

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		(				_UpperCAmelCase					):







    """simple docstring"""





    lowerCamelCase						      =				['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']


    def __init__( self					:			Dict,lowercase_					:			Tuple						)->       Tuple:





        '''simple docstring'''







        assert Path(hparams.data_dir						).exists()
        A__        =  Path(hparams.output_dir						)
        self.output_dir.mkdir(exist_ok=lowercase_						)

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

        A__        =  str(lowercase_						)  # Tell lightning we are training the student
        A__        =  AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher						).eval()
        use_task_specific_params(lowercase_,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(lowercase_,hparams.task						)
            A__ ,		A__        =  None, None
        else:
            A__ ,		A__ ,		A__        =  create_student_by_copying_alternating_layers(
                lowercase_,e=hparams.student_encoder_layers,d=hparams.student_decoder_layers,save_path=lowercase_						)

        if hparams.length_penalty != -1:
            A__        =  hparams.length_penalty
        A__        =  hparams.teacher  # Use teacher's tokenizer
        super().__init__(lowercase_,model=lowercase_,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(lowercase_						)						)
        if d_layer_ids is None:
            A__        =  list(range(lowercase_						)						)

        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=lowercase_						)
                A__        =  get_layers_to_supervise(
                    n_student=len(self.d_layer_ids						),n_teacher=lowercase_						)
            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 		snake_case__	( self					:			Dict,lowercase_					:			List[str],lowercase_					:			Dict,lowercase_					:			Optional[int]						)->       int:





        '''simple docstring'''







        A__        =  mask[:, :, None].expand_as(lowercase_						)
        A__        =  s_logits.size(-1						)
        A__        =  torch.masked_select(lowercase_,lowercase_						)  # (bs * seq_length * voc_size) modulo the 1s in mask
        A__        =  torch.masked_select(lowercase_,lowercase_						)  # (bs * seq_length * voc_size) modulo the 1s in mask
        A__        =  s_logits_slct.view(-1,lowercase_						)  # (bs * seq_length, voc_size) modulo the 1s in mask
        A__        =  t_logits_slct.view(-1,lowercase_						)  # (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 		snake_case__	( lowercase_					:			int,lowercase_					:			Optional[int]						)->       Dict:





        '''simple docstring'''







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


    def 		snake_case__	( self					:			List[str],lowercase_					:			dict						)->       tuple:





        '''simple docstring'''







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

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

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

        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()(
                lowercase_,attention_mask=lowercase_,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(
                    lowercase_,student_outputs['encoder_hidden_states'],all_teacher_encoder_outputs['hidden_states'],self.e_matches,normalize_hidden=self.hparams.normalize_hidden,)

        A__        =  self.teacher(
            lowercase_,attention_mask=lowercase_,encoder_outputs=(teacher_enc_outputs,),decoder_input_ids=lowercase_,output_hidden_states=self.do_calc_hidden_loss,use_cache=lowercase_,)
        A__        =  decoder_input_ids.ne(lowercase_						)
        A__        =  self.calc_ce_loss(lowercase_,lowercase_,teacher_outputs['logits']						)
        if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
            A__        =  self.calc_hidden_loss(
                lowercase_,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 		snake_case__	( lowercase_					:			Optional[int],lowercase_					:			List[str],lowercase_					:			List[str],lowercase_					:			Any,lowercase_					:			Tuple						)->       List[Any]:





        '''simple docstring'''







        A__        =  'expected list or tuple for hidden_states, got tensor of shape: '
        assert not isinstance(lowercase_,torch.Tensor						), F'{msg}{hidden_states.shape}'
        assert not isinstance(lowercase_,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(lowercase_						)						)]						)
        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(lowercase_,student_states.shape[1:]						)
            A__        =  nn.functional.layer_norm(lowercase_,teacher_states.shape[1:]						)
        A__        =  nn.functional.mse_loss(lowercase_,lowercase_,reduction='none'						)
        A__        =  (mse * mask.unsqueeze(0						).unsqueeze(-1						)).sum() / valid_count
        return masked_mse




def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      List[str] )				-> Dict:





    '''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		(				_UpperCAmelCase					):







    """simple docstring"""





    lowerCamelCase						      =				'translation'
    lowerCamelCase						      =				['bleu']
    lowerCamelCase						      =				'bleu'


    def __init__( self					:			Tuple,lowercase_					:			List[Any],**lowercase_					:			int						)->       List[Any]:





        '''simple docstring'''







        super().__init__(lowercase_,**lowercase_						)
        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,lowercase_						):
            A__        =  self.tokenizer.lang_code_to_id[hparams.tgt_lang]


    def 		snake_case__	( self					:			Dict,lowercase_					:			Optional[int],lowercase_					:			Optional[int]						)->       dict:





        '''simple docstring'''







        return calculate_bleu(lowercase_,lowercase_						)


    @staticmethod
    def 		snake_case__	( lowercase_					:			Tuple,lowercase_					:			List[Any]						)->       Optional[int]:





        '''simple docstring'''







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




def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Union[str, Any] )				-> List[Any]:





    '''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(SCREAMING_SNAKE_CASE__ )
    return model
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      int )				-> int:





    '''simple docstring'''






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

    A__        =  create_module(SCREAMING_SNAKE_CASE__ )
    return ft_main(SCREAMING_SNAKE_CASE__       ,     model=SCREAMING_SNAKE_CASE__ )


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

     distill_main(args)




import timeit


class 					A		:







    """simple docstring"""





    def __init__( self					:			Optional[int],lowercase_					:			Union[str, Any]						)->       List[str]:





        '''simple docstring'''







        if isinstance(lowercase_,lowercase_						):  # Initializes a matrix identical to the one provided.
            A__        =  arg
            A__        =  len(lowercase_						)
        else:  # Initializes a square matrix of the given size and set values to zero.
            A__        =  arg
            A__        =  [[0 for _ in range(self.n						)] for _ in range(self.n						)]


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





        '''simple docstring'''







        A__        =  Matrix(self.n						)
        for i in range(self.n						):
            for j in range(self.n						):
                for k in range(self.n						):
                    matrix.t[i][j] += self.t[i][k] * b.t[k][j]
        return matrix




def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      List[Any]       ,     SCREAMING_SNAKE_CASE__     :      Optional[Any] )				-> Union[str, Any]:





    '''simple docstring'''






    A__        =  Matrix([[1, 0], [0, 1]] )
    while b > 0:
        if b & 1:
            matrix *= a
        a *= a
        b >>= 1
    return matrix
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Optional[int]       ,     SCREAMING_SNAKE_CASE__     :      str       ,     SCREAMING_SNAKE_CASE__     :      Any )				-> List[str]:





    '''simple docstring'''






    if n == 1:
        return fa
    elif n == 2:
        return fa
    A__        =  Matrix([[1, 1], [1, 0]] )
    A__        =  modular_exponentiation(SCREAMING_SNAKE_CASE__       ,     n - 2 )
    return fa * matrix.t[0][0] + fa * matrix.t[0][1]
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Dict       ,     SCREAMING_SNAKE_CASE__     :      Tuple       ,     SCREAMING_SNAKE_CASE__     :      Tuple )				-> Dict:





    '''simple docstring'''






    if n == 1:
        return fa
    elif n == 2:
        return fa

    A__        =  fa
    A__        =  fa
    n -= 2

    while n > 0:
        A__ ,		A__        =  fn_a + fn_a, fn_a
        n -= 1

    return fn_a
def 		_snake_case(       )				-> Optional[Any]:





    '''simple docstring'''






    A__        =  '\nfrom random import randint\nfrom __main__ import fibonacci_with_matrix_exponentiation\n'
    A__        =  'fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'
    A__        =  timeit.timeit(setup=SCREAMING_SNAKE_CASE__       ,     stmt=SCREAMING_SNAKE_CASE__       ,     number=100 )
    print('With matrix exponentiation the average execution time is '       ,     exec_time / 100 )
    return exec_time
def 		_snake_case(       )				-> Optional[int]:





    '''simple docstring'''






    A__        =  '\nfrom random import randint\nfrom __main__ import simple_fibonacci\n'
    A__        =  'simple_fibonacci(randint(1,70000), 1, 1)'
    A__        =  timeit.timeit(setup=SCREAMING_SNAKE_CASE__       ,     stmt=SCREAMING_SNAKE_CASE__       ,     number=100 )
    print(
        'Without matrix exponentiation the average execution time is '       ,     exec_time / 100 )
    return exec_time
def 		_snake_case(       )				-> Union[str, Any]:





    '''simple docstring'''






    matrix_exponentiation_time()
    simple_fibonacci_time()


if __name__ == "__main__":
     main()

