





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 (    _a  ):
			lowerCamelCase_  :  str     =   ['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']






			def __init__(self      ,							__magic_name__					)  ->     Any:


							'''simple docstring'''



							assert Path(hparams.data_dir					).exists()
							snake_case_		:					int			    =						Path(hparams.output_dir					)
							self.output_dir.mkdir(exist_ok=__magic_name__					)

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

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

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

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

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

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






			def     lowerCamelCase						(self      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__					)  ->     int:


							'''simple docstring'''



							snake_case_		:					List[Any]			    =						mask[:, :, None].expand_as(__magic_name__					)
							snake_case_		:					Optional[int]			    =						s_logits.size(-1					)
							snake_case_		:					Optional[int]			    =						torch.masked_select(__magic_name__      ,							__magic_name__					)  # (bs * seq_length * voc_size) modulo the 1s in mask
							snake_case_		:					Union[str, Any]			    =						torch.masked_select(__magic_name__      ,							__magic_name__					)  # (bs * seq_length * voc_size) modulo the 1s in mask
							snake_case_		:					List[Any]			    =						s_logits_slct.view(-1      ,							__magic_name__					)  # (bs * seq_length, voc_size) modulo the 1s in mask
							snake_case_		:					Tuple			    =						t_logits_slct.view(-1      ,							__magic_name__					)  # (bs * seq_length, voc_size) modulo the 1s in mask
							assert t_logits_slct.size() == s_logits_slct.size()
							snake_case_		:					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     lowerCamelCase						(__magic_name__      ,							__magic_name__					)  ->     str:


							'''simple docstring'''



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






			def     lowerCamelCase						(self      ,							__magic_name__					)  ->     tuple:


							'''simple docstring'''



							snake_case_		:					List[str]			    =						self.tokenizer.pad_token_id
							snake_case_    ,							snake_case_    ,							snake_case_		:					int			    =						batch['''input_ids'''], batch['''attention_mask'''], batch['''labels''']
							if isinstance(self.model      ,							__magic_name__					):
											snake_case_		:					Optional[Any]			    =						self.model._shift_right(__magic_name__					)
							else:
											snake_case_		:					Tuple			    =						shift_tokens_right(__magic_name__      ,							__magic_name__					)

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

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

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

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

							snake_case_		:					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     lowerCamelCase						(__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__					)  ->     Dict:


							'''simple docstring'''



							snake_case_		:					Union[str, Any]			    =						'''expected list or tuple for hidden_states, got tensor of shape: '''
							assert not isinstance(__magic_name__      ,							torch.Tensor					), F'''{msg}{hidden_states.shape}'''
							assert not isinstance(__magic_name__      ,							torch.Tensor					), F'''{msg}{hidden_states_T.shape}'''
							snake_case_		:					str			    =						attention_mask.to(hidden_states[0]					)
							snake_case_		:					List[str]			    =						mask.sum() * hidden_states[0].size(-1					)
							snake_case_		:					Union[str, Any]			    =						torch.stack([hidden_states[i] for i in range(len(__magic_name__					)					)]					)
							snake_case_		:					Union[str, 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:
											snake_case_		:					int			    =						nn.functional.layer_norm(__magic_name__      ,							student_states.shape[1:]					)
											snake_case_		:					Union[str, Any]			    =						nn.functional.layer_norm(__magic_name__      ,							teacher_states.shape[1:]					)
							snake_case_		:					str			    =						nn.functional.mse_loss(__magic_name__      ,							__magic_name__      ,							reduction='''none'''					)
							snake_case_		:					str			    =						(mse * mask.unsqueeze(0					).unsqueeze(-1					)).sum() / valid_count
							return masked_mse






def   lowerCamelCase_		(       _UpperCamelCase				)	->		Dict:






				"""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 (    _a  ):
			lowerCamelCase_  :  int     =   '''translation'''
			lowerCamelCase_  :  Union[str, Any]     =   ['''bleu''']
			lowerCamelCase_  :  Any     =   '''bleu'''






			def __init__(self      ,							__magic_name__      ,							**__magic_name__					)  ->     List[Any]:


							'''simple docstring'''



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






			def     lowerCamelCase						(self      ,							__magic_name__      ,							__magic_name__					)  ->     dict:


							'''simple docstring'''



							return calculate_bleu(__magic_name__      ,							__magic_name__					)






			@staticmethod
			def     lowerCamelCase						(__magic_name__      ,							__magic_name__					)  ->     Optional[Any]:


							'''simple docstring'''



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






def   lowerCamelCase_		(       _UpperCamelCase				)	->		str:






				"""simple docstring"""


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






def   lowerCamelCase_		(       _UpperCamelCase				)	->		Optional[Any]:






				"""simple docstring"""


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

				snake_case_		:					Any			    =						create_module(_UpperCamelCase				)
				return ft_main(_UpperCamelCase				,       model=_UpperCamelCase				)


if __name__ == "__main__":
	lowerCAmelCase_												=       argparse.ArgumentParser()
	lowerCAmelCase_												=       pl.Trainer.add_argparse_args(parser)
	lowerCAmelCase_												=       SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
	lowerCAmelCase_												=       parser.parse_args()

	distill_main(args)







import warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


lowerCAmelCase_												=       logging.get_logger(__name__)



class       __lowerCAmelCase (    _a  ):






			def __init__(self      ,							*__magic_name__      ,							**__magic_name__					)  ->     None:


							'''simple docstring'''



							warnings.warn(
							    '''The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
							    ''' use LevitImageProcessor instead.'''      ,							__magic_name__      ,							)
							super().__init__(*__magic_name__      ,							**__magic_name__					)
