






import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case			:			Optional[Any]			   =   logging.get_logger(__name__)







def 		_UpperCamelCase      (			UpperCamelCase_				:    Union[str, Any]						)     ->      Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  ConvNextVaConfig()

						if "atto" in checkpoint_url:
												lowerCAmelCase__			  =  [2, 2, 6, 2]
												lowerCAmelCase__			  =  [40, 80, 160, 320]
						if "femto" in checkpoint_url:
												lowerCAmelCase__			  =  [2, 2, 6, 2]
												lowerCAmelCase__			  =  [48, 96, 192, 384]
						if "pico" in checkpoint_url:
												lowerCAmelCase__			  =  [2, 2, 6, 2]
												lowerCAmelCase__			  =  [64, 128, 256, 512]
						if "nano" in checkpoint_url:
												lowerCAmelCase__			  =  [2, 2, 8, 2]
												lowerCAmelCase__			  =  [80, 160, 320, 640]
						if "tiny" in checkpoint_url:
												lowerCAmelCase__			  =  [3, 3, 9, 3]
												lowerCAmelCase__			  =  [96, 192, 384, 768]
						if "base" in checkpoint_url:
												lowerCAmelCase__			  =  [3, 3, 27, 3]
												lowerCAmelCase__			  =  [128, 256, 512, 1024]
						if "large" in checkpoint_url:
												lowerCAmelCase__			  =  [3, 3, 27, 3]
												lowerCAmelCase__			  =  [192, 384, 768, 1536]
						if "huge" in checkpoint_url:
												lowerCAmelCase__			  =  [3, 3, 27, 3]
												lowerCAmelCase__			  =  [352, 704, 1408, 2816]

						lowerCAmelCase__			  =  1000
						lowerCAmelCase__			  =  'imagenet-1k-id2label.json'
						lowerCAmelCase__			  =  (1, 1000)

						lowerCAmelCase__			  =  'huggingface/label-files'
						lowerCAmelCase__			  =  num_labels
						lowerCAmelCase__			  =  json.load(open(hf_hub_download(UpperCamelCase_			,				UpperCamelCase_			,				repo_type='dataset'						)			,				'r'						)						)
						lowerCAmelCase__			  =  {int(UpperCamelCase_						): v for k, v in idalabel.items()}

						lowerCAmelCase__			  =  idalabel
						lowerCAmelCase__			  =  {v: k for k, v in idalabel.items()}
						lowerCAmelCase__			  =  hidden_sizes
						lowerCAmelCase__			  =  depths

						return config, expected_shape







def 		_UpperCamelCase      (			UpperCamelCase_				:    Optional[Any]						)     ->      Tuple:

						"""simple docstring"""



						if "downsample_layers.0.0" in name:
												lowerCAmelCase__			  =  name.replace('downsample_layers.0.0'			,				'embeddings.patch_embeddings'						)
						if "downsample_layers.0.1" in name:
												lowerCAmelCase__			  =  name.replace('downsample_layers.0.1'			,				'embeddings.norm'						)  # we rename to layernorm later on
						if "downsample_layers.1.0" in name:
												lowerCAmelCase__			  =  name.replace('downsample_layers.1.0'			,				'stages.1.downsampling_layer.0'						)
						if "downsample_layers.1.1" in name:
												lowerCAmelCase__			  =  name.replace('downsample_layers.1.1'			,				'stages.1.downsampling_layer.1'						)
						if "downsample_layers.2.0" in name:
												lowerCAmelCase__			  =  name.replace('downsample_layers.2.0'			,				'stages.2.downsampling_layer.0'						)
						if "downsample_layers.2.1" in name:
												lowerCAmelCase__			  =  name.replace('downsample_layers.2.1'			,				'stages.2.downsampling_layer.1'						)
						if "downsample_layers.3.0" in name:
												lowerCAmelCase__			  =  name.replace('downsample_layers.3.0'			,				'stages.3.downsampling_layer.0'						)
						if "downsample_layers.3.1" in name:
												lowerCAmelCase__			  =  name.replace('downsample_layers.3.1'			,				'stages.3.downsampling_layer.1'						)
						if "stages" in name and "downsampling_layer" not in name:
												# stages.0.0. for instance should be renamed to stages.0.layers.0.
												lowerCAmelCase__			  =  name[: len('stages.0'						)] + '.layers' + name[len('stages.0'						) :]
						if "gamma" in name:
												lowerCAmelCase__			  =  name.replace('gamma'			,				'weight'						)
						if "beta" in name:
												lowerCAmelCase__			  =  name.replace('beta'			,				'bias'						)
						if "stages" in name:
												lowerCAmelCase__			  =  name.replace('stages'			,				'encoder.stages'						)
						if "norm" in name:
												lowerCAmelCase__			  =  name.replace('norm'			,				'layernorm'						)
						if "head" in name:
												lowerCAmelCase__			  =  name.replace('head'			,				'classifier'						)

						return name







def 		_UpperCamelCase      (			)     ->      Any:

						"""simple docstring"""



						lowerCAmelCase__			  =  'http://images.cocodataset.org/val2017/000000039769.jpg'
						lowerCAmelCase__			  =  Image.open(requests.get(UpperCamelCase_			,				stream=UpperCamelCase_						).raw						)
						return im







def 		_UpperCamelCase      (			UpperCamelCase_				:    int						)     ->      Optional[Any]:

						"""simple docstring"""



						if "224" in checkpoint_url:
												lowerCAmelCase__			  =  224
												lowerCAmelCase__			  =  224 / 256
						elif "384" in checkpoint_url:
												lowerCAmelCase__			  =  384
												lowerCAmelCase__			  =  None
						else:
												lowerCAmelCase__			  =  512
												lowerCAmelCase__			  =  None

						return ConvNextImageProcessor(
						    size=UpperCamelCase_			,				crop_pct=UpperCamelCase_			,				image_mean=[0.485, 0.456, 0.406]			,				image_std=[0.229, 0.224, 0.225]			,				resample=PILImageResampling.BICUBIC			,				)







@torch.no_grad()
def 		_UpperCamelCase      (			UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    str			,				UpperCamelCase_				:    Optional[Any]			,				UpperCamelCase_				:    Any						)     ->      List[str]:

						"""simple docstring"""



						print('Downloading original model from checkpoint...'						)
						# define ConvNeXTV2 configuration based on URL
						lowerCAmelCase__  ,				lowerCAmelCase__			  =  get_convnextva_config(UpperCamelCase_						)
						# load original state_dict from URL
						lowerCAmelCase__			  =  torch.hub.load_state_dict_from_url(UpperCamelCase_						)['model']

						print('Converting model parameters...'						)
						# rename keys
						for key in state_dict.copy().keys():
												lowerCAmelCase__			  =  state_dict.pop(UpperCamelCase_						)
												lowerCAmelCase__			  =  val
						# add prefix to all keys expect classifier head
						for key in state_dict.copy().keys():
												lowerCAmelCase__			  =  state_dict.pop(UpperCamelCase_						)
												if not key.startswith('classifier'						):
																		lowerCAmelCase__			  =  'convnextv2.' + key
												lowerCAmelCase__			  =  val

						# load HuggingFace model
						lowerCAmelCase__			  =  ConvNextVaForImageClassification(UpperCamelCase_						)
						model.load_state_dict(UpperCamelCase_						)
						model.eval()

						# Check outputs on an image, prepared by ConvNextImageProcessor
						lowerCAmelCase__			  =  convert_preprocessor(UpperCamelCase_						)
						lowerCAmelCase__			  =  preprocessor(images=prepare_img()			,				return_tensors='pt'						)
						lowerCAmelCase__			  =  model(**UpperCamelCase_						).logits

						# note: the logits below were obtained without center cropping
						if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([-0.3930, 0.1747, -0.5246, 0.4177, 0.4295]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([-0.1727, -0.5341, -0.7818, -0.4745, -0.6566]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([-0.0333, 0.1563, -0.9137, 0.1054, 0.0381]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([-0.1744, -0.1555, -0.0713, 0.0950, -0.1431]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([0.9996, 0.1966, -0.4386, -0.3472, 0.6661]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([-0.2553, -0.6708, -0.1359, 0.2518, -0.2488]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([-0.0673, -0.5627, -0.3753, -0.2722, 0.0178]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([-0.6377, -0.7458, -0.2150, 0.1184, -0.0597]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([1.0799, 0.2322, -0.8860, 1.0219, 0.6231]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([0.3766, 0.4917, -1.1426, 0.9942, 0.6024]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([0.4220, -0.6919, -0.4317, -0.2881, -0.6609]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([0.1082, -0.8286, -0.5095, 0.4681, -0.8085]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([-0.2419, -0.6221, 0.2176, -0.0980, -0.7527]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([0.0391, -0.4371, 0.3786, 0.1251, -0.2784]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([-0.0504, 0.5636, -0.1729, -0.6507, -0.3949]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([0.3560, 0.9486, 0.3149, -0.2667, -0.5138]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([-0.2469, -0.4550, -0.5853, -0.0810, 0.0309]						)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
												lowerCAmelCase__			  =  torch.tensor([-0.3090, 0.0802, -0.0682, -0.1979, -0.2826]						)
						else:
												raise ValueError(F"Unknown URL: {checkpoint_url}"						)

						assert torch.allclose(logits[0, :5]			,				UpperCamelCase_			,				atol=1e-3						)
						assert logits.shape == expected_shape
						print('Model outputs match the original results!'						)

						if save_model:
												print('Saving model to local...'						)
												# Create folder to save model
												if not os.path.isdir(UpperCamelCase_						):
																		os.mkdir(UpperCamelCase_						)

												model.save_pretrained(UpperCamelCase_						)
												preprocessor.save_pretrained(UpperCamelCase_						)

						lowerCAmelCase__			  =  'convnextv2'
						if "atto" in checkpoint_url:
												model_name += "-atto"
						if "femto" in checkpoint_url:
												model_name += "-femto"
						if "pico" in checkpoint_url:
												model_name += "-pico"
						if "nano" in checkpoint_url:
												model_name += "-nano"
						elif "tiny" in checkpoint_url:
												model_name += "-tiny"
						elif "base" in checkpoint_url:
												model_name += "-base"
						elif "large" in checkpoint_url:
												model_name += "-large"
						elif "huge" in checkpoint_url:
												model_name += "-huge"
						if "22k" in checkpoint_url and "1k" not in checkpoint_url:
												model_name += "-22k"
						elif "22k" in checkpoint_url and "1k" in checkpoint_url:
												model_name += "-22k-1k"
						elif "1k" in checkpoint_url:
												model_name += "-1k"
						if "224" in checkpoint_url:
												model_name += "-224"
						elif "384" in checkpoint_url:
												model_name += "-384"
						elif "512" in checkpoint_url:
												model_name += "-512"

						if push_to_hub:
												print(F"Pushing {model_name} to the hub..."						)
												model.push_to_hub(UpperCamelCase_						)
												preprocessor.push_to_hub(UpperCamelCase_						)


if __name__ == "__main__":
					__snake_case			:			str			   =   argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--checkpoint_url""",
					    default="""https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt""",
					    type=str,
					    help="""URL of the original ConvNeXTV2 checkpoint you'd like to convert.""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default="""model""",
					    type=str,
					    help="""Path to the output PyTorch model directory.""",
					)
					parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
					parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image preprocessor to the hub""")

					__snake_case			:			List[str]			   =   parser.parse_args()
					convert_convnextva_checkpoint(
					    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
					)












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							(   lowercase_					):
  __lowerCamelCase		=			['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']







  def __init__(			self  :List[str]    ,							_lowercase  :Union[str, Any]					):






     '''simple docstring'''





     assert Path(hparams.data_dir					).exists()
     lowercase__    =  Path(hparams.output_dir					)
     self.output_dir.mkdir(exist_ok=_lowercase					)

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

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

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

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

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

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







  def   UpperCAmelCase     (			self  :Union[str, Any]    ,							_lowercase  :Optional[int]    ,							_lowercase  :Optional[int]    ,							_lowercase  :Any					):






     '''simple docstring'''





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






     '''simple docstring'''





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







  def   UpperCAmelCase     (			self  :Optional[Any]    ,							_lowercase  :dict					):






     '''simple docstring'''





     lowercase__    =  self.tokenizer.pad_token_id
     lowercase__   ,							lowercase__   ,							lowercase__    =  batch["input_ids"], batch["attention_mask"], batch["labels"]
     if isinstance(self.model    ,							_lowercase					):
        lowercase__    =  self.model._shift_right(_lowercase					)
     else:
        lowercase__    =  shift_tokens_right(_lowercase    ,							_lowercase					)

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

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

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

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

     lowercase__    =  (
         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     (			_lowercase  :int    ,							_lowercase  :Union[str, Any]    ,							_lowercase  :List[Any]    ,							_lowercase  :Any    ,							_lowercase  :Optional[Any]					):






     '''simple docstring'''





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


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





class 					lowerCAmelCase							(   lowercase_					):
  __lowerCamelCase		=			'translation'
  __lowerCamelCase		=			['bleu']
  __lowerCamelCase		=			'bleu'







  def __init__(			self  :int    ,							_lowercase  :int    ,							**_lowercase  :Union[str, Any]					):






     '''simple docstring'''





     super().__init__(_lowercase    ,							**_lowercase					)
     assert hparams.src_lang is not None
     assert hparams.tgt_lang is not None
     lowercase__    =  hparams.src_lang
     lowercase__    =  hparams.tgt_lang
     if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer    ,							_lowercase					):
        lowercase__    =  self.tokenizer.lang_code_to_id[hparams.tgt_lang]







  def   UpperCAmelCase     (			self  :str    ,							_lowercase  :List[str]    ,							_lowercase  :Optional[int]					):






     '''simple docstring'''





     return calculate_bleu(_lowercase    ,							_lowercase					)







  @staticmethod
  def   UpperCAmelCase     (			_lowercase  :str    ,							_lowercase  :List[Any]					):






     '''simple docstring'''





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


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


def _A   (    __magic_name__       ):
   Path(args.output_dir       ).mkdir(exist_ok=__magic_name__       )
   check_output_dir(__magic_name__  ,					expected_items=3       )

   lowercase__    =  create_module(__magic_name__       )
   return ft_main(__magic_name__  ,					model=__magic_name__       )


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

  distill_main(args)






