					:      Dict        =  'bleu'



   def __init__(							self,       lowercase_,       **lowercase_			)				->       Optional[int]:






     """simple docstring"""






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



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_			)				->       Union[str, Any]:






     """simple docstring"""






     return calculate_bleu(UpperCamelCase_,       UpperCamelCase_			)



   @staticmethod
   def 					_UpperCAmelCase					(							lowercase_,       lowercase_			)				->       Optional[Any]:






     """simple docstring"""






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





def      UpperCAmelCase__							(     _A :					Optional[int]				):





  '''simple docstring'''







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




def      UpperCAmelCase__							(     _A :					List[str]				):





  '''simple docstring'''







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

  a__    =create_module(_snake_case				)
  return ft_main(_snake_case      , model=_snake_case				)


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)




"""simple docstring"""





from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__		:  Optional[Any]     					=					logging.get_logger(__name__)

snake_case__		:  str     					=					{
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}



class 		snake_case_(							a__      ):
     __UpperCamelCase	=       '''prophetnet'''
     __UpperCamelCase	=       ['''past_key_values''']
     __UpperCamelCase	=       {
         '''num_attention_heads''': '''num_encoder_attention_heads''',
     }






     def __init__(						self	:       Union[str, Any]       ,					UpperCamelCase_	:       Optional[float] = 0.1       ,					UpperCamelCase_	:       Optional[Union[str, Callable]] = "gelu"       ,					UpperCamelCase_	:       Optional[int] = 3_0_5_2_2       ,					UpperCamelCase_	:       Optional[int] = 1_0_2_4       ,					UpperCamelCase_	:       Optional[int] = 4_0_9_6       ,					UpperCamelCase_	:       Optional[int] = 1_2       ,					UpperCamelCase_	:       Optional[int] = 1_6       ,					UpperCamelCase_	:       Optional[int] = 4_0_9_6       ,					UpperCamelCase_	:       Optional[int] = 1_2       ,					UpperCamelCase_	:       Optional[int] = 1_6       ,					UpperCamelCase_	:       Optional[float] = 0.1       ,					UpperCamelCase_	:       Optional[float] = 0.1       ,					UpperCamelCase_	:       Optional[int] = 5_1_2       ,					UpperCamelCase_	:       Optional[float] = 0.02       ,					UpperCamelCase_	:       Optional[bool] = True       ,					UpperCamelCase_	:       Optional[bool] = True       ,					UpperCamelCase_	:       Optional[int] = 0       ,					UpperCamelCase_	:       Optional[int] = 2       ,					UpperCamelCase_	:       Optional[int] = 3_2       ,					UpperCamelCase_	:       Optional[int] = 1_2_8       ,					UpperCamelCase_	:       Optional[bool] = False       ,					UpperCamelCase_	:       Optional[float] = 0.0       ,					UpperCamelCase_	:       Optional[bool] = True       ,					UpperCamelCase_	:       Optional[int] = 0       ,					UpperCamelCase_	:       Optional[int] = 1       ,					UpperCamelCase_	:       Optional[int] = 2       ,					**UpperCamelCase_	:       List[Any]       ,					):
           lowerCAmelCase       :						Union[str, Any]							= vocab_size
           lowerCAmelCase       :						Any							= hidden_size
           lowerCAmelCase       :						Tuple							= encoder_ffn_dim
           lowerCAmelCase       :						str							= num_encoder_layers
           lowerCAmelCase       :						int							= num_encoder_attention_heads
           lowerCAmelCase       :						Optional[Any]							= decoder_ffn_dim
           lowerCAmelCase       :						Union[str, Any]							= num_decoder_layers
           lowerCAmelCase       :						Union[str, Any]							= num_decoder_attention_heads
           lowerCAmelCase       :						List[Any]							= max_position_embeddings
           lowerCAmelCase       :						Dict							= init_std  # Normal(0, this parameter)
           lowerCAmelCase       :						Optional[int]							= activation_function

           # parameters for prophetnet
           lowerCAmelCase       :						Tuple							= ngram
           lowerCAmelCase       :						List[Any]							= num_buckets
           lowerCAmelCase       :						Optional[Any]							= relative_max_distance
           lowerCAmelCase       :						Dict							= disable_ngram_loss
           lowerCAmelCase       :						Optional[int]							= eps

           # 3 Types of Dropout
           lowerCAmelCase       :						Any							= attention_dropout
           lowerCAmelCase       :						List[Any]							= activation_dropout
           lowerCAmelCase       :						Tuple							= dropout

           lowerCAmelCase       :						Optional[int]							= use_cache

           super().__init__(
               pad_token_id=UpperCamelCase_       ,					bos_token_id=UpperCamelCase_       ,					eos_token_id=UpperCamelCase_       ,					is_encoder_decoder=UpperCamelCase_       ,					add_cross_attention=UpperCamelCase_       ,					decoder_start_token_id=UpperCamelCase_       ,					**UpperCamelCase_       ,					)






     @property
     def   lowerCamelCase__  (						self	:       str					):
           return self.num_encoder_layers + self.num_decoder_layers






     @num_hidden_layers.setter
     def   lowerCamelCase__  (						self	:       Optional[Any]       ,					UpperCamelCase_	:       int					):
           raise NotImplementedError(
               '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
               ''' `num_decoder_layers`.'''					)


