






"""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(   SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




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




						def __init__(   self       ,  snake_case__			):
												'''simple docstring'''
												assert Path(hparams.data_dir			).exists()
												lowercase__				:      Union[str, Any]=       Path(hparams.output_dir			)
												self.output_dir.mkdir(exist_ok=snake_case__			)

												lowercase__				:      List[str]=       self.output_dir.joinpath("student"			)

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

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

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

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

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




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      Dict=       mask[:, :, None].expand_as(snake_case__			)
												lowercase__				:      Tuple=       s_logits.size(-1			)
												lowercase__				:      List[str]=       torch.masked_select(snake_case__       ,  snake_case__			)  # (bs * seq_length * voc_size) modulo the 1s in mask
												lowercase__				:      int=       torch.masked_select(snake_case__       ,  snake_case__			)  # (bs * seq_length * voc_size) modulo the 1s in mask
												lowercase__				:      Optional[int]=       s_logits_slct.view(-1       ,  snake_case__			)  # (bs * seq_length, voc_size) modulo the 1s in mask
												lowercase__				:      Any=       t_logits_slct.view(-1       ,  snake_case__			)  # (bs * seq_length, voc_size) modulo the 1s in mask
												assert t_logits_slct.size() == s_logits_slct.size()
												lowercase__				:      Optional[int]=       (
												    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_       (   snake_case__       ,  snake_case__			):
												'''simple docstring'''
												SummarizationModule.add_model_specific_args(snake_case__       ,  snake_case__			)
												add_distill_args(snake_case__			)
												return parser




						def 		UpperCAmelCase_       (   self       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      Tuple=       self.tokenizer.pad_token_id
												lowercase__,	lowercase__,	lowercase__				:      Tuple=       batch["input_ids"], batch["attention_mask"], batch["labels"]
												if isinstance(self.model       ,  snake_case__			):
																		lowercase__				:      Optional[Any]=       self.model._shift_right(snake_case__			)
												else:
																		lowercase__				:      Optional[int]=       shift_tokens_right(snake_case__       ,  snake_case__			)

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

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

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

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

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





def 						lowercase__(A						)				->Any:




						"""simple docstring"""



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


class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						__lowerCamelCase				       =					"translation"
						__lowerCamelCase				       =					["bleu"]
						__lowerCamelCase				       =					"bleu"




						def __init__(   self       ,  snake_case__       ,  **snake_case__			):
												'''simple docstring'''
												super().__init__(snake_case__       ,  **snake_case__			)
												assert hparams.src_lang is not None
												assert hparams.tgt_lang is not None
												lowercase__				:      Any=       hparams.src_lang
												lowercase__				:      List[str]=       hparams.tgt_lang
												if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer       ,  snake_case__			):
																		lowercase__				:      int=       self.tokenizer.lang_code_to_id[hparams.tgt_lang]




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''
												return calculate_bleu(snake_case__       ,  snake_case__			)




						@staticmethod
						def 		UpperCAmelCase_       (   snake_case__       ,  snake_case__			):
												'''simple docstring'''
												TranslationModule.add_model_specific_args(snake_case__       ,  snake_case__			)
												add_distill_args(snake_case__			)
												return parser





def 						lowercase__(A						)				->Tuple:




						"""simple docstring"""



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





def 						lowercase__(A						)				->Dict:




						"""simple docstring"""



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

						lowercase__				:      Dict=       create_module(A						)
						return ft_main(A					,		model=A						)


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

			distill_main(args)









"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__A     :    int    =		{'configuration_bit': ['BIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BitConfig', 'BitOnnxConfig']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A     :    Optional[Any]    =		[
	    'BIT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'BitForImageClassification',
	    'BitModel',
	    'BitPreTrainedModel',
	    'BitBackbone',
	]


try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A     :    List[Any]    =		['BitImageProcessor']


if TYPE_CHECKING:
	from .configuration_bit import BIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BitConfig, BitOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_bit import (
		    BIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BitBackbone,
		    BitForImageClassification,
		    BitModel,
		    BitPreTrainedModel,
		)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_bit import BitImageProcessor

else:
	import sys

	__A     :    Union[str, Any]    =		_LazyModule(__name__, globals()['__file__'], _import_structure)





