






'''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    _snake_case   (				snake_case					):

					"""simple docstring"""





					_UpperCamelCase		 =      ["loss", "ce_loss", "mlm_loss", "hid_loss_enc", "hid_loss_dec"]




					def __init__(     self   , UpperCAmelCase__     )      ->	Union[str, Any]:
										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  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     )      ->	List[Any]:
										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  __SCREAMING_SNAKE_CASE (     UpperCAmelCase__   , UpperCAmelCase__     )      ->	List[str]:
										SummarizationModule.add_model_specific_args(UpperCAmelCase__   , UpperCAmelCase__     )
										add_distill_args(UpperCAmelCase__     )
										return parser




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	tuple:
										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  __SCREAMING_SNAKE_CASE (     UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     )      ->	Dict:
										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 							a	(   _UpperCAmelCase )				->				Union[str, Any]:






					"""simple docstring"""







					parser.add_argument('--teacher'					,  type=_UpperCAmelCase )
					parser.add_argument('--alpha_ce'					,  default=0.8					,  type=_UpperCAmelCase )
					parser.add_argument('--alpha_mlm'					,  default=0.2					,  type=_UpperCAmelCase )
					parser.add_argument('--alpha_hid'					,  default=0.0					,  type=_UpperCAmelCase					,  required=_UpperCAmelCase )
					parser.add_argument('--student'					,  type=_UpperCAmelCase					,  required=_UpperCAmelCase )
					parser.add_argument('--student_decoder_layers'					,  default=1_2					,  type=_UpperCAmelCase					,  required=_UpperCAmelCase )
					parser.add_argument('--student_encoder_layers'					,  default=1_2					,  type=_UpperCAmelCase					,  required=_UpperCAmelCase )
					parser.add_argument('--no_teacher'					,  action='store_true'					,  default=_UpperCAmelCase )
					parser.add_argument('--length_penalty'					,  type=_UpperCAmelCase					,  default=-1 )
					parser.add_argument('--supervise_forward'					,  action='store_true'					,  default=_UpperCAmelCase )
					parser.add_argument('--normalize_hidden'					,  action='store_true'					,  default=_UpperCAmelCase )


class    _snake_case   (				snake_case					):

					"""simple docstring"""





					_UpperCamelCase		 =      "translation"
					_UpperCamelCase		 =      ["bleu"]
					_UpperCamelCase		 =      "bleu"




					def __init__(     self   , UpperCAmelCase__   , **UpperCAmelCase__     )      ->	Dict:
										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  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__     )      ->	dict:
										return calculate_bleu(UpperCAmelCase__   , UpperCAmelCase__     )




					@staticmethod
					def  __SCREAMING_SNAKE_CASE (     UpperCAmelCase__   , UpperCAmelCase__     )      ->	List[Any]:
										TranslationModule.add_model_specific_args(UpperCAmelCase__   , UpperCAmelCase__     )
										add_distill_args(UpperCAmelCase__     )
										return parser



def 							a	(   _UpperCAmelCase )				->				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(_UpperCAmelCase )
					return model

def 							a	(   _UpperCAmelCase )				->				List[Any]:






					"""simple docstring"""







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

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


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'''
class    _snake_case   :

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__     )      ->	None:
										a_									=   data
										a_									=   42
										a_									=   42



def 							a	(   _UpperCAmelCase )				->				None:






					"""simple docstring"""







					a_									=   0
					a_									=   x

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase )				->				None:






					"""simple docstring"""







					a_							,					a_									=   find_set(_UpperCAmelCase ), find_set(_UpperCAmelCase )
					if x == y:
										return

					elif x.rank > y.rank:
										a_									=   x
					else:
										a_									=   y
										if x.rank == y.rank:
															y.rank += 1

def 							a	(   _UpperCAmelCase )				->				Node:






					"""simple docstring"""







					if x != x.parent:
										a_									=   find_set(x.parent )
					return x.parent

def 							a	(   _UpperCAmelCase )				->				set:






					"""simple docstring"""







					a_									=   ({0, 1, 2}, {3, 4, 5})
					for s in sets:
										if node.data in s:
															return s
					a_									=   F'''{node.data} is not in {sets}'''
					raise ValueError(_UpperCAmelCase )

def 							a	(   )				->				None:






					"""simple docstring"""







					a_									=   [Node(_UpperCAmelCase ) for i in range(6 )]
					for v in vertex:
										make_set(_UpperCAmelCase )

					union_set(vertex[0]					,  vertex[1] )
					union_set(vertex[1]					,  vertex[2] )
					union_set(vertex[3]					,  vertex[4] )
					union_set(vertex[3]					,  vertex[5] )

					for nodea in vertex:
										for nodea in vertex:
															if find_python_set(_UpperCAmelCase ).isdisjoint(find_python_set(_UpperCAmelCase ) ):
																				assert find_set(_UpperCAmelCase ) != find_set(_UpperCAmelCase )
															else:
																				assert find_set(_UpperCAmelCase ) == find_set(_UpperCAmelCase )


if __name__ == "__main__":
						test_disjoint_set()

