

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				(	__snake_case	):
      lowerCamelCase_	:      Dict          =					['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']







      def __init__(     self							,  lowerCamelCase   )						->       Tuple:
        assert Path(hparams.data_dir   ).exists()
        snake_case_       						=      Path(hparams.output_dir   )
        self.output_dir.mkdir(exist_ok=lowerCamelCase   )

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

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

        snake_case_       						=      not (hparams.student is None or hparams.teacher == hparams.student)
        snake_case_       						=      (not self.different_base_models) and hparams.alpha_hid > 0
        snake_case_       						=      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_       						=      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(range(lowerCamelCase   )   )
        if d_layer_ids is None:
          snake_case_       						=      list(range(lowerCamelCase   )   )

        snake_case_						,				snake_case_       						=      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_       						=      get_layers_to_supervise(
                n_student=len(self.e_layer_ids   )							,  n_teacher=lowerCamelCase   )
            snake_case_       						=      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_       						=      self.e_layer_ids
            snake_case_       						=      self.d_layer_ids
        else:
          snake_case_       						=      None
          snake_case_       						=      None

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







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Optional[Any]:
        snake_case_       						=      mask[:, :, None].expand_as(lowerCamelCase   )
        snake_case_       						=      s_logits.size(-1   )
        snake_case_       						=      torch.masked_select(lowerCamelCase							,  lowerCamelCase   )  # (bs * seq_length * voc_size) modulo the 1s in mask
        snake_case_       						=      torch.masked_select(lowerCamelCase							,  lowerCamelCase   )  # (bs * seq_length * voc_size) modulo the 1s in mask
        snake_case_       						=      s_logits_slct.view(-1							,  lowerCamelCase   )  # (bs * seq_length, voc_size) modulo the 1s in mask
        snake_case_       						=      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_       						=      (
            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_    (     lowerCamelCase							,  lowerCamelCase   )						->       Optional[Any]:
        SummarizationModule.add_model_specific_args(lowerCamelCase							,  lowerCamelCase   )
        add_distill_args(lowerCamelCase   )
        return parser







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase   )						->       tuple:
        snake_case_       						=      self.tokenizer.pad_token_id
        snake_case_						,				snake_case_						,				snake_case_       						=      batch["""input_ids"""], batch["""attention_mask"""], batch["""labels"""]
        if isinstance(self.model							,  lowerCamelCase   ):
          snake_case_       						=      self.model._shift_right(lowerCamelCase   )
        else:
          snake_case_       						=      shift_tokens_right(lowerCamelCase							,  lowerCamelCase   )

        # noinspection PyCallingNonCallable
        snake_case_       						=      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_       						=      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_       						=      nn.CrossEntropyLoss(ignore_index=lowerCamelCase   )
          snake_case_       						=      loss_fct(lm_logits.view(-1							,  lm_logits.shape[-1]   )							,  labels.view(-1   )   )
        else:
          snake_case_       						=      nn.functional.log_softmax(lowerCamelCase							,  dim=-1   )
          snake_case_						,				snake_case_       						=      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_       						=      student_outputs[
            """encoder_last_hidden_state"""
        ]  # use this unless self.different_base_models
        snake_case_						,				snake_case_       						=      zero_tensor(), zero_tensor()
        if self.different_encoder:  # compute encoder hidden state loss
          snake_case_       						=      self.teacher.get_encoder()(
              lowerCamelCase							,  attention_mask=lowerCamelCase							,  output_hidden_states=self.do_calc_hidden_loss							,  )
          if self.different_base_models:
            snake_case_       						=      all_teacher_encoder_outputs["""last_hidden_state"""]
          elif self.do_calc_hidden_loss:
            snake_case_       						=      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_       						=      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_       						=      decoder_input_ids.ne(lowerCamelCase   )
        snake_case_       						=      self.calc_ce_loss(lowerCamelCase							,  lowerCamelCase							,  teacher_outputs["""logits"""]   )
        if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
          snake_case_       						=      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_       						=      (
            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_    (     lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       int:
        snake_case_       						=      """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_       						=      attention_mask.to(hidden_states[0]   )
        snake_case_       						=      mask.sum() * hidden_states[0].size(-1   )
        snake_case_       						=      torch.stack([hidden_states[i] for i in range(len(lowerCamelCase   )   )]   )
        snake_case_       						=      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_       						=      nn.functional.layer_norm(lowerCamelCase							,  student_states.shape[1:]   )
          snake_case_       						=      nn.functional.layer_norm(lowerCamelCase							,  teacher_states.shape[1:]   )
        snake_case_       						=      nn.functional.mse_loss(lowerCamelCase							,  lowerCamelCase							,  reduction="""none"""   )
        snake_case_       						=      (mse * mask.unsqueeze(0   ).unsqueeze(-1   )).sum() / valid_count
        return masked_mse



def    UpperCamelCase(					lowercase_							)	->				Tuple:





  '''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  __lowerCamelCase				(	__snake_case	):
      lowerCamelCase_	:      Optional[Any]          =					'translation'
      lowerCamelCase_	:      Any          =					['bleu']
      lowerCamelCase_	:      Any          =					'bleu'







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







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase   )						->       dict:
        return calculate_bleu(lowerCamelCase							,  lowerCamelCase   )







      @staticmethod
      def 	lowerCAmelCase_    (     lowerCamelCase							,  lowerCamelCase   )						->       Tuple:
        TranslationModule.add_model_specific_args(lowerCamelCase							,  lowerCamelCase   )
        add_distill_args(lowerCamelCase   )
        return parser



def    UpperCamelCase(					lowercase_							)	->				List[Any]:





  '''simple docstring'''





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



def    UpperCamelCase(					lowercase_							)	->				Union[str, Any]:





  '''simple docstring'''





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

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


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 Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_UpperCamelCase     			=  logging.get_logger(__name__)







class 					lowerCamelCase__					(       snake_case ):
    SCREAMING_SNAKE_CASE		=			['''pixel_values''']






    def __init__( self	,A = True	,A = None	,A = PILImageResampling.BILINEAR	,A = True	,A = 1 / 255	,A = True	,A = None	,A = None	,**A	,):
          super().__init__(**A					)
          UpperCAmelCase							=       size if size is not None else {"""height""": 224, """width""": 224}
          UpperCAmelCase							=       get_size_dict(A					)
          UpperCAmelCase							=       do_resize
          UpperCAmelCase							=       do_rescale
          UpperCAmelCase							=       do_normalize
          UpperCAmelCase							=       size
          UpperCAmelCase							=       resample
          UpperCAmelCase							=       rescale_factor
          UpperCAmelCase							=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCAmelCase							=       image_std if image_std is not None else IMAGENET_STANDARD_STD






    def 				_UpperCamelCase							( self	,A	,A	,A = PILImageResampling.BILINEAR	,A = None	,**A	,):
          UpperCAmelCase							=       get_size_dict(A					)
          if "height" not in size or "width" not in size:
                raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''					)
          return resize(
              A	,size=(size["""height"""], size["""width"""])	,resample=A	,data_format=A	,**A					)






    def 				_UpperCamelCase							( self	,A	,A	,A = None	,**A					):
          return rescale(A	,scale=A	,data_format=A	,**A					)






    def 				_UpperCamelCase							( self	,A	,A	,A	,A = None	,**A	,):
          return normalize(A	,mean=A	,std=A	,data_format=A	,**A					)






    def 				_UpperCamelCase							( self	,A	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = ChannelDimension.FIRST	,**A	,):
          UpperCAmelCase							=       do_resize if do_resize is not None else self.do_resize
          UpperCAmelCase							=       do_rescale if do_rescale is not None else self.do_rescale
          UpperCAmelCase							=       do_normalize if do_normalize is not None else self.do_normalize
          UpperCAmelCase							=       resample if resample is not None else self.resample
          UpperCAmelCase							=       rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCAmelCase							=       image_mean if image_mean is not None else self.image_mean
          UpperCAmelCase							=       image_std if image_std is not None else self.image_std

          UpperCAmelCase							=       size if size is not None else self.size
          UpperCAmelCase							=       get_size_dict(A					)

          UpperCAmelCase							=       make_list_of_images(A					)

          if not valid_images(A					):
                raise ValueError(
                    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                    """torch.Tensor, tf.Tensor or jax.ndarray."""					)

          if do_resize and size is None:
                raise ValueError("""Size must be specified if do_resize is True."""					)

          if do_rescale and rescale_factor is None:
                raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

          # All transformations expect numpy arrays.
          UpperCAmelCase							=       [to_numpy_array(A					) for image in images]

          if do_resize:
                UpperCAmelCase							=       [self.resize(image=A	,size=A	,resample=A					) for image in images]

          if do_rescale:
                UpperCAmelCase							=       [self.rescale(image=A	,scale=A					) for image in images]

          if do_normalize:
                UpperCAmelCase							=       [self.normalize(image=A	,mean=A	,std=A					) for image in images]

          UpperCAmelCase							=       [to_channel_dimension_format(A	,A					) for image in images]

          UpperCAmelCase							=       {"""pixel_values""": images}
          return BatchFeature(data=A	,tensor_type=A					)



