
'''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):





							"""simple docstring"""




							SCREAMING_SNAKE_CASE =					['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']






							def __init__(							self    ,							A__ )      ->     Union[str, Any]:
										assert Path(hparams.data_dir ).exists()
										_SCREAMING_SNAKE_CASE       	=			Path(hparams.output_dir )
										self.output_dir.mkdir(exist_ok=A__ )

										_SCREAMING_SNAKE_CASE       	=			self.output_dir.joinpath("""student""" )

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

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

										_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			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:
																_SCREAMING_SNAKE_CASE       	=			get_layers_to_supervise(
																    n_student=len(self.e_layer_ids )    ,							n_teacher=A__ )
																_SCREAMING_SNAKE_CASE       	=			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
																_SCREAMING_SNAKE_CASE       	=			self.e_layer_ids
																_SCREAMING_SNAKE_CASE       	=			self.d_layer_ids
										else:
													_SCREAMING_SNAKE_CASE       	=			None
													_SCREAMING_SNAKE_CASE       	=			None

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






							def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__ )      ->     Optional[Any]:
										_SCREAMING_SNAKE_CASE       	=			mask[:, :, None].expand_as(A__ )
										_SCREAMING_SNAKE_CASE       	=			s_logits.size(-1 )
										_SCREAMING_SNAKE_CASE       	=			torch.masked_select(A__    ,							A__ )  # (bs * seq_length * voc_size) modulo the 1s in mask
										_SCREAMING_SNAKE_CASE       	=			torch.masked_select(A__    ,							A__ )  # (bs * seq_length * voc_size) modulo the 1s in mask
										_SCREAMING_SNAKE_CASE       	=			s_logits_slct.view(-1    ,							A__ )  # (bs * seq_length, voc_size) modulo the 1s in mask
										_SCREAMING_SNAKE_CASE       	=			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()
										_SCREAMING_SNAKE_CASE       	=			(
										    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			(							A__    ,							A__ )      ->     Tuple:
										SummarizationModule.add_model_specific_args(A__    ,							A__ )
										add_distill_args(A__ )
										return parser






							def     UpperCamelCase			(							self    ,							A__ )      ->     Any:
										_SCREAMING_SNAKE_CASE       	=			self.tokenizer.pad_token_id
										_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			batch["""input_ids"""], batch["""attention_mask"""], batch["""labels"""]
										if isinstance(self.model    ,							A__ ):
													_SCREAMING_SNAKE_CASE       	=			self.model._shift_right(A__ )
										else:
													_SCREAMING_SNAKE_CASE       	=			shift_tokens_right(A__    ,							A__ )

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

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

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

										_SCREAMING_SNAKE_CASE       	=			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__    ,							)
										_SCREAMING_SNAKE_CASE       	=			decoder_input_ids.ne(A__ )
										_SCREAMING_SNAKE_CASE       	=			self.calc_ce_loss(A__    ,							A__    ,							teacher_outputs["""logits"""] )
										if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
													_SCREAMING_SNAKE_CASE       	=			self.calc_hidden_loss(
													    A__    ,							student_outputs["""decoder_hidden_states"""]    ,							teacher_outputs["""decoder_hidden_states"""]    ,							self.d_matches    ,							normalize_hidden=self.hparams.normalize_hidden    ,							)

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






def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_  )       ->       str:




			"""simple docstring"""
			parser.add_argument("""--teacher"""      , type=snake_case__  )
			parser.add_argument("""--alpha_ce"""      , default=0.8      , type=snake_case__  )
			parser.add_argument("""--alpha_mlm"""      , default=0.2      , type=snake_case__  )
			parser.add_argument("""--alpha_hid"""      , default=0.0      , type=snake_case__      , required=snake_case__  )
			parser.add_argument("""--student"""      , type=snake_case__      , required=snake_case__  )
			parser.add_argument("""--student_decoder_layers"""      , default=12      , type=snake_case__      , required=snake_case__  )
			parser.add_argument("""--student_encoder_layers"""      , default=12      , type=snake_case__      , required=snake_case__  )
			parser.add_argument("""--no_teacher"""      , action="""store_true"""      , default=snake_case__  )
			parser.add_argument("""--length_penalty"""      , type=snake_case__      , default=-1  )
			parser.add_argument("""--supervise_forward"""      , action="""store_true"""      , default=snake_case__  )
			parser.add_argument("""--normalize_hidden"""      , action="""store_true"""      , default=snake_case__  )

class 				_a			(_a):





							"""simple docstring"""




							SCREAMING_SNAKE_CASE =					'translation'
							SCREAMING_SNAKE_CASE =					['bleu']
							SCREAMING_SNAKE_CASE =					'bleu'






							def __init__(							self    ,							A__    ,							**A__ )      ->     Dict:
										super().__init__(A__    ,							**A__ )
										assert hparams.src_lang is not None
										assert hparams.tgt_lang is not None
										_SCREAMING_SNAKE_CASE       	=			hparams.src_lang
										_SCREAMING_SNAKE_CASE       	=			hparams.tgt_lang
										if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer    ,							A__ ):
													_SCREAMING_SNAKE_CASE       	=			self.tokenizer.lang_code_to_id[hparams.tgt_lang]






							def     UpperCamelCase			(							self    ,							A__    ,							A__ )      ->     Optional[int]:
										return calculate_bleu(A__    ,							A__ )






							@staticmethod
							def     UpperCamelCase			(							A__    ,							A__ )      ->     List[str]:
										TranslationModule.add_model_specific_args(A__    ,							A__ )
										add_distill_args(A__ )
										return parser






def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_  )       ->       Dict:




			"""simple docstring"""






			if args.no_teacher:
						_SCREAMING_SNAKE_CASE       	=			TranslationModule if """translation""" in args.task else SummarizationModule
			else:  # DISTILL WITH TEACHER
						_SCREAMING_SNAKE_CASE       	=			TranslationDistiller if """translation""" in args.task else SummarizationDistiller
			_SCREAMING_SNAKE_CASE       	=			module_cls.__name__
			print(F"using module {args.setup_cls}"  )
			_SCREAMING_SNAKE_CASE       	=			module_cls(snake_case__  )
			return model
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_  )       ->       List[str]:




			"""simple docstring"""
			Path(args.output_dir  ).mkdir(exist_ok=snake_case__  )
			check_output_dir(snake_case__      , expected_items=3  )

			_SCREAMING_SNAKE_CASE       	=			create_module(snake_case__  )
			return ft_main(snake_case__      , model=snake_case__  )


if __name__ == "__main__":
					UpperCamelCase__ :						List[str]    							=					argparse.ArgumentParser()
					UpperCamelCase__ :						Any    							=					pl.Trainer.add_argparse_args(parser)
					UpperCamelCase__ :						Tuple    							=					SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
					UpperCamelCase__ :						Dict    							=					parser.parse_args()

					distill_main(args)








'''simple docstring'''



def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_  )       ->       str:




   """simple docstring"""
   return " ".join(sorted(set(sentence.split()  )  )  )


if __name__ == "__main__":
     import doctest

     doctest.testmod()






