




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__(   __UpperCamelCase):
      UpperCAmelCase__ :      Tuple       						=  ["loss", "ce_loss", "mlm_loss", "hid_loss_enc", "hid_loss_dec"]
      def __init__(							self:					Optional[Any]       ,			UpperCamelCase_:					Any  ):
             assert Path(hparams.data_dir  ).exists()
             __lowerCamelCase  =		Path(hparams.output_dir  )
             self.output_dir.mkdir(exist_ok=_lowerCAmelCase  )

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

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

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

             __lowerCamelCase  =		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:
                           __lowerCamelCase  =		get_layers_to_supervise(
                               n_student=len(self.e_layer_ids  )       ,			n_teacher=_lowerCAmelCase  )
                           __lowerCamelCase  =		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
                           __lowerCamelCase  =		self.e_layer_ids
                           __lowerCamelCase  =		self.d_layer_ids
             else:
                    __lowerCamelCase  =		None
                    __lowerCamelCase  =		None

             __lowerCamelCase  =		nn.KLDivLoss(reduction="""batchmean"""  )
             __lowerCamelCase  =		2.0
             __lowerCamelCase  =		hparams.alpha_mlm
             __lowerCamelCase  =		hparams.alpha_ce
             __lowerCamelCase  =		hparams.alpha_hid
             gc.collect()
             torch.cuda.empty_cache()
      def      lowerCAmelCase__		(							self:					Optional[Any]       ,			UpperCamelCase_:					Union[str, Any]       ,			UpperCamelCase_:					List[str]       ,			UpperCamelCase_:					int  ):
             __lowerCamelCase  =		mask[:, :, None].expand_as(_lowerCAmelCase  )
             __lowerCamelCase  =		s_logits.size(-1  )
             __lowerCamelCase  =		torch.masked_select(_lowerCAmelCase       ,			_lowerCAmelCase  )  # (bs * seq_length * voc_size) modulo the 1s in mask
             __lowerCamelCase  =		torch.masked_select(_lowerCAmelCase       ,			_lowerCAmelCase  )  # (bs * seq_length * voc_size) modulo the 1s in mask
             __lowerCamelCase  =		s_logits_slct.view(-1       ,			_lowerCAmelCase  )  # (bs * seq_length, voc_size) modulo the 1s in mask
             __lowerCamelCase  =		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()
             __lowerCamelCase  =		(
                 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__		(							UpperCamelCase_:					Tuple       ,			UpperCamelCase_:					Union[str, Any]  ):
             SummarizationModule.add_model_specific_args(_lowerCAmelCase       ,			_lowerCAmelCase  )
             add_distill_args(_lowerCAmelCase  )
             return parser
      def      lowerCAmelCase__		(							self:					Tuple       ,			UpperCamelCase_:					dict  ):
             __lowerCamelCase  =		self.tokenizer.pad_token_id
             __lowerCamelCase  =		batch["""input_ids"""], batch["""attention_mask"""], batch["""labels"""]
             if isinstance(self.model       ,			_lowerCAmelCase  ):
                    __lowerCamelCase  =		self.model._shift_right(_lowerCAmelCase  )
             else:
                    __lowerCamelCase  =		shift_tokens_right(_lowerCAmelCase       ,			_lowerCAmelCase  )

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

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

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

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

             __lowerCamelCase  =		(
                 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__		(							UpperCamelCase_:					Tuple       ,			UpperCamelCase_:					Tuple       ,			UpperCamelCase_:					str       ,			UpperCamelCase_:					Optional[int]       ,			UpperCamelCase_:					Dict  ):
             __lowerCamelCase  =		"""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}'
             __lowerCamelCase  =		attention_mask.to(hidden_states[0]  )
             __lowerCamelCase  =		mask.sum() * hidden_states[0].size(-1  )
             __lowerCamelCase  =		torch.stack([hidden_states[i] for i in range(len(_lowerCAmelCase  )  )]  )
             __lowerCamelCase  =		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:
                    __lowerCamelCase  =		nn.functional.layer_norm(_lowerCAmelCase       ,			student_states.shape[1:]  )
                    __lowerCamelCase  =		nn.functional.layer_norm(_lowerCAmelCase       ,			teacher_states.shape[1:]  )
             __lowerCamelCase  =		nn.functional.mse_loss(_lowerCAmelCase       ,			_lowerCAmelCase       ,			reduction="""none"""  )
             __lowerCamelCase  =		(mse * mask.unsqueeze(0  ).unsqueeze(-1  )).sum() / valid_count
             return masked_mse




def    lowerCamelCase__			(		A__			:   Tuple      ):





       '''simple docstring'''



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





class        lowerCamelCase__(   __UpperCamelCase):
      UpperCAmelCase__ :      int       						=  "translation"
      UpperCAmelCase__ :      Tuple       						=  ["bleu"]
      UpperCAmelCase__ :      Dict       						=  "bleu"
      def __init__(							self:					Any       ,			UpperCamelCase_:					Union[str, Any]       ,			**UpperCamelCase_:					Optional[int]  ):
             super().__init__(_lowerCAmelCase       ,			**_lowerCAmelCase  )
             assert hparams.src_lang is not None
             assert hparams.tgt_lang is not None
             __lowerCamelCase  =		hparams.src_lang
             __lowerCamelCase  =		hparams.tgt_lang
             if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer       ,			_lowerCAmelCase  ):
                    __lowerCamelCase  =		self.tokenizer.lang_code_to_id[hparams.tgt_lang]
      def      lowerCAmelCase__		(							self:					Dict       ,			UpperCamelCase_:					Union[str, Any]       ,			UpperCamelCase_:					Optional[Any]  ):
             return calculate_bleu(_lowerCAmelCase       ,			_lowerCAmelCase  )




      @staticmethod
      def      lowerCAmelCase__		(							UpperCamelCase_:					Dict       ,			UpperCamelCase_:					Union[str, Any]  ):
             TranslationModule.add_model_specific_args(_lowerCAmelCase       ,			_lowerCAmelCase  )
             add_distill_args(_lowerCAmelCase  )
             return parser




def    lowerCamelCase__			(		A__			:   List[Any]      ):





       '''simple docstring'''



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




def    lowerCamelCase__			(		A__			:   Union[str, Any]      ):





       '''simple docstring'''



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

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


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

       distill_main(args)


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_  							=			logging.get_logger(__name__)

lowercase_  							=			{
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	List[Any]          =	MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
       A							:	int          =	"mobilebert"

       def __init__(							self					:     Any			,			_lowerCAmelCase					:     Union[str, Any]=3_05_22			,			_lowerCAmelCase					:     Tuple=5_12			,			_lowerCAmelCase					:     Dict=24			,			_lowerCAmelCase					:     int=4			,			_lowerCAmelCase					:     Optional[Any]=5_12			,			_lowerCAmelCase					:     Union[str, Any]="relu"			,			_lowerCAmelCase					:     List[str]=0.0			,			_lowerCAmelCase					:     str=0.1			,			_lowerCAmelCase					:     Tuple=5_12			,			_lowerCAmelCase					:     Optional[Any]=2			,			_lowerCAmelCase					:     Optional[int]=0.02			,			_lowerCAmelCase					:     List[str]=1e-12			,			_lowerCAmelCase					:     int=0			,			_lowerCAmelCase					:     Union[str, Any]=1_28			,			_lowerCAmelCase					:     Optional[Any]=True			,			_lowerCAmelCase					:     Dict=True			,			_lowerCAmelCase					:     Any=1_28			,			_lowerCAmelCase					:     Optional[int]=False			,			_lowerCAmelCase					:     str=True			,			_lowerCAmelCase					:     Tuple=4			,			_lowerCAmelCase					:     List[str]="no_norm"			,			_lowerCAmelCase					:     Union[str, Any]=True			,			_lowerCAmelCase					:     Optional[int]=None			,			**_lowerCAmelCase					:     int			,			):
           super().__init__(pad_token_id=_lowerCAmelCase			,			**_lowerCAmelCase			)

           __snake_case       :				Any 			=    vocab_size
           __snake_case       :				Tuple 			=    hidden_size
           __snake_case       :				int 			=    num_hidden_layers
           __snake_case       :				Dict 			=    num_attention_heads
           __snake_case       :				List[Any] 			=    hidden_act
           __snake_case       :				List[Any] 			=    intermediate_size
           __snake_case       :				Union[str, Any] 			=    hidden_dropout_prob
           __snake_case       :				Any 			=    attention_probs_dropout_prob
           __snake_case       :				Any 			=    max_position_embeddings
           __snake_case       :				List[Any] 			=    type_vocab_size
           __snake_case       :				List[Any] 			=    initializer_range
           __snake_case       :				Dict 			=    layer_norm_eps
           __snake_case       :				Any 			=    embedding_size
           __snake_case       :				List[str] 			=    trigram_input
           __snake_case       :				Union[str, Any] 			=    use_bottleneck
           __snake_case       :				List[Any] 			=    intra_bottleneck_size
           __snake_case       :				Tuple 			=    use_bottleneck_attention
           __snake_case       :				Optional[Any] 			=    key_query_shared_bottleneck
           __snake_case       :				Tuple 			=    num_feedforward_networks
           __snake_case       :				int 			=    normalization_type
           __snake_case       :				List[Any] 			=    classifier_activation

           if self.use_bottleneck:
               __snake_case       :				List[str] 			=    intra_bottleneck_size
           else:
               __snake_case       :				int 			=    hidden_size

           __snake_case       :				Optional[Any] 			=    classifier_dropout







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):

       @property
       def 			snake_case__    (							self					:     Optional[Any]			):
           if self.task == "multiple-choice":
               __snake_case       :				Any 			=    {0: """batch""", 1: """choice""", 2: """sequence"""}
           else:
               __snake_case       :				Optional[Any] 			=    {0: """batch""", 1: """sequence"""}
           return OrderedDict(
               [
                   ("""input_ids""", dynamic_axis),
                   ("""attention_mask""", dynamic_axis),
                   ("""token_type_ids""", dynamic_axis),
               ]			)



