
'''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 							UpperCAmelCase   (						snake_case_						):




	'''simple docstring'''





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






	def __init__( self      :				List[str]							,A      :				Tuple				):
					assert Path(hparams.data_dir				).exists()
					__A         =				Path(hparams.output_dir				)
					self.output_dir.mkdir(exist_ok=A				)

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

					__A         =				str(A				)  # Tell lightning we are training the student
					__A         =				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:
									__A         =				AutoModelForSeqaSeqLM.from_pretrained(hparams.student				)
									use_task_specific_params(A							,hparams.task				)
									__A    ,							__A         =				None, None
					else:
									__A    ,							__A    ,							__A         =				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:
									__A         =				hparams.length_penalty
					__A         =				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":
									__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(A				)				)
					if d_layer_ids is None:
									__A         =				list(range(A				)				)

					__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=A				)
													__A         =				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
													__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        UpperCamelCase_						( self      :				Optional[Any]							,A      :				Any							,A      :				Optional[int]							,A      :				Union[str, Any]				):
					__A         =				mask[:, :, None].expand_as(A				)
					__A         =				s_logits.size(-1				)
					__A         =				torch.masked_select(A							,A				)  # (bs * seq_length * voc_size) modulo the 1s in mask
					__A         =				torch.masked_select(A							,A				)  # (bs * seq_length * voc_size) modulo the 1s in mask
					__A         =				s_logits_slct.view(-1							,A				)  # (bs * seq_length, voc_size) modulo the 1s in mask
					__A         =				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()
					__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        UpperCamelCase_						( A      :				Union[str, Any]							,A      :				List[str]				):
					SummarizationModule.add_model_specific_args(A							,A				)
					add_distill_args(A				)
					return parser






	def        UpperCamelCase_						( self      :				List[Any]							,A      :				Any				):
					__A         =				self.tokenizer.pad_token_id
					__A    ,							__A    ,							__A         =				batch["input_ids"], batch["attention_mask"], batch["labels"]
					if isinstance(self.model							,A				):
									__A         =				self.model._shift_right(A				)
					else:
									__A         =				shift_tokens_right(A							,A				)

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

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

					__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()(
									    A							,attention_mask=A							,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(
													    A							,student_outputs["encoder_hidden_states"]							,all_teacher_encoder_outputs["hidden_states"]							,self.e_matches							,normalize_hidden=self.hparams.normalize_hidden							,)

					__A         =				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							,)
					__A         =				decoder_input_ids.ne(A				)
					__A         =				self.calc_ce_loss(A							,A							,teacher_outputs["logits"]				)
					if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
									__A         =				self.calc_hidden_loss(
									    A							,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        UpperCamelCase_						( A      :				Dict							,A      :				Union[str, Any]							,A      :				Dict							,A      :				Any							,A      :				Tuple				):
					__A         =				"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}'''
					__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(A				)				)]				)
					__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(A							,student_states.shape[1:]				)
									__A         =				nn.functional.layer_norm(A							,teacher_states.shape[1:]				)
					__A         =				nn.functional.mse_loss(A							,A							,reduction="none"				)
					__A         =				(mse * mask.unsqueeze(0				).unsqueeze(-1				)).sum() / valid_count
					return masked_mse





def 		UpperCAmelCase    (			a_		)	->			str:







				"""simple docstring"""



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





class 							UpperCAmelCase   (						snake_case_						):




	'''simple docstring'''





	snake_case_      					=  "translation"
	snake_case_      					=  ["bleu"]
	snake_case_      					=  "bleu"






	def __init__( self      :				Optional[int]							,A      :				Tuple							,**A      :				Tuple				):
					super().__init__(A							,**A				)
					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							,A				):
									__A         =				self.tokenizer.lang_code_to_id[hparams.tgt_lang]






	def        UpperCamelCase_						( self      :				Tuple							,A      :				List[str]							,A      :				str				):
					return calculate_bleu(A							,A				)







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





def 		UpperCAmelCase    (			a_		)	->			Tuple:







				"""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(__lowerCamelCase		)
				return model


def 		UpperCAmelCase    (			a_		)	->			str:







				"""simple docstring"""



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

				__A         =				create_module(__lowerCamelCase		)
				return ft_main(__lowerCamelCase	,     model=__lowerCamelCase		)


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

					distill_main(args)


import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig





class 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE						):




	'''simple docstring'''





	snake_case_      					=  CustomConfig






	def __init__( self      :				Optional[int]							,A      :				List[str]				):
					super().__init__(A				)
					__A         =				torch.nn.Linear(config.hidden_size							,config.hidden_size				)






	def        UpperCamelCase_						( self      :				Union[str, Any]							,A      :				Optional[int]				):
					return self.linear(A				)







	def        UpperCamelCase_						( self      :				Optional[int]							,A      :				int				):
					pass





class 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE						):




	'''simple docstring'''





	snake_case_      					=  NoSuperInitConfig






	def __init__( self      :				Optional[int]							,A      :				Union[str, Any]				):
					super().__init__(A				)
					__A         =				torch.nn.Linear(config.attribute							,config.attribute				)






	def        UpperCamelCase_						( self      :				str							,A      :				Any				):
					return self.linear(A				)







	def        UpperCamelCase_						( self      :				Tuple							,A      :				int				):
					pass
