


"""simple docstring"""







import numpy as np






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__	,				UpperCAmelCase__		=    a.shape
						UpperCAmelCase__		=    min(_UpperCamelCase	,			_UpperCamelCase )
						UpperCAmelCase__		=    np.eye(_UpperCamelCase )
						UpperCAmelCase__		=    a.copy()

						for k in range(t - 1 ):
												# select a column of modified matrix A':
												UpperCAmelCase__		=    r[k:, [k]]
												# construct first basis vector
												UpperCAmelCase__		=    np.zeros_like(_UpperCamelCase )
												UpperCAmelCase__		=    1.0
												# determine scaling factor
												UpperCAmelCase__		=    np.linalg.norm(_UpperCamelCase )
												# construct vector v for Householder reflection
												UpperCAmelCase__		=    x + np.sign(x[0] ) * alpha * ea
												v /= np.linalg.norm(_UpperCamelCase )

												# construct the Householder matrix
												UpperCAmelCase__		=    np.eye(m - k ) - 2.0 * v @ v.T
												# pad with ones and zeros as necessary
												UpperCAmelCase__		=    np.block([[np.eye(_UpperCamelCase ), np.zeros((k, m - k) )], [np.zeros((m - k, k) ), q_k]] )

												UpperCAmelCase__		=    q @ q_k.T
												UpperCAmelCase__		=    q_k @ r

						return q, r


if __name__ == "__main__":
						import doctest

						doctest.testmod()



"""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 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      ["""loss""", """ce_loss""", """mlm_loss""", """hid_loss_enc""", """hid_loss_dec"""]






							def __init__(						self :     int		, lowerCamelCase_ :     Tuple				):
													assert Path(hparams.data_dir				).exists()
													UpperCAmelCase__		=    Path(hparams.output_dir				)
													self.output_dir.mkdir(exist_ok=lowerCamelCase_				)

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

													UpperCAmelCase__		=    str(lowerCamelCase_				)  # Tell lightning we are training the student
													UpperCAmelCase__		=    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:
																			UpperCAmelCase__		=    AutoModelForSeqaSeqLM.from_pretrained(hparams.student				)
																			use_task_specific_params(lowerCamelCase_		, hparams.task				)
																			UpperCAmelCase__	,				UpperCAmelCase__		=    None, None
													else:
																			UpperCAmelCase__	,				UpperCAmelCase__	,				UpperCAmelCase__		=    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:
																			UpperCAmelCase__		=    hparams.length_penalty
													UpperCAmelCase__		=    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":
																			UpperCAmelCase__		=    len(student.get_encoder().block				)
																			UpperCAmelCase__		=    len(student.get_decoder().block				)
																			UpperCAmelCase__		=    len(teacher.get_encoder().block				)
																			UpperCAmelCase__		=    len(teacher.get_decoder().block				)
													else:
																			UpperCAmelCase__		=    student.config.encoder_layers
																			UpperCAmelCase__		=    student.config.decoder_layers
																			UpperCAmelCase__		=    teacher.config.encoder_layers
																			UpperCAmelCase__		=    teacher.config.decoder_layers

													UpperCAmelCase__		=    not (hparams.student is None or hparams.teacher == hparams.student)
													UpperCAmelCase__		=    (not self.different_base_models) and hparams.alpha_hid > 0
													UpperCAmelCase__		=    self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
													# self.different_encoder determines whether we need to run the teacher encoder
													UpperCAmelCase__		=    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:
																			UpperCAmelCase__		=    list(range(lowerCamelCase_				)				)
													if d_layer_ids is None:
																			UpperCAmelCase__		=    list(range(lowerCamelCase_				)				)

													UpperCAmelCase__	,				UpperCAmelCase__		=    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:
																									UpperCAmelCase__		=    get_layers_to_supervise(
																									    n_student=len(self.e_layer_ids				)		, n_teacher=lowerCamelCase_				)
																									UpperCAmelCase__		=    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
																									UpperCAmelCase__		=    self.e_layer_ids
																									UpperCAmelCase__		=    self.d_layer_ids
													else:
																			UpperCAmelCase__		=    None
																			UpperCAmelCase__		=    None

													UpperCAmelCase__		=    nn.KLDivLoss(reduction='''batchmean'''				)
													UpperCAmelCase__		=    2.0
													UpperCAmelCase__		=    hparams.alpha_mlm
													UpperCAmelCase__		=    hparams.alpha_ce
													UpperCAmelCase__		=    hparams.alpha_hid
													gc.collect()
													torch.cuda.empty_cache()






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






							def 					lowerCAmelCase     (						self :     Dict		, lowerCamelCase_ :     dict				):
													UpperCAmelCase__		=    self.tokenizer.pad_token_id
													UpperCAmelCase__	,				UpperCAmelCase__	,				UpperCAmelCase__		=    batch['''input_ids'''], batch['''attention_mask'''], batch['''labels''']
													if isinstance(self.model		, lowerCamelCase_				):
																			UpperCAmelCase__		=    self.model._shift_right(lowerCamelCase_				)
													else:
																			UpperCAmelCase__		=    shift_tokens_right(lowerCamelCase_		, lowerCamelCase_				)

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

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

													UpperCAmelCase__		=    student_outputs[
													    '''encoder_last_hidden_state'''
													]  # use this unless self.different_base_models
													UpperCAmelCase__	,				UpperCAmelCase__		=    zero_tensor(), zero_tensor()
													if self.different_encoder:  # compute encoder hidden state loss
																			UpperCAmelCase__		=    self.teacher.get_encoder()(
																			    lowerCamelCase_		, attention_mask=lowerCamelCase_		, output_hidden_states=self.do_calc_hidden_loss		, )
																			if self.different_base_models:
																									UpperCAmelCase__		=    all_teacher_encoder_outputs['''last_hidden_state''']
																			elif self.do_calc_hidden_loss:
																									UpperCAmelCase__		=    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		, )

													UpperCAmelCase__		=    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_		, )
													UpperCAmelCase__		=    decoder_input_ids.ne(lowerCamelCase_				)
													UpperCAmelCase__		=    self.calc_ce_loss(lowerCamelCase_		, lowerCamelCase_		, teacher_outputs['''logits''']				)
													if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
																			UpperCAmelCase__		=    self.calc_hidden_loss(
																			    lowerCamelCase_		, student_outputs['''decoder_hidden_states''']		, teacher_outputs['''decoder_hidden_states''']		, self.d_matches		, normalize_hidden=self.hparams.normalize_hidden		, )

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






def 					a__ (       _UpperCamelCase ):





						"""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=12	,			type=_UpperCamelCase	,			required=_UpperCamelCase )
						parser.add_argument('''--student_encoder_layers'''	,			default=12	,			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 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      """translation"""
							SCREAMING_SNAKE_CASE										=      ["""bleu"""]
							SCREAMING_SNAKE_CASE										=      """bleu"""






							def __init__(						self :     Any		, lowerCamelCase_ :     Any		, **lowerCamelCase_ :     str				):
													super().__init__(lowerCamelCase_		, **lowerCamelCase_				)
													assert hparams.src_lang is not None
													assert hparams.tgt_lang is not None
													UpperCAmelCase__		=    hparams.src_lang
													UpperCAmelCase__		=    hparams.tgt_lang
													if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer		, lowerCamelCase_				):
																			UpperCAmelCase__		=    self.tokenizer.lang_code_to_id[hparams.tgt_lang]






							def 					lowerCAmelCase     (						self :     Any		, lowerCamelCase_ :     List[str]		, lowerCamelCase_ :     int				):
													return calculate_bleu(lowerCamelCase_		, lowerCamelCase_				)






							@staticmethod
							def 					lowerCAmelCase     (						lowerCamelCase_ :     List[Any]		, lowerCamelCase_ :     str				):
													TranslationModule.add_model_specific_args(lowerCamelCase_		, lowerCamelCase_				)
													add_distill_args(lowerCamelCase_				)
													return parser






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						if args.no_teacher:
												UpperCAmelCase__		=    TranslationModule if '''translation''' in args.task else SummarizationModule
						else:  # DISTILL WITH TEACHER
												UpperCAmelCase__		=    TranslationDistiller if '''translation''' in args.task else SummarizationDistiller
						UpperCAmelCase__		=    module_cls.__name__
						print(F"""using module {args.setup_cls}""" )
						UpperCAmelCase__		=    module_cls(_UpperCamelCase )
						return model






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

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

						UpperCAmelCase__		=    create_module(_UpperCamelCase )
						return ft_main(_UpperCamelCase	,			model=_UpperCamelCase )


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)