code: 
  
  
  '''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 (		lowercase__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    List[Any]   					=       ["""loss""", """ce_loss""", """mlm_loss""", """hid_loss_enc""", """hid_loss_dec"""]
  
  
  
  
  
  
  
  		def __init__(      self	:		Dict						,  _A	:		List[str]    )						->				Dict:
  			assert Path(hparams.data_dir    ).exists()
  			__magic_name__ :					int  			=  Path(hparams.output_dir    )
  			self.output_dir.mkdir(exist_ok=_A    )
  
  			__magic_name__ :					str  			=  self.output_dir.joinpath('student'    )
  
  			__magic_name__ :					int  			=  str(_A    )  # Tell lightning we are training the student
  			__magic_name__ :					int  			=  AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher    ).eval()
  			use_task_specific_params(_A						,  hparams.task    )  # We copy good generation parameters to student by default
  			if hparams.student is not None:
  				__magic_name__ :					int  			=  AutoModelForSeqaSeqLM.from_pretrained(hparams.student    )
  				use_task_specific_params(_A						,  hparams.task    )
  				__magic_name__			,       __magic_name__ :					List[str]  			=  None, None
  			else:
  				__magic_name__			,       __magic_name__			,       __magic_name__ :					int  			=  create_student_by_copying_alternating_layers(
  				    _A						,  e=hparams.student_encoder_layers						,  d=hparams.student_decoder_layers						,  save_path=_A    )
  
  			if hparams.length_penalty != -1:
  				__magic_name__ :					Optional[int]  			=  hparams.length_penalty
  			__magic_name__ :					Union[str, Any]  			=  hparams.teacher  # Use teacher's tokenizer
  			super().__init__(_A						,  model=_A						,  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":
  				__magic_name__ :					Dict  			=  len(student.get_encoder().block    )
  				__magic_name__ :					List[Any]  			=  len(student.get_decoder().block    )
  				__magic_name__ :					str  			=  len(teacher.get_encoder().block    )
  				__magic_name__ :					List[Any]  			=  len(teacher.get_decoder().block    )
  			else:
  				__magic_name__ :					Optional[Any]  			=  student.config.encoder_layers
  				__magic_name__ :					Optional[int]  			=  student.config.decoder_layers
  				__magic_name__ :					Optional[Any]  			=  teacher.config.encoder_layers
  				__magic_name__ :					str  			=  teacher.config.decoder_layers
  
  			__magic_name__ :					Optional[Any]  			=  not (hparams.student is None or hparams.teacher == hparams.student)
  			__magic_name__ :					int  			=  (not self.different_base_models) and hparams.alpha_hid > 0
  			__magic_name__ :					Optional[int]  			=  self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
  			# self.different_encoder determines whether we need to run the teacher encoder
  			__magic_name__ :					List[Any]  			=  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:
  				__magic_name__ :					Dict  			=  list(range(_A    )    )
  			if d_layer_ids is None:
  				__magic_name__ :					Optional[int]  			=  list(range(_A    )    )
  
  			__magic_name__			,       __magic_name__ :					Optional[Any]  			=  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:
  					__magic_name__ :					Optional[Any]  			=  get_layers_to_supervise(
  					    n_student=len(self.e_layer_ids    )						,  n_teacher=_A    )
  					__magic_name__ :					Optional[Any]  			=  get_layers_to_supervise(
  					    n_student=len(self.d_layer_ids    )						,  n_teacher=_A    )
  				else:  # student layer should emulate hidden states of the teacher layer it was copied from
  					__magic_name__ :					int  			=  self.e_layer_ids
  					__magic_name__ :					Any  			=  self.d_layer_ids
  			else:
  				__magic_name__ :					Tuple  			=  None
  				__magic_name__ :					int  			=  None
  
  			__magic_name__ :					int  			=  nn.KLDivLoss(reduction='batchmean'    )
  			__magic_name__ :					Optional[int]  			=  2.0
  			__magic_name__ :					Optional[int]  			=  hparams.alpha_mlm
  			__magic_name__ :					int  			=  hparams.alpha_ce
  			__magic_name__ :					int  			=  hparams.alpha_hid
  			gc.collect()
  			torch.cuda.empty_cache()
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Any						,  _A	:		Optional[int]						,  _A	:		Dict						,  _A	:		Optional[Any]    )						->				List[str]:
  			__magic_name__ :					List[str]  			=  mask[:, :, None].expand_as(_A    )
  			__magic_name__ :					Any  			=  s_logits.size(-1    )
  			__magic_name__ :					Tuple  			=  torch.masked_select(_A						,  _A    )  # (bs * seq_length * voc_size) modulo the 1s in mask
  			__magic_name__ :					str  			=  torch.masked_select(_A						,  _A    )  # (bs * seq_length * voc_size) modulo the 1s in mask
  			__magic_name__ :					Union[str, Any]  			=  s_logits_slct.view(-1						,  _A    )  # (bs * seq_length, voc_size) modulo the 1s in mask
  			__magic_name__ :					Any  			=  t_logits_slct.view(-1						,  _A    )  # (bs * seq_length, voc_size) modulo the 1s in mask
  			assert t_logits_slct.size() == s_logits_slct.size()
  			__magic_name__ :					List[str]  			=  (
  			    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	(      _A	:		Tuple						,  _A	:		Any    )						->				List[str]:
  			SummarizationModule.add_model_specific_args(_A						,  _A    )
  			add_distill_args(_A    )
  			return parser
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]						,  _A	:		dict    )						->				tuple:
  			__magic_name__ :					List[Any]  			=  self.tokenizer.pad_token_id
  			__magic_name__			,       __magic_name__			,       __magic_name__ :					List[Any]  			=  batch['input_ids'], batch['attention_mask'], batch['labels']
  			if isinstance(self.model						,  _A    ):
  				__magic_name__ :					Union[str, Any]  			=  self.model._shift_right(_A    )
  			else:
  				__magic_name__ :					Union[str, Any]  			=  shift_tokens_right(_A						,  _A    )
  
  			# noinspection PyCallingNonCallable
  			__magic_name__ :					Union[str, Any]  			=  self(
  			    _A						,  attention_mask=_A						,  decoder_input_ids=_A						,  output_hidden_states=self.do_calc_hidden_loss						,  output_attentions=_A						,  use_cache=_A						,  )
  			__magic_name__ :					List[Any]  			=  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
  				__magic_name__ :					Optional[int]  			=  nn.CrossEntropyLoss(ignore_index=_A    )
  				__magic_name__ :					str  			=  loss_fct(lm_logits.view(-1						,  lm_logits.shape[-1]    )						,  labels.view(-1    )    )
  			else:
  				__magic_name__ :					Any  			=  nn.functional.log_softmax(_A						,  dim=-1    )
  				__magic_name__			,       __magic_name__ :					List[str]  			=  label_smoothed_nll_loss(
  				    _A						,  _A						,  self.hparams.label_smoothing						,  ignore_index=_A    )
  
  			def zero_tensor():
  				return torch.tensor(0.0    ).type_as(_A    )
  
  			__magic_name__ :					Optional[Any]  			=  student_outputs[
  			    'encoder_last_hidden_state'
  			]  # use this unless self.different_base_models
  			__magic_name__			,       __magic_name__ :					Any  			=  zero_tensor(), zero_tensor()
  			if self.different_encoder:  # compute encoder hidden state loss
  				__magic_name__ :					Optional[Any]  			=  self.teacher.get_encoder()(
  				    _A						,  attention_mask=_A						,  output_hidden_states=self.do_calc_hidden_loss						,  )
  				if self.different_base_models:
  					__magic_name__ :					Optional[int]  			=  all_teacher_encoder_outputs['last_hidden_state']
  				elif self.do_calc_hidden_loss:
  					__magic_name__ :					Any  			=  self.calc_hidden_loss(
  					    _A						,  student_outputs['encoder_hidden_states']						,  all_teacher_encoder_outputs['hidden_states']						,  self.e_matches						,  normalize_hidden=self.hparams.normalize_hidden						,  )
  
  			__magic_name__ :					Any  			=  self.teacher(
  			    _A						,  attention_mask=_A						,  encoder_outputs=(teacher_enc_outputs,)						,  decoder_input_ids=_A						,  output_hidden_states=self.do_calc_hidden_loss						,  use_cache=_A						,  )
  			__magic_name__ :					Tuple  			=  decoder_input_ids.ne(_A    )
  			__magic_name__ :					str  			=  self.calc_ce_loss(_A						,  _A						,  teacher_outputs['logits']    )
  			if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
  				__magic_name__ :					List[Any]  			=  self.calc_hidden_loss(
  				    _A						,  student_outputs['decoder_hidden_states']						,  teacher_outputs['decoder_hidden_states']						,  self.d_matches						,  normalize_hidden=self.hparams.normalize_hidden						,  )
  
  			__magic_name__ :					int  			=  (
  			    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	(      _A	:		int						,  _A	:		Optional[Any]						,  _A	:		Optional[Any]						,  _A	:		Optional[Any]						,  _A	:		Union[str, Any]    )						->				Any:
  			__magic_name__ :					Tuple  			=  'expected list or tuple for hidden_states, got tensor of shape: '
  			assert not isinstance(_A						,  torch.Tensor    ), F'{msg}{hidden_states.shape}'
  			assert not isinstance(_A						,  torch.Tensor    ), F'{msg}{hidden_states_T.shape}'
  			__magic_name__ :					str  			=  attention_mask.to(hidden_states[0]    )
  			__magic_name__ :					Dict  			=  mask.sum() * hidden_states[0].size(-1    )
  			__magic_name__ :					int  			=  torch.stack([hidden_states[i] for i in range(len(_A    )    )]    )
  			__magic_name__ :					Any  			=  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:
  				__magic_name__ :					Optional[int]  			=  nn.functional.layer_norm(_A						,  student_states.shape[1:]    )
  				__magic_name__ :					Dict  			=  nn.functional.layer_norm(_A						,  teacher_states.shape[1:]    )
  			__magic_name__ :					int  			=  nn.functional.mse_loss(_A						,  _A						,  reduction='none'    )
  			__magic_name__ :					Union[str, Any]  			=  (mse * mask.unsqueeze(0    ).unsqueeze(-1    )).sum() / valid_count
  			return masked_mse
  
  
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			List[str]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	parser.add_argument('--teacher'	,	type=lowerCAmelCase	)
  	parser.add_argument('--alpha_ce'	,	default=0.8	,	type=lowerCAmelCase	)
  	parser.add_argument('--alpha_mlm'	,	default=0.2	,	type=lowerCAmelCase	)
  	parser.add_argument('--alpha_hid'	,	default=0.0	,	type=lowerCAmelCase	,	required=lowerCAmelCase	)
  	parser.add_argument('--student'	,	type=lowerCAmelCase	,	required=lowerCAmelCase	)
  	parser.add_argument('--student_decoder_layers'	,	default=12	,	type=lowerCAmelCase	,	required=lowerCAmelCase	)
  	parser.add_argument('--student_encoder_layers'	,	default=12	,	type=lowerCAmelCase	,	required=lowerCAmelCase	)
  	parser.add_argument('--no_teacher'	,	action='store_true'	,	default=lowerCAmelCase	)
  	parser.add_argument('--length_penalty'	,	type=lowerCAmelCase	,	default=-1	)
  	parser.add_argument('--supervise_forward'	,	action='store_true'	,	default=lowerCAmelCase	)
  	parser.add_argument('--normalize_hidden'	,	action='store_true'	,	default=lowerCAmelCase	)
  class _lowerCamelCase (		lowercase__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    Optional[Any]   					=       """translation"""
  		A_			:    List[Any]   					=       ["""bleu"""]
  		A_			:    Any   					=       """bleu"""
  
  
  
  
  
  
  
  		def __init__(      self	:		Tuple						,  _A	:		Any						,  **_A	:		Optional[int]    )						->				str:
  			super().__init__(_A						,  **_A    )
  			assert hparams.src_lang is not None
  			assert hparams.tgt_lang is not None
  			__magic_name__ :					Optional[Any]  			=  hparams.src_lang
  			__magic_name__ :					int  			=  hparams.tgt_lang
  			if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer						,  _A    ):
  				__magic_name__ :					Any  			=  self.tokenizer.lang_code_to_id[hparams.tgt_lang]
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Any						,  _A	:		Optional[int]						,  _A	:		List[str]    )						->				dict:
  			return calculate_bleu(_A						,  _A    )
  
  
  
  
  
  
  
  		@staticmethod
  		def      __lowerCAmelCase	(      _A	:		List[str]						,  _A	:		int    )						->				Any:
  			TranslationModule.add_model_specific_args(_A						,  _A    )
  			add_distill_args(_A    )
  			return parser
  
  
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			List[str]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if args.no_teacher:
  		__magic_name__ :					Union[str, Any]  			=  TranslationModule if 'translation' in args.task else SummarizationModule
  	else:  # DISTILL WITH TEACHER
  		__magic_name__ :					Union[str, Any]  			=  TranslationDistiller if 'translation' in args.task else SummarizationDistiller
  	__magic_name__ :					str  			=  module_cls.__name__
  	print(f'using module {args.setup_cls}'	)
  	__magic_name__ :					Union[str, Any]  			=  module_cls(lowerCAmelCase	)
  	return model
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			int	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	Path(args.output_dir	).mkdir(exist_ok=lowerCAmelCase	)
  	check_output_dir(lowerCAmelCase	,	expected_items=3	)
  
  	__magic_name__ :					List[Any]  			=  create_module(lowerCAmelCase	)
  	return ft_main(lowerCAmelCase	,	model=lowerCAmelCase	)
  
  
  if __name__ == "__main__":
  							lowerCAmelCase   :Tuple								=    argparse.ArgumentParser()
  							lowerCAmelCase   :List[str]								=    pl.Trainer.add_argparse_args(parser)
  							lowerCAmelCase   :List[Any]								=    SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
  							lowerCAmelCase   :Any								=    parser.parse_args()
  
  							distill_main(args)
style_context: 
  
  
  '''simple docstring'''
  
  import random
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
  from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch
  
  
  enable_full_determinism()
  class _lowerCamelCase (		unittest.TestCase							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				List[Any]:
  			__magic_name__ :					Union[str, Any]  			=  1
  			__magic_name__ :					str  			=  3
  			__magic_name__ :					Optional[Any]  			=  (32, 32)
  
  			__magic_name__ :					Dict  			=  floats_tensor((batch_size, num_channels) + sizes						,  rng=random.Random(0    )    ).to(_A    )
  			return image
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		Optional[Any]    )						->				List[str]:
  			torch.manual_seed(0    )
  			__magic_name__ :					Dict  			=  UNetaDModel(
  			    block_out_channels=(32, 64)						,  layers_per_block=2						,  sample_size=32						,  in_channels=6						,  out_channels=3						,  down_block_types=('DownBlock2D', 'AttnDownBlock2D')						,  up_block_types=('AttnUpBlock2D', 'UpBlock2D')						,  )
  			return model
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		Any    )						->				Optional[int]:
  			torch.manual_seed(0    )
  			__magic_name__ :					Union[str, Any]  			=  VQModel(
  			    block_out_channels=[32, 64]						,  in_channels=3						,  out_channels=3						,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']						,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']						,  latent_channels=3						,  )
  			return model
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[int]    )						->				int:
  			__magic_name__ :					Dict  			=  'cpu'
  			__magic_name__ :					str  			=  self.dummy_uncond_unet
  			__magic_name__ :					Optional[int]  			=  DDIMScheduler()
  			__magic_name__ :					Optional[Any]  			=  self.dummy_vq_model
  
  			__magic_name__ :					Tuple  			=  LDMSuperResolutionPipeline(unet=_A						,  vqvae=_A						,  scheduler=_A    )
  			ldm.to(_A    )
  			ldm.set_progress_bar_config(disable=_A    )
  
  			__magic_name__ :					Optional[int]  			=  self.dummy_image.to(_A    )
  
  			__magic_name__ :					List[Any]  			=  torch.Generator(device=_A    ).manual_seed(0    )
  			__magic_name__ :					str  			=  ldm(image=_A						,  generator=_A						,  num_inference_steps=2						,  output_type='numpy'    ).images
  
  			__magic_name__ :					List[Any]  			=  image[0, -3:, -3:, -1]
  
  			assert image.shape == (1, 64, 64, 3)
  			__magic_name__ :					Optional[Any]  			=  np.array([0.8678, 0.8245, 0.6381, 0.6830, 0.4385, 0.5599, 0.4641, 0.6201, 0.5150]    )
  
  			assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
  
  
  
  
  
  
  
  		@unittest.skipIf(torch_device != 'cuda'						,  'This test requires a GPU'    )
  		def      __lowerCAmelCase	(      self	:		List[Any]    )						->				List[Any]:
  			__magic_name__ :					Union[str, Any]  			=  self.dummy_uncond_unet
  			__magic_name__ :					Tuple  			=  DDIMScheduler()
  			__magic_name__ :					Union[str, Any]  			=  self.dummy_vq_model
  
  			# put models in fp16
  			__magic_name__ :					Optional[int]  			=  unet.half()
  			__magic_name__ :					int  			=  vqvae.half()
  
  			__magic_name__ :					Optional[Any]  			=  LDMSuperResolutionPipeline(unet=_A						,  vqvae=_A						,  scheduler=_A    )
  			ldm.to(_A    )
  			ldm.set_progress_bar_config(disable=_A    )
  
  			__magic_name__ :					str  			=  self.dummy_image.to(_A    )
  
  			__magic_name__ :					Union[str, Any]  			=  ldm(_A						,  num_inference_steps=2						,  output_type='numpy'    ).images
  
  			assert image.shape == (1, 64, 64, 3)
  
  
  
  
  
  
  
  @slow
  @require_torch
  class _lowerCamelCase (		unittest.TestCase							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		str    )						->				Dict:
  			__magic_name__ :					Tuple  			=  load_image(
  			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
  			    '/vq_diffusion/teddy_bear_pool.png'    )
  			__magic_name__ :					Dict  			=  init_image.resize((64, 64)						,  resample=PIL_INTERPOLATION['lanczos']    )
  
  			__magic_name__ :					List[Any]  			=  LDMSuperResolutionPipeline.from_pretrained('duongna/ldm-super-resolution'						,  device_map='auto'    )
  			ldm.set_progress_bar_config(disable=_A    )
  
  			__magic_name__ :					int  			=  torch.manual_seed(0    )
  			__magic_name__ :					Optional[Any]  			=  ldm(image=_A						,  generator=_A						,  num_inference_steps=20						,  output_type='numpy'    ).images
  
  			__magic_name__ :					List[str]  			=  image[0, -3:, -3:, -1]
  
  			assert image.shape == (1, 256, 256, 3)
  			__magic_name__ :					int  			=  np.array([0.7644, 0.7679, 0.7642, 0.7633, 0.7666, 0.7560, 0.7425, 0.7257, 0.6907]    )
  
  			assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2