


'''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 					lowerCAmelCase_     (    UpperCAmelCase  ):
       __UpperCAmelCase				=['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']






       def __init__(   self ,    _UpperCamelCase      )->			str:
            assert Path(hparams.data_dir      ).exists()
            _A				=       Path(hparams.output_dir      )
            self.output_dir.mkdir(exist_ok=_UpperCamelCase      )

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

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

            if hparams.length_penalty != -1:
                 _A				=       hparams.length_penalty
            _A				=       hparams.teacher  # Use teacher's tokenizer
            super().__init__(_UpperCamelCase ,    model=_UpperCamelCase ,    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":
                 _A				=       len(student.get_encoder().block      )
                 _A				=       len(student.get_decoder().block      )
                 _A				=       len(teacher.get_encoder().block      )
                 _A				=       len(teacher.get_decoder().block      )
            else:
                 _A				=       student.config.encoder_layers
                 _A				=       student.config.decoder_layers
                 _A				=       teacher.config.encoder_layers
                 _A				=       teacher.config.decoder_layers

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

            _A		,							_A				=       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:
                      _A				=       get_layers_to_supervise(
                          n_student=len(self.e_layer_ids      ) ,    n_teacher=_UpperCamelCase      )
                      _A				=       get_layers_to_supervise(
                          n_student=len(self.d_layer_ids      ) ,    n_teacher=_UpperCamelCase      )
                 else:  # student layer should emulate hidden states of the teacher layer it was copied from
                      _A				=       self.e_layer_ids
                      _A				=       self.d_layer_ids
            else:
                 _A				=       None
                 _A				=       None

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






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )->			Any:
            _A				=       mask[:, :, None].expand_as(_UpperCamelCase      )
            _A				=       s_logits.size(-1      )
            _A				=       torch.masked_select(_UpperCamelCase ,    _UpperCamelCase      )  # (bs * seq_length * voc_size) modulo the 1s in mask
            _A				=       torch.masked_select(_UpperCamelCase ,    _UpperCamelCase      )  # (bs * seq_length * voc_size) modulo the 1s in mask
            _A				=       s_logits_slct.view(-1 ,    _UpperCamelCase      )  # (bs * seq_length, voc_size) modulo the 1s in mask
            _A				=       t_logits_slct.view(-1 ,    _UpperCamelCase      )  # (bs * seq_length, voc_size) modulo the 1s in mask
            assert t_logits_slct.size() == s_logits_slct.size()
            _A				=       (
                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	(   _UpperCamelCase ,    _UpperCamelCase      )->			List[Any]:
            SummarizationModule.add_model_specific_args(_UpperCamelCase ,    _UpperCamelCase      )
            add_distill_args(_UpperCamelCase      )
            return parser






       def      UpperCamelCase	(   self ,    _UpperCamelCase      )->			tuple:
            _A				=       self.tokenizer.pad_token_id
            _A		,							_A		,							_A				=       batch['input_ids'], batch['attention_mask'], batch['labels']
            if isinstance(self.model ,    _UpperCamelCase      ):
                 _A				=       self.model._shift_right(_UpperCamelCase      )
            else:
                 _A				=       shift_tokens_right(_UpperCamelCase ,    _UpperCamelCase      )

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

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

            _A				=       student_outputs[
                'encoder_last_hidden_state'
            ]  # use this unless self.different_base_models
            _A		,							_A				=       zero_tensor(), zero_tensor()
            if self.different_encoder:  # compute encoder hidden state loss
                 _A				=       self.teacher.get_encoder()(
                     _UpperCamelCase ,    attention_mask=_UpperCamelCase ,    output_hidden_states=self.do_calc_hidden_loss ,    )
                 if self.different_base_models:
                      _A				=       all_teacher_encoder_outputs['last_hidden_state']
                 elif self.do_calc_hidden_loss:
                      _A				=       self.calc_hidden_loss(
                          _UpperCamelCase ,    student_outputs['encoder_hidden_states'] ,    all_teacher_encoder_outputs['hidden_states'] ,    self.e_matches ,    normalize_hidden=self.hparams.normalize_hidden ,    )

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

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






def 				lowerCamelCase_   (   __UpperCamelCase				:					Dict		)				->  Any:







     """simple docstring"""
     # NOTE: if --student argument was specified and the teacher and student base models
     # are different, the models still have to have the same tokenizer, specified by
     # --tokenizer_name. So, for example, you can distill from t5_large to t5_small but not
     # from bart to t5. This s because if the tokenizers are different, the output space
     # for the two models is also different and their logits are not comparable.
     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=1_2       ,						type=__UpperCamelCase       ,						required=__UpperCamelCase		)
     parser.add_argument('--student_encoder_layers'       ,						default=1_2       ,						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_     (    UpperCAmelCase  ):
       __UpperCAmelCase				='translation'
       __UpperCAmelCase				=['bleu']
       __UpperCAmelCase				='bleu'






       def __init__(   self ,    _UpperCamelCase ,    **_UpperCamelCase      )->			str:
            super().__init__(_UpperCamelCase ,    **_UpperCamelCase      )
            assert hparams.src_lang is not None
            assert hparams.tgt_lang is not None
            _A				=       hparams.src_lang
            _A				=       hparams.tgt_lang
            if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer ,    _UpperCamelCase      ):
                 _A				=       self.tokenizer.lang_code_to_id[hparams.tgt_lang]






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase      )->			dict:
            return calculate_bleu(_UpperCamelCase ,    _UpperCamelCase      )






       @staticmethod
       def      UpperCamelCase	(   _UpperCamelCase ,    _UpperCamelCase      )->			Any:
            TranslationModule.add_model_specific_args(_UpperCamelCase ,    _UpperCamelCase      )
            add_distill_args(_UpperCamelCase      )
            return parser






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







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

def 				lowerCamelCase_   (   __UpperCamelCase				:					Optional[int]		)				->  str:







     """simple docstring"""
     Path(args.output_dir		).mkdir(exist_ok=__UpperCamelCase		)
     check_output_dir(__UpperCamelCase       ,						expected_items=3		)

     _A				=       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)









'''simple docstring'''
from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
    import torch

lowerCAmelCase  =				logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
    from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):

    def 				lowerCamelCase_   (   cls				:					Tuple		)				->  Dict:







         """simple docstring"""
         return cls

def 				lowerCamelCase_   (   __UpperCamelCase				:					Union[Tuple, List]       ,						__UpperCamelCase				:					Optional[Union[List["torch.Generator"], "torch.Generator"]] = None       ,						__UpperCamelCase				:					Optional["torch.device"] = None       ,						__UpperCamelCase				:					Optional["torch.dtype"] = None       ,						__UpperCamelCase				:					Optional["torch.layout"] = None       ,						)				->  Tuple:







     """simple docstring"""
     _A				=       device
     _A				=       shape[0]

     _A				=       layout or torch.strided
     _A				=       device or torch.device('cpu'		)

     if generator is not None:
          _A				=       generator.device.type if not isinstance(__UpperCamelCase       ,						__UpperCamelCase		) else generator[0].device.type
          if gen_device_type != device.type and gen_device_type == "cpu":
               _A				=       'cpu'
               if device != "mps":
                    logger.info(
                        F'The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'
                        F' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'
                        F' slighly speed up this function by passing a generator that was created on the {device} device.'		)
          elif gen_device_type != device.type and gen_device_type == "cuda":
               raise ValueError(F'Cannot generate a {device} tensor from a generator of type {gen_device_type}.'		)

     if isinstance(__UpperCamelCase       ,						__UpperCamelCase		):
          _A				=       (1,) + shape[1:]
          _A				=       [
              torch.randn(__UpperCamelCase       ,						generator=generator[i]       ,						device=__UpperCamelCase       ,						dtype=__UpperCamelCase       ,						layout=__UpperCamelCase		)
              for i in range(__UpperCamelCase		)
          ]
          _A				=       torch.cat(__UpperCamelCase       ,						dim=0		).to(__UpperCamelCase		)
     else:
          _A				=       torch.randn(__UpperCamelCase       ,						generator=__UpperCamelCase       ,						device=__UpperCamelCase       ,						dtype=__UpperCamelCase       ,						layout=__UpperCamelCase		).to(__UpperCamelCase		)

     return latents

def 				lowerCamelCase_   (   __UpperCamelCase				:					Any		)				->  Optional[int]:







     """simple docstring"""
     if is_torch_version('<'       ,						'2.0.0'		) or not hasattr(__UpperCamelCase       ,						'_dynamo'		):
          return False
     return isinstance(__UpperCamelCase       ,						torch._dynamo.eval_frame.OptimizedModule		)





