

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 							A_				(	snake_case__      ):
     _lowercase    :  int          =			['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']


     def __init__(							self    :	str    ,						UpperCAmelCase    :	List[Any]      )							->			Union[str, Any]:
      assert Path(hparams.data_dir      ).exists()
      __lowerCAmelCase:     List[Any]     			=   Path(hparams.output_dir      )
      self.output_dir.mkdir(exist_ok=UpperCAmelCase      )

      __lowerCAmelCase:     List[Any]     			=   self.output_dir.joinpath('student'      )

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

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

      __lowerCAmelCase			,							__lowerCAmelCase:     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:
        __lowerCAmelCase:     Dict     			=   get_layers_to_supervise(
            n_student=len(self.e_layer_ids      )    ,						n_teacher=UpperCAmelCase      )
        __lowerCAmelCase:     int     			=   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
        __lowerCAmelCase:     Any     			=   self.e_layer_ids
        __lowerCAmelCase:     Union[str, Any]     			=   self.d_layer_ids
      else:
       __lowerCAmelCase:     Any     			=   None
       __lowerCAmelCase:     Union[str, Any]     			=   None

      __lowerCAmelCase:     List[Any]     			=   nn.KLDivLoss(reduction='batchmean'      )
      __lowerCAmelCase:     Union[str, Any]     			=   2.0
      __lowerCAmelCase:     Optional[int]     			=   hparams.alpha_mlm
      __lowerCAmelCase:     List[Any]     			=   hparams.alpha_ce
      __lowerCAmelCase:     int     			=   hparams.alpha_hid
      gc.collect()
      torch.cuda.empty_cache()


     def 						UpperCAmelCase						(							self    :	Tuple    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	List[Any]    ,						UpperCAmelCase    :	Optional[Any]      )							->			Optional[int]:
      __lowerCAmelCase:     int     			=   mask[:, :, None].expand_as(UpperCAmelCase      )
      __lowerCAmelCase:     Optional[Any]     			=   s_logits.size(-1      )
      __lowerCAmelCase:     Tuple     			=   torch.masked_select(UpperCAmelCase    ,						UpperCAmelCase      )  # (bs * seq_length * voc_size) modulo the 1s in mask
      __lowerCAmelCase:     str     			=   torch.masked_select(UpperCAmelCase    ,						UpperCAmelCase      )  # (bs * seq_length * voc_size) modulo the 1s in mask
      __lowerCAmelCase:     Any     			=   s_logits_slct.view(-1    ,						UpperCAmelCase      )  # (bs * seq_length, voc_size) modulo the 1s in mask
      __lowerCAmelCase:     Dict     			=   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()
      __lowerCAmelCase:     Union[str, 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    :	str    ,						UpperCAmelCase    :	Any      )							->			List[Any]:
      SummarizationModule.add_model_specific_args(UpperCAmelCase    ,						UpperCAmelCase      )
      add_distill_args(UpperCAmelCase      )
      return parser


     def 						UpperCAmelCase						(							self    :	int    ,						UpperCAmelCase    :	dict      )							->			tuple:
      __lowerCAmelCase:     Tuple     			=   self.tokenizer.pad_token_id
      __lowerCAmelCase			,							__lowerCAmelCase			,							__lowerCAmelCase:     int     			=   batch['input_ids'], batch['attention_mask'], batch['labels']
      if isinstance(self.model    ,						UpperCAmelCase      ):
       __lowerCAmelCase:     Optional[int]     			=   self.model._shift_right(UpperCAmelCase      )
      else:
       __lowerCAmelCase:     Union[str, Any]     			=   shift_tokens_right(UpperCAmelCase    ,						UpperCAmelCase      )

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

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

      __lowerCAmelCase:     str     			=   student_outputs[
          'encoder_last_hidden_state'
      ]  # use this unless self.different_base_models
      __lowerCAmelCase			,							__lowerCAmelCase:     List[str]     			=   zero_tensor(), zero_tensor()
      if self.different_encoder:  # compute encoder hidden state loss
       __lowerCAmelCase:     Optional[Any]     			=   self.teacher.get_encoder()(
           UpperCAmelCase    ,						attention_mask=UpperCAmelCase    ,						output_hidden_states=self.do_calc_hidden_loss    ,						)
       if self.different_base_models:
        __lowerCAmelCase:     List[str]     			=   all_teacher_encoder_outputs['last_hidden_state']
       elif self.do_calc_hidden_loss:
        __lowerCAmelCase:     Tuple     			=   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    ,						)

      __lowerCAmelCase:     Dict     			=   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    ,						)
      __lowerCAmelCase:     Tuple     			=   decoder_input_ids.ne(UpperCAmelCase      )
      __lowerCAmelCase:     Any     			=   self.calc_ce_loss(UpperCAmelCase    ,						UpperCAmelCase    ,						teacher_outputs['logits']      )
      if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
       __lowerCAmelCase:     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    ,						)

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







def _a				( SCREAMING_SNAKE_CASE	:    Tuple	)					->					List[str]:

 """simple docstring"""






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






class 							A_				(	snake_case__      ):
     _lowercase    :  int          =			'translation'
     _lowercase    :  Union[str, Any]          =			['bleu']
     _lowercase    :  str          =			'bleu'


     def __init__(							self    :	Any    ,						UpperCAmelCase    :	Any    ,						**UpperCAmelCase    :	int      )							->			Tuple:
      super().__init__(UpperCAmelCase    ,						**UpperCAmelCase      )
      assert hparams.src_lang is not None
      assert hparams.tgt_lang is not None
      __lowerCAmelCase:     Any     			=   hparams.src_lang
      __lowerCAmelCase:     Tuple     			=   hparams.tgt_lang
      if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer    ,						UpperCAmelCase      ):
       __lowerCAmelCase:     str     			=   self.tokenizer.lang_code_to_id[hparams.tgt_lang]


     def 						UpperCAmelCase						(							self    :	List[Any]    ,						UpperCAmelCase    :	str    ,						UpperCAmelCase    :	str      )							->			dict:
      return calculate_bleu(UpperCAmelCase    ,						UpperCAmelCase      )


     @staticmethod
     def 						UpperCAmelCase						(							UpperCAmelCase    :	Dict    ,						UpperCAmelCase    :	int      )							->			Dict:
      TranslationModule.add_model_specific_args(UpperCAmelCase    ,						UpperCAmelCase      )
      add_distill_args(UpperCAmelCase      )
      return parser







def _a				( SCREAMING_SNAKE_CASE	:    Tuple	)					->					List[str]:

 """simple docstring"""






 if args.no_teacher:
  __lowerCAmelCase:     Union[str, Any]     			=   TranslationModule if 'translation' in args.task else SummarizationModule
 else:  # DISTILL WITH TEACHER
  __lowerCAmelCase:     Tuple     			=   TranslationDistiller if 'translation' in args.task else SummarizationDistiller
 __lowerCAmelCase:     str     			=   module_cls.__name__
 print(f'''using module {args.setup_cls}'''	)
 __lowerCAmelCase:     Tuple     			=   module_cls(SCREAMING_SNAKE_CASE	)
 return model







def _a				( SCREAMING_SNAKE_CASE	:    Optional[int]	)					->					Tuple:

 """simple docstring"""






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

 __lowerCAmelCase:     Tuple     			=   create_module(SCREAMING_SNAKE_CASE	)
 return ft_main(SCREAMING_SNAKE_CASE					,      model=SCREAMING_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)








# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


_a	 =     {
    '''configuration_tvlt''': ['''TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TvltConfig'''],
    '''feature_extraction_tvlt''': ['''TvltFeatureExtractor'''],
    '''processing_tvlt''': ['''TvltProcessor'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     [
      '''TVLT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TvltModel''',
      '''TvltForPreTraining''',
      '''TvltForAudioVisualClassification''',
      '''TvltPreTrainedModel''',
  ]

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     ['''TvltImageProcessor''']


if TYPE_CHECKING:
  from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
  from .processing_tvlt import TvltProcessor
  from .feature_extraction_tvlt import TvltFeatureExtractor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tvlt import (
        TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TvltForAudioVisualClassification,
        TvltForPreTraining,
        TvltModel,
        TvltPreTrainedModel,
    )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_tvlt import TvltImageProcessor


else:
  import sys

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





