



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				:					int			,			a_				:					int					):
					'''simple docstring'''





					assert Path(hparams.data_dir					).exists()
					__UpperCAmelCase       :	List[Any]						=      Path(hparams.output_dir					)
					self.output_dir.mkdir(exist_ok=a_					)

					__UpperCAmelCase       :	Tuple						=      self.output_dir.joinpath('''student'''					)

					__UpperCAmelCase       :	List[Any]						=      str(a_					)  # Tell lightning we are training the student
					__UpperCAmelCase       :	int						=      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       :	List[str]						=      AutoModelForSeqaSeqLM.from_pretrained(hparams.student					)
							use_task_specific_params(a_			,			hparams.task					)
							__UpperCAmelCase				,   __UpperCAmelCase       :	Union[str, 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       :	Union[str, Any]						=      hparams.length_penalty
					__UpperCAmelCase       :	Tuple						=      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       :	Union[str, Any]						=      len(student.get_encoder().block					)
							__UpperCAmelCase       :	List[Any]						=      len(student.get_decoder().block					)
							__UpperCAmelCase       :	Optional[Any]						=      len(teacher.get_encoder().block					)
							__UpperCAmelCase       :	Optional[Any]						=      len(teacher.get_decoder().block					)
					else:
							__UpperCAmelCase       :	Union[str, Any]						=      student.config.encoder_layers
							__UpperCAmelCase       :	Union[str, Any]						=      student.config.decoder_layers
							__UpperCAmelCase       :	Dict						=      teacher.config.encoder_layers
							__UpperCAmelCase       :	Any						=      teacher.config.decoder_layers

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

					__UpperCAmelCase				,   __UpperCAmelCase       :	List[Any]						=      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       :	str						=      get_layers_to_supervise(
									    n_student=len(self.e_layer_ids					)			,			n_teacher=a_					)
									__UpperCAmelCase       :	List[str]						=      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       :	Optional[Any]						=      self.e_layer_ids
									__UpperCAmelCase       :	str						=      self.d_layer_ids
					else:
							__UpperCAmelCase       :	Any						=      None
							__UpperCAmelCase       :	Any						=      None

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




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





					__UpperCAmelCase       :	Optional[Any]						=      mask[:, :, None].expand_as(a_					)
					__UpperCAmelCase       :	Any						=      s_logits.size(-1					)
					__UpperCAmelCase       :	Optional[Any]						=      torch.masked_select(a_			,			a_					)  # (bs * seq_length * voc_size) modulo the 1s in mask
					__UpperCAmelCase       :	Union[str, Any]						=      torch.masked_select(a_			,			a_					)  # (bs * seq_length * voc_size) modulo the 1s in mask
					__UpperCAmelCase       :	Tuple						=      s_logits_slct.view(-1			,			a_					)  # (bs * seq_length, voc_size) modulo the 1s in mask
					__UpperCAmelCase       :	Union[str, Any]						=      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       :	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__	(  a_				:					Optional[int]			,			a_				:					Any					):
					'''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       :	List[str]						=      self.tokenizer.pad_token_id
					__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase       :	Dict						=      batch['''input_ids'''], batch['''attention_mask'''], batch['''labels''']
					if isinstance(self.model			,			a_					):
							__UpperCAmelCase       :	int						=      self.model._shift_right(a_					)
					else:
							__UpperCAmelCase       :	List[Any]						=      shift_tokens_right(a_			,			a_					)

					# noinspection PyCallingNonCallable
					__UpperCAmelCase       :	List[str]						=      self(
					    a_			,			attention_mask=a_			,			decoder_input_ids=a_			,			output_hidden_states=self.do_calc_hidden_loss			,			output_attentions=a_			,			use_cache=a_			,			)
					__UpperCAmelCase       :	List[str]						=      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       :	int						=      loss_fct(lm_logits.view(-1			,			lm_logits.shape[-1]					)			,			labels.view(-1					)					)
					else:
							__UpperCAmelCase       :	Any						=      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       :	Any						=      student_outputs[
					    '''encoder_last_hidden_state'''
					]  # use this unless self.different_base_models
					__UpperCAmelCase				,   __UpperCAmelCase       :	Optional[Any]						=      zero_tensor(), zero_tensor()
					if self.different_encoder:  # compute encoder hidden state loss
							__UpperCAmelCase       :	int						=      self.teacher.get_encoder()(
							    a_			,			attention_mask=a_			,			output_hidden_states=self.do_calc_hidden_loss			,			)
							if self.different_base_models:
									__UpperCAmelCase       :	List[Any]						=      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       :	Dict						=      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       :	Any						=      decoder_input_ids.ne(a_					)
					__UpperCAmelCase       :	Optional[int]						=      self.calc_ce_loss(a_			,			a_			,			teacher_outputs['''logits''']					)
					if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
							__UpperCAmelCase       :	Optional[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[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__	(  a_				:					str			,			a_				:					List[Any]			,			a_				:					List[Any]			,			a_				:					List[Any]			,			a_				:					Any					):
					'''simple docstring'''





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

def 			a		( _UpperCAmelCase				:   int		):







		'''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				:					Optional[Any]			,			a_				:					Optional[int]			,			**a_				:					Union[str, Any]					):
					'''simple docstring'''





					super().__init__(a_			,			**a_					)
					assert hparams.src_lang is not None
					assert hparams.tgt_lang is not None
					__UpperCAmelCase       :	Optional[int]						=      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				:					Tuple			,			a_				:					List[Any]			,			a_				:					List[Any]					):
					'''simple docstring'''





					return calculate_bleu(a_			,			a_					)




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





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

def 			a		( _UpperCAmelCase				:   Tuple		):







		'''simple docstring'''





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

def 			a		( _UpperCAmelCase				:   int		):







		'''simple docstring'''





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

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

		if num_nodes > 1:
				__UpperCAmelCase       :	Tuple						=      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       :	Dict						=      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()





