



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__      (      __UpperCamelCase       ):




			'''simple docstring'''






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




			def __init__(       self							:			Union[str, Any]	,	a_							:			List[Any]			):
					'''simple docstring'''





					assert Path(hparams.data_dir			).exists()
					__UpperCAmelCase   :					str													=     Path(hparams.output_dir			)
					self.output_dir.mkdir(exist_ok=a_			)

					__UpperCAmelCase   :					List[Any]													=     self.output_dir.joinpath('''student'''			)

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

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

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

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

					__UpperCAmelCase   :					str													=     nn.KLDivLoss(reduction='''batchmean'''			)
					__UpperCAmelCase   :					Optional[Any]													=     2.0
					__UpperCAmelCase   :					List[str]													=     hparams.alpha_mlm
					__UpperCAmelCase   :					Optional[Any]													=     hparams.alpha_ce
					__UpperCAmelCase   :					Any													=     hparams.alpha_hid
					gc.collect()
					torch.cuda.empty_cache()




			def 			snake_case__					(       self							:			Union[str, Any]	,	a_							:			str	,	a_							:			List[str]	,	a_							:			Union[str, Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Union[str, Any]													=     mask[:, :, None].expand_as(a_			)
					__UpperCAmelCase   :					int													=     s_logits.size(-1			)
					__UpperCAmelCase   :					Optional[Any]													=     torch.masked_select(a_	,	a_			)  # (bs * seq_length * voc_size) modulo the 1s in mask
					__UpperCAmelCase   :					str													=     torch.masked_select(a_	,	a_			)  # (bs * seq_length * voc_size) modulo the 1s in mask
					__UpperCAmelCase   :					List[Any]													=     s_logits_slct.view(-1	,	a_			)  # (bs * seq_length, voc_size) modulo the 1s in mask
					__UpperCAmelCase   :					int													=     t_logits_slct.view(-1	,	a_			)  # (bs * seq_length, voc_size) modulo the 1s in mask
					assert t_logits_slct.size() == s_logits_slct.size()
					__UpperCAmelCase   :					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 			snake_case__					(       a_							:			List[Any]	,	a_							:			Tuple			):
					'''simple docstring'''





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




			def 			snake_case__					(       self							:			Any	,	a_							:			dict			):
					'''simple docstring'''





					__UpperCAmelCase   :					Any													=     self.tokenizer.pad_token_id
					__UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase   :					Union[str, Any]													=     batch['''input_ids'''], batch['''attention_mask'''], batch['''labels''']
					if isinstance(self.model	,	a_			):
							__UpperCAmelCase   :					int													=     self.model._shift_right(a_			)
					else:
							__UpperCAmelCase   :					str													=     shift_tokens_right(a_	,	a_			)

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

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

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

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

					__UpperCAmelCase   :					Optional[int]													=     (
					    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__					(       a_							:			Optional[int]	,	a_							:			List[str]	,	a_							:			List[str]	,	a_							:			List[str]	,	a_							:			Optional[int]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[Any]													=     '''expected list or tuple for hidden_states, got tensor of shape: '''
					assert not isinstance(a_	,	torch.Tensor			), F'{msg}{hidden_states.shape}'
					assert not isinstance(a_	,	torch.Tensor			), F'{msg}{hidden_states_T.shape}'
					__UpperCAmelCase   :					int													=     attention_mask.to(hidden_states[0]			)
					__UpperCAmelCase   :					Tuple													=     mask.sum() * hidden_states[0].size(-1			)
					__UpperCAmelCase   :					Any													=     torch.stack([hidden_states[i] for i in range(len(a_			)			)]			)
					__UpperCAmelCase   :					Dict													=     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   :					List[str]													=     nn.functional.layer_norm(a_	,	student_states.shape[1:]			)
							__UpperCAmelCase   :					Optional[int]													=     nn.functional.layer_norm(a_	,	teacher_states.shape[1:]			)
					__UpperCAmelCase   :					Union[str, Any]													=     nn.functional.mse_loss(a_	,	a_	,	reduction='''none'''			)
					__UpperCAmelCase   :					Union[str, Any]													=     (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=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 		UpperCAmelCase__      (      __UpperCamelCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	"""translation"""
			UpperCamelCase      						=	["""bleu"""]
			UpperCamelCase      						=	"""bleu"""




			def __init__(       self							:			Any	,	a_							:			List[str]	,	**a_							:			List[Any]			):
					'''simple docstring'''





					super().__init__(a_	,	**a_			)
					assert hparams.src_lang is not None
					assert hparams.tgt_lang is not None
					__UpperCAmelCase   :					List[str]													=     hparams.src_lang
					__UpperCAmelCase   :					Tuple													=     hparams.tgt_lang
					if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer	,	a_			):
							__UpperCAmelCase   :					Dict													=     self.tokenizer.lang_code_to_id[hparams.tgt_lang]




			def 			snake_case__					(       self							:			Any	,	a_							:			Any	,	a_							:			Optional[int]			):
					'''simple docstring'''





					return calculate_bleu(a_	,	a_			)




			@staticmethod
			def 			snake_case__					(       a_							:			List[str]	,	a_							:			Optional[int]			):
					'''simple docstring'''





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

def 	a						(				_UpperCAmelCase							: Optional[Any]						):







		'''simple docstring'''





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

def 	a						(				_UpperCAmelCase							: Optional[Any]						):







		'''simple docstring'''





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

		__UpperCAmelCase   :					Any													=     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)










import json
import logging
import os
import subprocess
from argparse import ArgumentParser


__A             =logging.getLogger(__name__)

def 	a						(				):







		'''simple docstring'''





		__UpperCAmelCase   :					Union[str, Any]													=     ArgumentParser()
		__UpperCAmelCase							, __UpperCAmelCase   :					Optional[int]													=     parser.parse_known_args()
		for arg in unknown:
				if arg.startswith(('''-''', '''--''')						):
						parser.add_argument(arg.split('''='''						)[0]						)

		return parser.parse_args()

def 	a						(				):







		'''simple docstring'''





		__UpperCAmelCase   :					int													=     parse_args()
		__UpperCAmelCase   :					int													=     88_88
		__UpperCAmelCase   :					Union[str, Any]													=     int(os.environ['''SM_NUM_GPUS''']						)
		__UpperCAmelCase   :					Any													=     json.loads(os.environ['''SM_HOSTS''']						)
		__UpperCAmelCase   :					int													=     len(_UpperCAmelCase						)
		__UpperCAmelCase   :					Tuple													=     os.environ['''SM_CURRENT_HOST''']
		__UpperCAmelCase   :					Union[str, Any]													=     hosts.index(_UpperCAmelCase						)
		__UpperCAmelCase   :					Tuple													=     '''INFO'''

		if num_nodes > 1:
				__UpperCAmelCase   :					int													=     f'python -m torch.distributed.launch \\n                --nnodes={num_nodes}  \\n                --node_rank={rank}  \\n                --nproc_per_node={num_gpus}  \\n                --master_addr={hosts[0]}  \\n                --master_port={port} \\n                ./run_glue.py \\n                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]						)}'
		else:
				__UpperCAmelCase   :					Optional[Any]													=     f'python -m torch.distributed.launch \\n            --nproc_per_node={num_gpus}  \\n            ./run_glue.py \\n            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]						)}'
		try:
				subprocess.run(_UpperCAmelCase			,       shell=_UpperCAmelCase						)
		except Exception as e:
				logger.info(_UpperCAmelCase						)


if __name__ == "__main__":
				main()





