



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 					lowercase						(	__snake_case ):




       '''simple docstring'''







       __SCREAMING_SNAKE_CASE										=					['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']






       def __init__(						self		,       _snake_case   )					->		Tuple:

           """simple docstring"""
           assert Path(hparams.data_dir   ).exists()
           UpperCAmelCase     =    Path(hparams.output_dir   )
           self.output_dir.mkdir(exist_ok=_snake_case   )

           UpperCAmelCase     =    self.output_dir.joinpath('''student'''   )

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

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

           UpperCAmelCase   ,			UpperCAmelCase     =    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:
                   UpperCAmelCase     =    get_layers_to_supervise(
                       n_student=len(self.e_layer_ids   )		,       n_teacher=_snake_case   )
                   UpperCAmelCase     =    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
                   UpperCAmelCase     =    self.e_layer_ids
                   UpperCAmelCase     =    self.d_layer_ids
           else:
               UpperCAmelCase     =    None
               UpperCAmelCase     =    None

           UpperCAmelCase     =    nn.KLDivLoss(reduction='''batchmean'''   )
           UpperCAmelCase     =    2.0
           UpperCAmelCase     =    hparams.alpha_mlm
           UpperCAmelCase     =    hparams.alpha_ce
           UpperCAmelCase     =    hparams.alpha_hid
           gc.collect()
           torch.cuda.empty_cache()






       def 				snake_case_ (						self		,       _snake_case		,       _snake_case		,       _snake_case   )					->		Optional[Any]:

           """simple docstring"""
           UpperCAmelCase     =    mask[:, :, None].expand_as(_snake_case   )
           UpperCAmelCase     =    s_logits.size(-1   )
           UpperCAmelCase     =    torch.masked_select(_snake_case		,       _snake_case   )  # (bs * seq_length * voc_size) modulo the 1s in mask
           UpperCAmelCase     =    torch.masked_select(_snake_case		,       _snake_case   )  # (bs * seq_length * voc_size) modulo the 1s in mask
           UpperCAmelCase     =    s_logits_slct.view(-1		,       _snake_case   )  # (bs * seq_length, voc_size) modulo the 1s in mask
           UpperCAmelCase     =    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()
           UpperCAmelCase     =    (
               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 				snake_case_ (						_snake_case		,       _snake_case   )					->		Optional[Any]:

           """simple docstring"""
           SummarizationModule.add_model_specific_args(_snake_case		,       _snake_case   )
           add_distill_args(_snake_case   )
           return parser






       def 				snake_case_ (						self		,       _snake_case   )					->		tuple:

           """simple docstring"""
           UpperCAmelCase     =    self.tokenizer.pad_token_id
           UpperCAmelCase   ,			UpperCAmelCase   ,			UpperCAmelCase     =    batch['''input_ids'''], batch['''attention_mask'''], batch['''labels''']
           if isinstance(self.model		,       _snake_case   ):
               UpperCAmelCase     =    self.model._shift_right(_snake_case   )
           else:
               UpperCAmelCase     =    shift_tokens_right(_snake_case		,       _snake_case   )

           # noinspection PyCallingNonCallable
           UpperCAmelCase     =    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		,       )
           UpperCAmelCase     =    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
               UpperCAmelCase     =    nn.CrossEntropyLoss(ignore_index=_snake_case   )
               UpperCAmelCase     =    loss_fct(lm_logits.view(-1		,       lm_logits.shape[-1]   )		,       labels.view(-1   )   )
           else:
               UpperCAmelCase     =    nn.functional.log_softmax(_snake_case		,       dim=-1   )
               UpperCAmelCase   ,			UpperCAmelCase     =    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   )

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

           UpperCAmelCase     =    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		,       )
           UpperCAmelCase     =    decoder_input_ids.ne(_snake_case   )
           UpperCAmelCase     =    self.calc_ce_loss(_snake_case		,       _snake_case		,       teacher_outputs['''logits''']   )
           if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
               UpperCAmelCase     =    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		,       )

           UpperCAmelCase     =    (
               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 				snake_case_ (						_snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case   )					->		int:

           """simple docstring"""
           UpperCAmelCase     =    '''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}"""
           UpperCAmelCase     =    attention_mask.to(hidden_states[0]   )
           UpperCAmelCase     =    mask.sum() * hidden_states[0].size(-1   )
           UpperCAmelCase     =    torch.stack([hidden_states[i] for i in range(len(_snake_case   )   )]   )
           UpperCAmelCase     =    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:
               UpperCAmelCase     =    nn.functional.layer_norm(_snake_case		,       student_states.shape[1:]   )
               UpperCAmelCase     =    nn.functional.layer_norm(_snake_case		,       teacher_states.shape[1:]   )
           UpperCAmelCase     =    nn.functional.mse_loss(_snake_case		,       _snake_case		,       reduction='''none'''   )
           UpperCAmelCase     =    (mse * mask.unsqueeze(0   ).unsqueeze(-1   )).sum() / valid_count
           return masked_mse




def 					_lowerCAmelCase   (     A__:	List[str]		):


    '''simple docstring'''


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

class 					lowercase						(	__snake_case ):




       '''simple docstring'''







       __SCREAMING_SNAKE_CASE										=					'translation'
       __SCREAMING_SNAKE_CASE										=					['bleu']
       __SCREAMING_SNAKE_CASE										=					'bleu'






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

           """simple docstring"""
           super().__init__(_snake_case		,       **_snake_case   )
           assert hparams.src_lang is not None
           assert hparams.tgt_lang is not None
           UpperCAmelCase     =    hparams.src_lang
           UpperCAmelCase     =    hparams.tgt_lang
           if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer		,       _snake_case   ):
               UpperCAmelCase     =    self.tokenizer.lang_code_to_id[hparams.tgt_lang]






       def 				snake_case_ (						self		,       _snake_case		,       _snake_case   )					->		dict:

           """simple docstring"""
           return calculate_bleu(_snake_case		,       _snake_case   )






       @staticmethod
       def 				snake_case_ (						_snake_case		,       _snake_case   )					->		Tuple:

           """simple docstring"""
           TranslationModule.add_model_specific_args(_snake_case		,       _snake_case   )
           add_distill_args(_snake_case   )
           return parser




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


    '''simple docstring'''


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




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


    '''simple docstring'''


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

    UpperCAmelCase     =    create_module(lowercase_		)
    return ft_main(lowercase_		,						model=lowercase_		)


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

    distill_main(args)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_    =					{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					['''MPNetTokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					[
        '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MPNetForMaskedLM''',
        '''MPNetForMultipleChoice''',
        '''MPNetForQuestionAnswering''',
        '''MPNetForSequenceClassification''',
        '''MPNetForTokenClassification''',
        '''MPNetLayer''',
        '''MPNetModel''',
        '''MPNetPreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					[
        '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFMPNetEmbeddings''',
        '''TFMPNetForMaskedLM''',
        '''TFMPNetForMultipleChoice''',
        '''TFMPNetForQuestionAnswering''',
        '''TFMPNetForSequenceClassification''',
        '''TFMPNetForTokenClassification''',
        '''TFMPNetMainLayer''',
        '''TFMPNetModel''',
        '''TFMPNetPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
    from .tokenization_mpnet import MPNetTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mpnet_fast import MPNetTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mpnet import (
            MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            MPNetForMaskedLM,
            MPNetForMultipleChoice,
            MPNetForQuestionAnswering,
            MPNetForSequenceClassification,
            MPNetForTokenClassification,
            MPNetLayer,
            MPNetModel,
            MPNetPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mpnet import (
            TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFMPNetEmbeddings,
            TFMPNetForMaskedLM,
            TFMPNetForMultipleChoice,
            TFMPNetForQuestionAnswering,
            TFMPNetForSequenceClassification,
            TFMPNetForTokenClassification,
            TFMPNetMainLayer,
            TFMPNetModel,
            TFMPNetPreTrainedModel,
        )

else:
    import sys

    lowerCamelCase_    =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)