
'''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 					a_					(_a      ):
       __lowerCAmelCase      :			Any      = ["""loss""", """ce_loss""", """mlm_loss""", """hid_loss_enc""", """hid_loss_dec"""]




       def __init__(       self				,				snake_case_					):
         assert Path(hparams.data_dir					).exists()
         _lowerCAmelCase    :					Optional[Any]							=   Path(hparams.output_dir					)
         self.output_dir.mkdir(exist_ok=snake_case_					)

         _lowerCAmelCase    :					Optional[Any]							=   self.output_dir.joinpath("""student"""					)

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

         if hparams.length_penalty != -1:
           _lowerCAmelCase    :					str							=   hparams.length_penalty
         _lowerCAmelCase    :					Dict							=   hparams.teacher  # Use teacher's tokenizer
         super().__init__(snake_case_				,				model=snake_case_				,				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    :					Optional[Any]							=   len(student.get_encoder().block					)
           _lowerCAmelCase    :					Tuple							=   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    :					int							=   student.config.decoder_layers
           _lowerCAmelCase    :					Optional[Any]							=   teacher.config.encoder_layers
           _lowerCAmelCase    :					List[str]							=   teacher.config.decoder_layers

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

         _lowerCAmelCase			,   _lowerCAmelCase    :					int							=   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    :					Optional[Any]							=   get_layers_to_supervise(
                 n_student=len(self.e_layer_ids					)				,				n_teacher=snake_case_					)
             _lowerCAmelCase    :					str							=   get_layers_to_supervise(
                 n_student=len(self.d_layer_ids					)				,				n_teacher=snake_case_					)
           else:  # student layer should emulate hidden states of the teacher layer it was copied from
             _lowerCAmelCase    :					Union[str, Any]							=   self.e_layer_ids
             _lowerCAmelCase    :					Tuple							=   self.d_layer_ids
         else:
           _lowerCAmelCase    :					List[Any]							=   None
           _lowerCAmelCase    :					List[Any]							=   None

         _lowerCAmelCase    :					int							=   nn.KLDivLoss(reduction="""batchmean"""					)
         _lowerCAmelCase    :					str							=   2.0
         _lowerCAmelCase    :					Optional[int]							=   hparams.alpha_mlm
         _lowerCAmelCase    :					int							=   hparams.alpha_ce
         _lowerCAmelCase    :					Dict							=   hparams.alpha_hid
         gc.collect()
         torch.cuda.empty_cache()




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




       def 							__UpperCamelCase    (       self				,				snake_case_					):
         _lowerCAmelCase    :					int							=   self.tokenizer.pad_token_id
         _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase    :					Union[str, Any]							=   batch["""input_ids"""], batch["""attention_mask"""], batch["""labels"""]
         if isinstance(self.model				,				snake_case_					):
           _lowerCAmelCase    :					Tuple							=   self.model._shift_right(snake_case_					)
         else:
           _lowerCAmelCase    :					List[str]							=   shift_tokens_right(snake_case_				,				snake_case_					)

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

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

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

         _lowerCAmelCase    :					List[str]							=   self.teacher(
             snake_case_				,				attention_mask=snake_case_				,				encoder_outputs=(teacher_enc_outputs,)				,				decoder_input_ids=snake_case_				,				output_hidden_states=self.do_calc_hidden_loss				,				use_cache=snake_case_				,				)
         _lowerCAmelCase    :					Any							=   decoder_input_ids.ne(snake_case_					)
         _lowerCAmelCase    :					Optional[Any]							=   self.calc_ce_loss(snake_case_				,				snake_case_				,				teacher_outputs["""logits"""]					)
         if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
           _lowerCAmelCase    :					Optional[int]							=   self.calc_hidden_loss(
               snake_case_				,				student_outputs["""decoder_hidden_states"""]				,				teacher_outputs["""decoder_hidden_states"""]				,				self.d_matches				,				normalize_hidden=self.hparams.normalize_hidden				,				)

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



def 		_UpperCAmelCase			(					_lowerCamelCase  :    List[Any]						)      ->  List[Any]:
  # NOTE: if --student argument was specified and the teacher and student base models
  # are different, the models still have to have the same tokenizer, specified by
  # --tokenizer_name. So, for example, you can distill from t5_large to t5_small but not
  # from bart to t5. This s because if the tokenizers are different, the output space
  # for the two models is also different and their logits are not comparable.
  parser.add_argument("""--teacher"""					, type=_lowerCamelCase						)
  parser.add_argument("""--alpha_ce"""					, default=0.8					, type=_lowerCamelCase						)
  parser.add_argument("""--alpha_mlm"""					, default=0.2					, type=_lowerCamelCase						)
  parser.add_argument("""--alpha_hid"""					, default=0.0					, type=_lowerCamelCase					, required=_lowerCamelCase						)
  parser.add_argument("""--student"""					, type=_lowerCamelCase					, required=_lowerCamelCase						)
  parser.add_argument("""--student_decoder_layers"""					, default=12					, type=_lowerCamelCase					, required=_lowerCamelCase						)
  parser.add_argument("""--student_encoder_layers"""					, default=12					, type=_lowerCamelCase					, required=_lowerCamelCase						)
  parser.add_argument("""--no_teacher"""					, action="""store_true"""					, default=_lowerCamelCase						)
  parser.add_argument("""--length_penalty"""					, type=_lowerCamelCase					, default=-1						)
  parser.add_argument("""--supervise_forward"""					, action="""store_true"""					, default=_lowerCamelCase						)
  parser.add_argument("""--normalize_hidden"""					, action="""store_true"""					, default=_lowerCamelCase						)


class 					a_					(_a      ):
       __lowerCAmelCase      :			Optional[int]      = """translation"""
       __lowerCAmelCase      :			Optional[Any]      = ["""bleu"""]
       __lowerCAmelCase      :			List[Any]      = """bleu"""




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




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_					):
         return calculate_bleu(snake_case_				,				snake_case_					)







       @staticmethod
       def 							__UpperCamelCase    (       snake_case_				,				snake_case_					):
         TranslationModule.add_model_specific_args(snake_case_				,				snake_case_					)
         add_distill_args(snake_case_					)
         return parser



def 		_UpperCAmelCase			(					_lowerCamelCase  :    str						)      ->  Optional[Any]:
  if args.no_teacher:
    _lowerCAmelCase    :					Any							=   TranslationModule if """translation""" in args.task else SummarizationModule
  else:  # DISTILL WITH TEACHER
    _lowerCAmelCase    :					Dict							=   TranslationDistiller if """translation""" in args.task else SummarizationDistiller
  _lowerCAmelCase    :					str							=   module_cls.__name__
  print(f'using module {args.setup_cls}'						)
  _lowerCAmelCase    :					str							=   module_cls(_lowerCamelCase						)
  return model


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Optional[Any]						)      ->  str:
  Path(args.output_dir						).mkdir(exist_ok=_lowerCamelCase						)
  check_output_dir(_lowerCamelCase					, expected_items=3						)

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


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

     distill_main(args)




'''simple docstring'''

from __future__ import annotations


class 					a_					:




       def __init__(       self				,				snake_case_ = 0					):
         _lowerCAmelCase    :					Optional[Any]							=   value
         _lowerCAmelCase    :					Node | None							=   None
         _lowerCAmelCase    :					Node | None							=   None



def 		_UpperCAmelCase			(					_lowerCamelCase  :    Node | None					, _lowerCamelCase  :    Node | None						)      ->  Node | None:
  if treea is None:
    return treea
  if treea is None:
    return treea

  _lowerCAmelCase    :					Any							=   treea.value + treea.value
  _lowerCAmelCase    :					Tuple							=   merge_two_binary_trees(treea.left					, treea.left						)
  _lowerCAmelCase    :					List[str]							=   merge_two_binary_trees(treea.right					, treea.right						)
  return treea


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Node | None						)      ->  None:
  if root:
    print(root.value						)
    print_preorder(root.left						)
    print_preorder(root.right						)


if __name__ == "__main__":
     UpperCamelCase_            =    Node(1)
     UpperCamelCase_            =    Node(2)
     UpperCamelCase_            =    Node(3)
     UpperCamelCase_            =    Node(4)

     UpperCamelCase_            =    Node(2)
     UpperCamelCase_            =    Node(4)
     UpperCamelCase_            =    Node(6)
     UpperCamelCase_            =    Node(9)
     UpperCamelCase_            =    Node(5)

     print("""Tree1 is: """)
     print_preorder(treea)
     print("""Tree2 is: """)
     print_preorder(treea)
     UpperCamelCase_            =    merge_two_binary_trees(treea, treea)
     print("""Merged Tree is: """)
     print_preorder(merged_tree)


