






'''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     _UpperCAmelCase  (						snake_case     ):
    __lowerCamelCase:				int						    =      ['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']




    def __init__(     self					:    Tuple					,       a					:    Optional[Any]     ):






       '''simple docstring'''






       assert Path(hparams.data_dir     ).exists()
       lowercase_		:      Union[str, Any]          =       Path(hparams.output_dir     )
       self.output_dir.mkdir(exist_ok=a     )

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

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

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

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

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

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




    def 			lowerCAmelCase__			(     self					:    List[str]					,       a					:    Tuple					,       a					:    int					,       a					:    List[Any]     ):






       '''simple docstring'''






       lowercase_		:      Tuple          =       mask[:, :, None].expand_as(a     )
       lowercase_		:      Union[str, Any]          =       s_logits.size(-1     )
       lowercase_		:      Optional[Any]          =       torch.masked_select(a					,       a     )  # (bs * seq_length * voc_size) modulo the 1s in mask
       lowercase_		:      Any          =       torch.masked_select(a					,       a     )  # (bs * seq_length * voc_size) modulo the 1s in mask
       lowercase_		:      Any          =       s_logits_slct.view(-1					,       a     )  # (bs * seq_length, voc_size) modulo the 1s in mask
       lowercase_		:      Optional[Any]          =       t_logits_slct.view(-1					,       a     )  # (bs * seq_length, voc_size) modulo the 1s in mask
       assert t_logits_slct.size() == s_logits_slct.size()
       lowercase_		:      Optional[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 			lowerCAmelCase__			(     a					:    List[Any]					,       a					:    Optional[Any]     ):






       '''simple docstring'''






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




    def 			lowerCAmelCase__			(     self					:    List[Any]					,       a					:    dict     ):






       '''simple docstring'''






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

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

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

       lowercase_		:      str          =       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_		:      Tuple          =       self.teacher.get_encoder()(
              a					,       attention_mask=a					,       output_hidden_states=self.do_calc_hidden_loss					,       )
          if self.different_base_models:
             lowercase_		:      int          =       all_teacher_encoder_outputs["last_hidden_state"]
          elif self.do_calc_hidden_loss:
             lowercase_		:      Dict          =       self.calc_hidden_loss(
                 a					,       student_outputs["encoder_hidden_states"]					,       all_teacher_encoder_outputs["hidden_states"]					,       self.e_matches					,       normalize_hidden=self.hparams.normalize_hidden					,       )

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

       lowercase_		:      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 			lowerCAmelCase__			(     a					:    Dict					,       a					:    Union[str, Any]					,       a					:    Optional[int]					,       a					:    int					,       a					:    Dict     ):






       '''simple docstring'''






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




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase							):


   """simple docstring"""




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


class     _UpperCAmelCase  (						snake_case     ):
    __lowerCamelCase:				Dict						    =      'translation'
    __lowerCamelCase:				int						    =      ['bleu']
    __lowerCamelCase:				Any						    =      'bleu'




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






       '''simple docstring'''






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




    def 			lowerCAmelCase__			(     self					:    List[Any]					,       a					:    int					,       a					:    List[Any]     ):






       '''simple docstring'''






       return calculate_bleu(a					,       a     )





    @staticmethod
    def 			lowerCAmelCase__			(     a					:    int					,       a					:    Dict     ):






       '''simple docstring'''






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




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase							):


   """simple docstring"""




   if args.no_teacher:
      lowercase_		:      Optional[Any]          =       TranslationModule if "translation" in args.task else SummarizationModule
   else:  # DISTILL WITH TEACHER
      lowercase_		:      str          =       TranslationDistiller if "translation" in args.task else SummarizationDistiller
   lowercase_		:      str          =       module_cls.__name__
   print(F"""using module {args.setup_cls}"""							)
   lowercase_		:      Any          =       module_cls(_UpperCamelCase							)
   return model




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase							):


   """simple docstring"""




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

   lowercase_		:      int          =       create_module(_UpperCamelCase							)
   return ft_main(_UpperCamelCase  ,						model=_UpperCamelCase							)


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

 distill_main(args)












'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCamelCase__	=     {
    'configuration_unispeech_sat': ['UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechSatConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__	=     [
     'UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'UniSpeechSatForAudioFrameClassification',
     'UniSpeechSatForCTC',
     'UniSpeechSatForPreTraining',
     'UniSpeechSatForSequenceClassification',
     'UniSpeechSatForXVector',
     'UniSpeechSatModel',
     'UniSpeechSatPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_unispeech_sat import (
      UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      UniSpeechSatForAudioFrameClassification,
      UniSpeechSatForCTC,
      UniSpeechSatForPreTraining,
      UniSpeechSatForSequenceClassification,
      UniSpeechSatForXVector,
      UniSpeechSatModel,
      UniSpeechSatPreTrainedModel,
  )

else:
 import sys

 UpperCamelCase__	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




