'''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__	(							lowercase__      ):






   """simple docstring"""
   A				:							Tuple			     =     ['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']
   def __init__(self							,					_a					)     ->  int:
        assert Path(hparams.data_dir					).exists()
        lowercase_       :							str  					=       Path(hparams.output_dir					)
        self.output_dir.mkdir(exist_ok=lowerCAmelCase__					)

        lowercase_       :							Any  					=       self.output_dir.joinpath('student'					)

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

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

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

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

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

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

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

        lowercase_       :							Union[str, Any]  					=       student_outputs[
            'encoder_last_hidden_state'
        ]  # use this unless self.different_base_models
        lowercase_					,lowercase_       :							str  					=       zero_tensor(), zero_tensor()
        if self.different_encoder:  # compute encoder hidden state loss
             lowercase_       :							str  					=       self.teacher.get_encoder()(
                 lowerCAmelCase__							,					attention_mask=lowerCAmelCase__							,					output_hidden_states=self.do_calc_hidden_loss							,					)
             if self.different_base_models:
                  lowercase_       :							List[Any]  					=       all_teacher_encoder_outputs['last_hidden_state']
             elif self.do_calc_hidden_loss:
                  lowercase_       :							Dict  					=       self.calc_hidden_loss(
                      lowerCAmelCase__							,					student_outputs['encoder_hidden_states']							,					all_teacher_encoder_outputs['hidden_states']							,					self.e_matches							,					normalize_hidden=self.hparams.normalize_hidden							,					)

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

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



def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     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    UpperCAmelCase__	(							lowercase__      ):






   """simple docstring"""
   A				:							Any			     =     '''translation'''
   A				:							Dict			     =     ['''bleu''']
   A				:							Optional[Any]			     =     '''bleu'''
   def __init__(self							,					_a							,					**_a					)     ->  List[Any]:
        super().__init__(lowerCAmelCase__							,					**lowerCAmelCase__					)
        assert hparams.src_lang is not None
        assert hparams.tgt_lang is not None
        lowercase_       :							Dict  					=       hparams.src_lang
        lowercase_       :							int  					=       hparams.tgt_lang
        if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer							,					lowerCAmelCase__					):
             lowercase_       :							Optional[Any]  					=       self.tokenizer.lang_code_to_id[hparams.tgt_lang]
   def 		_lowerCamelCase			(self							,					_a							,					_a					)     ->  dict:
        return calculate_bleu(lowerCAmelCase__							,					lowerCAmelCase__					)





   @staticmethod
   def 		_lowerCamelCase			(_a							,					_a					)     ->  Optional[int]:
        TranslationModule.add_model_specific_args(lowerCAmelCase__							,					lowerCAmelCase__					)
        add_distill_args(lowerCAmelCase__					)
        return parser



def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):

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

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     Path(args.output_dir				).mkdir(exist_ok=lowercase_				)
     check_output_dir(lowercase_	,							expected_items=3				)

     lowercase_       :							Optional[int]  					=       create_module(lowercase_				)
     return ft_main(lowercase_	,							model=lowercase_				)


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)












# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__UpperCAmelCase      =				abspath(join(dirname(dirname(dirname(__file__))), "src"))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)





def 						A_					(						lowercase_			)				->str:







 """simple docstring"""

 from transformers.testing_utils import pytest_addoption_shared

 pytest_addoption_shared(lowercase_			)





def 						A_					(						lowercase_			)				->Tuple:







 """simple docstring"""

 from transformers.testing_utils import pytest_terminal_summary_main

 SCREAMING_SNAKE_CASE				    =		terminalreporter.config.getoption('--make-reports'			)
 if make_reports:
  pytest_terminal_summary_main(lowercase_	,  id=lowercase_			)




