


"""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 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							__magic_name__					:     int				  =  ['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']




							def __init__(						self	,					lowerCamelCase__)   ->	Union[str, Any]:







								'''simple docstring'''




								assert Path(hparams.data_dir).exists()
								snake_case__  :						List[str]		=      Path(hparams.output_dir)
								self.output_dir.mkdir(exist_ok=lowerCamelCase__)

								snake_case__  :						Dict		=      self.output_dir.joinpath("student")

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

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

								snake_case__  :						List[str]		=      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__  :						Union[str, Any]		=      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__  :						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:
									snake_case__  :						List[str]		=      list(range(lowerCamelCase__))
								if d_layer_ids is None:
									snake_case__  :						Union[str, Any]		=      list(range(lowerCamelCase__))

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

								snake_case__  :						Tuple		=      nn.KLDivLoss(reduction="batchmean")
								snake_case__  :						int		=      2.0
								snake_case__  :						Any		=      hparams.alpha_mlm
								snake_case__  :						Tuple		=      hparams.alpha_ce
								snake_case__  :						int		=      hparams.alpha_hid
								gc.collect()
								torch.cuda.empty_cache()




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	List[Any]:







								'''simple docstring'''




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







								'''simple docstring'''




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




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	tuple:







								'''simple docstring'''




								snake_case__  :						str		=      self.tokenizer.pad_token_id
								snake_case__,					snake_case__,					snake_case__  :						Dict		=      batch["input_ids"], batch["attention_mask"], batch["labels"]
								if isinstance(self.model	,					lowerCamelCase__):
									snake_case__  :						Optional[Any]		=      self.model._shift_right(lowerCamelCase__)
								else:
									snake_case__  :						int		=      shift_tokens_right(lowerCamelCase__	,					lowerCamelCase__)

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

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

								snake_case__  :						Optional[int]		=      student_outputs[
								    "encoder_last_hidden_state"
								]  # use this unless self.different_base_models
								snake_case__,					snake_case__  :						Optional[int]		=      zero_tensor(), zero_tensor()
								if self.different_encoder:  # compute encoder hidden state loss
									snake_case__  :						str		=      self.teacher.get_encoder()(
									    lowerCamelCase__	,					attention_mask=lowerCamelCase__	,					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__  :						int		=      self.calc_hidden_loss(
										    lowerCamelCase__	,					student_outputs["encoder_hidden_states"]	,					all_teacher_encoder_outputs["hidden_states"]	,					self.e_matches	,					normalize_hidden=self.hparams.normalize_hidden	,					)

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

								snake_case__  :						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        UpperCAmelCase (						lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	List[Any]:







								'''simple docstring'''




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



def 			A__		(					_UpperCAmelCase    :      int	)							->	Union[str, Any]:

	'''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 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							__magic_name__					:     Optional[Any]				  =  '''translation'''
							__magic_name__					:     Optional[int]				  =  ['''bleu''']
							__magic_name__					:     Dict				  =  '''bleu'''




							def __init__(						self	,					lowerCamelCase__	,					**lowerCamelCase__)   ->	Optional[int]:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	dict:







								'''simple docstring'''




								return calculate_bleu(lowerCamelCase__	,					lowerCamelCase__)







							@staticmethod
							def        UpperCAmelCase (						lowerCamelCase__	,					lowerCamelCase__)   ->	Tuple:







								'''simple docstring'''




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



def 			A__		(					_UpperCAmelCase    :      int	)							->	Union[str, Any]:

	'''simple docstring'''


	if args.no_teacher:
		snake_case__  :						Optional[int]		=      TranslationModule if "translation" in args.task else SummarizationModule
	else:  # DISTILL WITH TEACHER
		snake_case__  :						int		=      TranslationDistiller if "translation" in args.task else SummarizationDistiller
	snake_case__  :						str		=      module_cls.__name__
	print(F"""using module {args.setup_cls}"""	)
	snake_case__  :						int		=      module_cls(_UpperCAmelCase	)
	return model



def 			A__		(					_UpperCAmelCase    :      Any	)							->	int:

	'''simple docstring'''


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

	snake_case__  :						Optional[int]		=      create_module(_UpperCAmelCase	)
	return ft_main(_UpperCAmelCase				,    model=_UpperCAmelCase	)


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

					distill_main(args)













'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__A				:				Optional[int]					=		logging.get_logger(__name__)

__A				:				Tuple					=		{
    """facebook/convnext-tiny-224""": """https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json""",
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}
class 					lowercase							(				_lowerCamelCase			,							_lowerCamelCase   ):


    '''simple docstring'''

    lowerCAmelCase__   					=				"convnext"

    def __init__(       self  :  Any					,  __lowerCamelCase  :  Dict=3					,  __lowerCamelCase  :  int=4					,  __lowerCamelCase  :  Any=4					,  __lowerCamelCase  :  Dict=None					,  __lowerCamelCase  :  str=None					,  __lowerCamelCase  :  Optional[int]="gelu"					,  __lowerCamelCase  :  List[str]=0.0_2					,  __lowerCamelCase  :  int=1E-12					,  __lowerCamelCase  :  Optional[Any]=1E-6					,  __lowerCamelCase  :  Any=0.0					,  __lowerCamelCase  :  Union[str, Any]=224					,  __lowerCamelCase  :  Dict=None					,  __lowerCamelCase  :  List[str]=None					,  **__lowerCamelCase  :  Tuple					,  )				->				Dict:

     '''simple docstring'''




     super().__init__(**__lowerCamelCase  )

     lowerCamelCase__	=  num_channels
     lowerCamelCase__	=  patch_size
     lowerCamelCase__	=  num_stages
     lowerCamelCase__	=  [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
     lowerCamelCase__	=  [3, 3, 9, 3] if depths is None else depths
     lowerCamelCase__	=  hidden_act
     lowerCamelCase__	=  initializer_range
     lowerCamelCase__	=  layer_norm_eps
     lowerCamelCase__	=  layer_scale_init_value
     lowerCamelCase__	=  drop_path_rate
     lowerCamelCase__	=  image_size
     lowerCamelCase__	=  ["stem"] + [f'''stage{idx}''' for idx in range(1					,  len(self.depths  ) + 1  )]
     lowerCamelCase__	,						lowerCamelCase__	=  get_aligned_output_features_output_indices(
         out_features=__lowerCamelCase					,  out_indices=__lowerCamelCase					,  stage_names=self.stage_names  )
class 					lowercase							(				_lowerCamelCase   ):


    '''simple docstring'''

    lowerCAmelCase__   					=				version.parse("1.11"   )

    @property
    def  a__					(       self  :  List[Any]  )				->				Mapping[str, Mapping[int, str]]:

     '''simple docstring'''




     return OrderedDict(
         [
             ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
         ]  )




    @property
    def  a__					(       self  :  Tuple  )				->				float:

     '''simple docstring'''




     return 1E-5

