






'''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      __SCREAMING_SNAKE_CASE					(__A					):


  """simple docstring"""


  _a     :   Dict			 =    ['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']







  def __init__( self      ,  UpperCamelCase__  ):






         """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_    ,  a_	    = None, None
         else:
                a_    ,  a_    ,  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_    ,  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       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__  ):






         """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       _a					( UpperCamelCase__      ,  UpperCamelCase__  ):






         """simple docstring"""


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







  def       _a					( self      ,  UpperCamelCase__  ):






         """simple docstring"""


         a_	    = self.tokenizer.pad_token_id
         a_    ,  a_    ,  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_    ,  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_    ,  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       _a					( UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__  ):






         """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				(			lowercase_					:     Any      ):
       """simple docstring"""




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




class      __SCREAMING_SNAKE_CASE					(__A					):


  """simple docstring"""


  _a     :   Union[str, Any]			 =    '''translation'''
  _a     :   List[Any]			 =    ['''bleu''']
  _a     :   Optional[int]			 =    '''bleu'''







  def __init__( self      ,  UpperCamelCase__      ,  **UpperCamelCase__  ):






         """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       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__  ):






         """simple docstring"""


         return calculate_bleu(UpperCamelCase__      ,  UpperCamelCase__  )







  @staticmethod
  def       _a					( UpperCamelCase__      ,  UpperCamelCase__  ):






         """simple docstring"""


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





def 			__UpperCamelCase				(			lowercase_					:     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(lowercase_      )
       return model


def 			__UpperCamelCase				(			lowercase_					:     Dict      ):
       """simple docstring"""




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

       a_	    = create_module(lowercase_      )
       return ft_main(lowercase_      ,					model=lowercase_      )


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'''







import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
  import torch

  from transformers import BlipTextModel
  from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST




class      __SCREAMING_SNAKE_CASE					:


  """simple docstring"""


  def __init__( self      ,  UpperCamelCase__      ,  UpperCamelCase__=12      ,  UpperCamelCase__=7      ,  UpperCamelCase__=True      ,  UpperCamelCase__=True      ,  UpperCamelCase__=True      ,  UpperCamelCase__=99      ,  UpperCamelCase__=32      ,  UpperCamelCase__=32      ,  UpperCamelCase__=5      ,  UpperCamelCase__=4      ,  UpperCamelCase__=37      ,  UpperCamelCase__=0.1      ,  UpperCamelCase__=0.1      ,  UpperCamelCase__=512      ,  UpperCamelCase__=0.02      ,  UpperCamelCase__=0      ,  UpperCamelCase__=None      ,  ):






         """simple docstring"""


         a_	    = parent
         a_	    = batch_size
         a_	    = seq_length
         a_	    = is_training
         a_	    = use_input_mask
         a_	    = use_labels
         a_	    = vocab_size
         a_	    = hidden_size
         a_	    = projection_dim
         a_	    = num_hidden_layers
         a_	    = num_attention_heads
         a_	    = intermediate_size
         a_	    = dropout
         a_	    = attention_dropout
         a_	    = max_position_embeddings
         a_	    = initializer_range
         a_	    = scope
         a_	    = bos_token_id







  def       _a					( self  ):






         """simple docstring"""


         a_	    = ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size  )

         a_	    = None
         if self.use_input_mask:
                a_	    = random_attention_mask([self.batch_size, self.seq_length]  )

         if input_mask is not None:
                a_    ,  a_	    = input_mask.shape
                a_	    = np.random.randint(1      ,  seq_length - 1      ,  size=(batch_size,)  )
                for batch_idx, start_index in enumerate(UpperCamelCase__  ):
                       a_	    = 1
                       a_	    = 0

         a_	    = self.get_config()

         return config, input_ids, input_mask







  def       _a					( self  ):






         """simple docstring"""


         return BlipTextConfig(
             vocab_size=self.vocab_size      ,  hidden_size=self.hidden_size      ,  projection_dim=self.projection_dim      ,  num_hidden_layers=self.num_hidden_layers      ,  num_attention_heads=self.num_attention_heads      ,  intermediate_size=self.intermediate_size      ,  dropout=self.dropout      ,  attention_dropout=self.attention_dropout      ,  max_position_embeddings=self.max_position_embeddings      ,  initializer_range=self.initializer_range      ,  bos_token_id=self.bos_token_id      ,  )







  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__  ):






         """simple docstring"""


         a_	    = BlipTextModel(config=UpperCamelCase__  )
         model.to(UpperCamelCase__  )
         model.eval()
         with torch.no_grad():
                a_	    = model(UpperCamelCase__      ,  attention_mask=UpperCamelCase__  )
                a_	    = model(UpperCamelCase__  )
         self.parent.assertEqual(result.last_hidden_state.shape      ,  (self.batch_size, self.seq_length, self.hidden_size)  )
         self.parent.assertEqual(result.pooler_output.shape      ,  (self.batch_size, self.hidden_size)  )







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.prepare_config_and_inputs()
         a_    ,  a_    ,  a_	    = config_and_inputs
         a_	    = {'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict





@require_torch
class      __SCREAMING_SNAKE_CASE					(__A       ,					unittest.TestCase					):


  """simple docstring"""


  _a     :   Dict			 =    (BlipTextModel,) if is_torch_available() else ()
  _a     :   str			 =    False
  _a     :   str			 =    False
  _a     :   str			 =    False







  def       _a					( self  ):






         """simple docstring"""


         a_	    = BlipTextModelTester(self  )
         a_	    = ConfigTester(self      ,  config_class=UpperCamelCase__      ,  hidden_size=37  )







  def       _a					( self  ):






         """simple docstring"""


         self.config_tester.run_common_tests()







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*UpperCamelCase__  )







  def       _a					( self  ):






         """simple docstring"""


         pass







  def       _a					( self  ):






         """simple docstring"""


         pass







  @unittest.skip(reason='Blip does not use inputs_embeds'  )
  def       _a					( self  ):






         """simple docstring"""


         pass







  @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'  )
  def       _a					( self  ):






         """simple docstring"""


         pass







  @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'  )
  def       _a					( self  ):






         """simple docstring"""


         pass







  @slow
  def       _a					( self  ):






         """simple docstring"""


         for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                a_	    = BlipTextModel.from_pretrained(UpperCamelCase__  )
                self.assertIsNotNone(UpperCamelCase__  )







  def       _a					( self  ):






         """simple docstring"""


         super().test_pt_tf_model_equivalence(allow_missing_keys=UpperCamelCase__  )




