
'''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__       ):
 UpperCAmelCase_		     =    ["""loss""", """ce_loss""", """mlm_loss""", """hid_loss_enc""", """hid_loss_dec"""]





 def __init__(     self			,							lowercase_):
     assert Path(hparams.data_dir).exists()
     snake_case_			:     Any        =    Path(hparams.output_dir)
     self.output_dir.mkdir(exist_ok=lowercase_)

     snake_case_			:     int        =    self.output_dir.joinpath("student")

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

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

     snake_case_      ,			snake_case_			:     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:
             snake_case_			:     List[Any]        =    get_layers_to_supervise(
                 n_student=len(self.e_layer_ids)			,							n_teacher=lowercase_)
             snake_case_			:     Optional[int]        =    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
             snake_case_			:     str        =    self.e_layer_ids
             snake_case_			:     List[str]        =    self.d_layer_ids
     else:
         snake_case_			:     List[Any]        =    None
         snake_case_			:     Union[str, Any]        =    None

     snake_case_			:     List[Any]        =    nn.KLDivLoss(reduction="batchmean")
     snake_case_			:     int        =    2.0
     snake_case_			:     List[Any]        =    hparams.alpha_mlm
     snake_case_			:     Any        =    hparams.alpha_ce
     snake_case_			:     Tuple        =    hparams.alpha_hid
     gc.collect()
     torch.cuda.empty_cache()





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_			,							lowercase_):
     snake_case_			:     List[str]        =    mask[:, :, None].expand_as(lowercase_)
     snake_case_			:     int        =    s_logits.size(-1)
     snake_case_			:     List[Any]        =    torch.masked_select(lowercase_			,							lowercase_)  # (bs * seq_length * voc_size) modulo the 1s in mask
     snake_case_			:     str        =    torch.masked_select(lowercase_			,							lowercase_)  # (bs * seq_length * voc_size) modulo the 1s in mask
     snake_case_			:     List[str]        =    s_logits_slct.view(-1			,							lowercase_)  # (bs * seq_length, voc_size) modulo the 1s in mask
     snake_case_			:     List[str]        =    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()
     snake_case_			:     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 							snake_case__		(     lowercase_			,							lowercase_):
     SummarizationModule.add_model_specific_args(lowercase_			,							lowercase_)
     add_distill_args(lowercase_)
     return parser





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

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

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

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

     snake_case_			:     List[str]        =    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_			,							)
     snake_case_			:     Optional[int]        =    decoder_input_ids.ne(lowercase_)
     snake_case_			:     str        =    self.calc_ce_loss(lowercase_			,							lowercase_			,							teacher_outputs["logits"])
     if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
         snake_case_			:     int        =    self.calc_hidden_loss(
             lowercase_			,							student_outputs["decoder_hidden_states"]			,							teacher_outputs["decoder_hidden_states"]			,							self.d_matches			,							normalize_hidden=self.hparams.normalize_hidden			,							)

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


def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """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=1_2,		type=__SCREAMING_SNAKE_CASE,		required=__SCREAMING_SNAKE_CASE							)
    parser.add_argument("--student_encoder_layers",		default=1_2,		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  UpperCAmelCase_					(						snake_case__       ):
 UpperCAmelCase_		     =    """translation"""
 UpperCAmelCase_		     =    ["""bleu"""]
 UpperCAmelCase_		     =    """bleu"""





 def __init__(     self			,							lowercase_			,							**lowercase_):
     super().__init__(lowercase_			,							**lowercase_)
     assert hparams.src_lang is not None
     assert hparams.tgt_lang is not None
     snake_case_			:     Tuple        =    hparams.src_lang
     snake_case_			:     Optional[int]        =    hparams.tgt_lang
     if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer			,							lowercase_):
         snake_case_			:     Optional[Any]        =    self.tokenizer.lang_code_to_id[hparams.tgt_lang]





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_):
     return calculate_bleu(lowercase_			,							lowercase_)





 @staticmethod
 def 							snake_case__		(     lowercase_			,							lowercase_):
     TranslationModule.add_model_specific_args(lowercase_			,							lowercase_)
     add_distill_args(lowercase_)
     return parser


def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






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

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






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

    snake_case_			:     int        =    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)






'''simple docstring'''
def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if right <= left:
        return
    snake_case_			:     Dict        =    left
    snake_case_			:     Tuple        =    right
    snake_case_			:     List[str]        =    sorting[left]
    while i <= b:
        if sorting[i] < pivot:
            snake_case_      ,			snake_case_			:     Union[str, Any]        =    sorting[i], sorting[a]
            a += 1
            i += 1
        elif sorting[i] > pivot:
            snake_case_      ,			snake_case_			:     List[str]        =    sorting[i], sorting[b]
            b -= 1
        else:
            i += 1
    quick_sort_apartition(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		a - 1							)
    quick_sort_apartition(__SCREAMING_SNAKE_CASE,		b + 1,		__SCREAMING_SNAKE_CASE							)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if left < right:
        snake_case_			:     Dict        =    lomuto_partition(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							)
        quick_sort_lomuto_partition(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		pivot_index - 1							)
        quick_sort_lomuto_partition(__SCREAMING_SNAKE_CASE,		pivot_index + 1,		__SCREAMING_SNAKE_CASE							)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     List[Any]        =    sorting[right]
    snake_case_			:     Optional[int]        =    left
    for i in range(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
        if sorting[i] < pivot:
            snake_case_      ,			snake_case_			:     Dict        =    sorting[i], sorting[store_index]
            store_index += 1
    snake_case_      ,			snake_case_			:     Optional[Any]        =    sorting[store_index], sorting[right]
    return store_index

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if len(__SCREAMING_SNAKE_CASE							) <= 1:
        return sorting
    return (
        three_way_radix_quicksort([i for i in sorting if i < sorting[0]]							)
        + [i for i in sorting if i == sorting[0]]
        + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]							)
    )


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)

  a_           =    input("Enter numbers separated by a comma:\n").strip()
  a_           =    [int(item) for item in user_input.split(",")]
  quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
  print(unsorted)




