



'''simple docstring'''






import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


UpperCamelCase__:   Any								=			{
    "distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    "roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    "bert": (BertConfig, BertForMaskedLM, BertTokenizer),
    "gpt2": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}


def  snake_case_     (					_lowerCAmelCase  :						Optional[Any]  )      ->   Tuple:
							assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
							assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
							if args.mlm:
														assert os.path.isfile(args.token_counts  )
														assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
							else:
														assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

							assert args.teacher_type == args.student_type or (
							    args.student_type == "distilbert" and args.teacher_type == "bert"
							)
							assert os.path.isfile(args.student_config  )
							if args.student_pretrained_weights is not None:
														assert os.path.isfile(args.student_pretrained_weights  )

							if args.freeze_token_type_embds:
														assert args.student_type in ["roberta"]

							assert args.alpha_ce >= 0.0
							assert args.alpha_mlm >= 0.0
							assert args.alpha_clm >= 0.0
							assert args.alpha_mse >= 0.0
							assert args.alpha_cos >= 0.0
							assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0


def  snake_case_     (					_lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						Optional[Any]  )      ->   str:
							if args.student_type == "roberta":
														UpperCAmelCase :       Any 							=		False
							elif args.student_type == "gpt2":
														UpperCAmelCase :       List[Any] 							=		False


def  snake_case_     (					_lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						Union[str, Any]  )      ->   str:
							if args.student_type == "roberta":
														UpperCAmelCase :       List[Any] 							=		False


def  snake_case_     (					)      ->   Union[str, Any]:
							UpperCAmelCase :       str 							=		argparse.ArgumentParser(description='''Training'''  )
							parser.add_argument('''--force'''   ,      action='''store_true'''   ,      help='''Overwrite dump_path if it already exists.'''  )

							parser.add_argument(
							    '''--dump_path'''   ,      type=_lowerCAmelCase   ,      required=_lowerCAmelCase   ,      help='''The output directory (log, checkpoints, parameters, etc.)'''  )
							parser.add_argument(
							    '''--data_file'''   ,      type=_lowerCAmelCase   ,      required=_lowerCAmelCase   ,      help='''The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'''   ,      )

							parser.add_argument(
							    '''--student_type'''   ,      type=_lowerCAmelCase   ,      choices=['''distilbert''', '''roberta''', '''gpt2''']   ,      required=_lowerCAmelCase   ,      help='''The student type (DistilBERT, RoBERTa).'''   ,      )
							parser.add_argument('''--student_config'''   ,      type=_lowerCAmelCase   ,      required=_lowerCAmelCase   ,      help='''Path to the student configuration.'''  )
							parser.add_argument(
							    '''--student_pretrained_weights'''   ,      default=_lowerCAmelCase   ,      type=_lowerCAmelCase   ,      help='''Load student initialization checkpoint.'''  )

							parser.add_argument(
							    '''--teacher_type'''   ,      choices=['''bert''', '''roberta''', '''gpt2''']   ,      required=_lowerCAmelCase   ,      help='''Teacher type (BERT, RoBERTa).'''  )
							parser.add_argument('''--teacher_name'''   ,      type=_lowerCAmelCase   ,      required=_lowerCAmelCase   ,      help='''The teacher model.'''  )

							parser.add_argument('''--temperature'''   ,      default=2.0   ,      type=_lowerCAmelCase   ,      help='''Temperature for the softmax temperature.'''  )
							parser.add_argument(
							    '''--alpha_ce'''   ,      default=0.5   ,      type=_lowerCAmelCase   ,      help='''Linear weight for the distillation loss. Must be >=0.'''  )
							parser.add_argument(
							    '''--alpha_mlm'''   ,      default=0.0   ,      type=_lowerCAmelCase   ,      help='''Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.'''   ,      )
							parser.add_argument('''--alpha_clm'''   ,      default=0.5   ,      type=_lowerCAmelCase   ,      help='''Linear weight for the CLM loss. Must be >=0.'''  )
							parser.add_argument('''--alpha_mse'''   ,      default=0.0   ,      type=_lowerCAmelCase   ,      help='''Linear weight of the MSE loss. Must be >=0.'''  )
							parser.add_argument(
							    '''--alpha_cos'''   ,      default=0.0   ,      type=_lowerCAmelCase   ,      help='''Linear weight of the cosine embedding loss. Must be >=0.'''  )

							parser.add_argument(
							    '''--mlm'''   ,      action='''store_true'''   ,      help='''The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.'''  )
							parser.add_argument(
							    '''--mlm_mask_prop'''   ,      default=0.1_5   ,      type=_lowerCAmelCase   ,      help='''Proportion of tokens for which we need to make a prediction.'''   ,      )
							parser.add_argument('''--word_mask'''   ,      default=0.8   ,      type=_lowerCAmelCase   ,      help='''Proportion of tokens to mask out.'''  )
							parser.add_argument('''--word_keep'''   ,      default=0.1   ,      type=_lowerCAmelCase   ,      help='''Proportion of tokens to keep.'''  )
							parser.add_argument('''--word_rand'''   ,      default=0.1   ,      type=_lowerCAmelCase   ,      help='''Proportion of tokens to randomly replace.'''  )
							parser.add_argument(
							    '''--mlm_smoothing'''   ,      default=0.7   ,      type=_lowerCAmelCase   ,      help='''Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'''   ,      )
							parser.add_argument('''--token_counts'''   ,      type=_lowerCAmelCase   ,      help='''The token counts in the data_file for MLM.'''  )

							parser.add_argument(
							    '''--restrict_ce_to_mask'''   ,      action='''store_true'''   ,      help='''If true, compute the distillation loss only the [MLM] prediction distribution.'''   ,      )
							parser.add_argument(
							    '''--freeze_pos_embs'''   ,      action='''store_true'''   ,      help='''Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.'''   ,      )
							parser.add_argument(
							    '''--freeze_token_type_embds'''   ,      action='''store_true'''   ,      help='''Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.'''   ,      )

							parser.add_argument('''--n_epoch'''   ,      type=_lowerCAmelCase   ,      default=3   ,      help='''Number of pass on the whole dataset.'''  )
							parser.add_argument('''--batch_size'''   ,      type=_lowerCAmelCase   ,      default=5   ,      help='''Batch size (for each process).'''  )
							parser.add_argument(
							    '''--group_by_size'''   ,      action='''store_false'''   ,      help='''If true, group sequences that have similar length into the same batch. Default is true.'''   ,      )

							parser.add_argument(
							    '''--gradient_accumulation_steps'''   ,      type=_lowerCAmelCase   ,      default=50   ,      help='''Gradient accumulation for larger training batches.'''   ,      )
							parser.add_argument('''--warmup_prop'''   ,      default=0.0_5   ,      type=_lowerCAmelCase   ,      help='''Linear warmup proportion.'''  )
							parser.add_argument('''--weight_decay'''   ,      default=0.0   ,      type=_lowerCAmelCase   ,      help='''Weight decay if we apply some.'''  )
							parser.add_argument('''--learning_rate'''   ,      default=5e-4   ,      type=_lowerCAmelCase   ,      help='''The initial learning rate for Adam.'''  )
							parser.add_argument('''--adam_epsilon'''   ,      default=1e-6   ,      type=_lowerCAmelCase   ,      help='''Epsilon for Adam optimizer.'''  )
							parser.add_argument('''--max_grad_norm'''   ,      default=5.0   ,      type=_lowerCAmelCase   ,      help='''Max gradient norm.'''  )
							parser.add_argument('''--initializer_range'''   ,      default=0.0_2   ,      type=_lowerCAmelCase   ,      help='''Random initialization range.'''  )

							parser.add_argument(
							    '''--fp16'''   ,      action='''store_true'''   ,      help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''   ,      )
							parser.add_argument(
							    '''--fp16_opt_level'''   ,      type=_lowerCAmelCase   ,      default='''O1'''   ,      help=(
							        '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
							        '''See details at https://nvidia.github.io/apex/amp.html'''
							    )   ,      )
							parser.add_argument('''--n_gpu'''   ,      type=_lowerCAmelCase   ,      default=1   ,      help='''Number of GPUs in the node.'''  )
							parser.add_argument('''--local_rank'''   ,      type=_lowerCAmelCase   ,      default=-1   ,      help='''Distributed training - Local rank'''  )
							parser.add_argument('''--seed'''   ,      type=_lowerCAmelCase   ,      default=56   ,      help='''Random seed'''  )

							parser.add_argument('''--log_interval'''   ,      type=_lowerCAmelCase   ,      default=500   ,      help='''Tensorboard logging interval.'''  )
							parser.add_argument('''--checkpoint_interval'''   ,      type=_lowerCAmelCase   ,      default=4000   ,      help='''Checkpoint interval.'''  )
							UpperCAmelCase :       Optional[int] 							=		parser.parse_args()
							sanity_checks(_lowerCAmelCase  )

							# ARGS #
							init_gpu_params(_lowerCAmelCase  )
							set_seed(_lowerCAmelCase  )
							if args.is_master:
														if os.path.exists(args.dump_path  ):
																					if not args.force:
																												raise ValueError(
																												    f"""Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"""
																												    ''' itUse `--force` if you want to overwrite it'''  )
																					else:
																												shutil.rmtree(args.dump_path  )

														if not os.path.exists(args.dump_path  ):
																					os.makedirs(args.dump_path  )
														logger.info(f"""Experiment will be dumped and logged in {args.dump_path}"""  )

														# SAVE PARAMS #
														logger.info(f"""Param: {args}"""  )
														with open(os.path.join(args.dump_path   ,      '''parameters.json'''  )   ,      '''w'''  ) as f:
																					json.dump(vars(_lowerCAmelCase  )   ,      _lowerCAmelCase   ,      indent=4  )
														git_log(args.dump_path  )

							UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		MODEL_CLASSES[args.student_type]
							UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       List[str] 							=		MODEL_CLASSES[args.teacher_type]

							# TOKENIZER #
							UpperCAmelCase :       List[Any] 							=		teacher_tokenizer_class.from_pretrained(args.teacher_name  )
							UpperCAmelCase :       str 							=		{}
							for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
														UpperCAmelCase :       int 							=		tokenizer.all_special_tokens.index(_lowerCAmelCase  )
														UpperCAmelCase :       List[Any] 							=		tokenizer.all_special_ids[idx]
							logger.info(f"""Special tokens {special_tok_ids}"""  )
							UpperCAmelCase :       Any 							=		special_tok_ids
							UpperCAmelCase :       List[Any] 							=		tokenizer.max_model_input_sizes[args.teacher_name]

							# DATA LOADER #
							logger.info(f"""Loading data from {args.data_file}"""  )
							with open(args.data_file   ,      '''rb'''  ) as fp:
														UpperCAmelCase :       str 							=		pickle.load(_lowerCAmelCase  )

							if args.mlm:
														logger.info(f"""Loading token counts from {args.token_counts} (already pre-computed)"""  )
														with open(args.token_counts   ,      '''rb'''  ) as fp:
																					UpperCAmelCase :       List[str] 							=		pickle.load(_lowerCAmelCase  )

														UpperCAmelCase :       str 							=		np.maximum(_lowerCAmelCase   ,      1  ) ** -args.mlm_smoothing
														for idx in special_tok_ids.values():
																					UpperCAmelCase :       Optional[Any] 							=		0.0  # do not predict special tokens
														UpperCAmelCase :       str 							=		torch.from_numpy(_lowerCAmelCase  )
							else:
														UpperCAmelCase :       Optional[int] 							=		None

							UpperCAmelCase :       List[str] 							=		LmSeqsDataset(params=_lowerCAmelCase   ,      data=_lowerCAmelCase  )
							logger.info('''Data loader created.'''  )

							# STUDENT #
							logger.info(f"""Loading student config from {args.student_config}"""  )
							UpperCAmelCase :       Dict 							=		student_config_class.from_pretrained(args.student_config  )
							UpperCAmelCase :       List[str] 							=		True

							if args.student_pretrained_weights is not None:
														logger.info(f"""Loading pretrained weights from {args.student_pretrained_weights}"""  )
														UpperCAmelCase :       Optional[int] 							=		student_model_class.from_pretrained(args.student_pretrained_weights   ,      config=_lowerCAmelCase  )
							else:
														UpperCAmelCase :       Optional[Any] 							=		student_model_class(_lowerCAmelCase  )

							if args.n_gpu > 0:
														student.to(f"""cuda:{args.local_rank}"""  )
							logger.info('''Student loaded.'''  )

							# TEACHER #
							UpperCAmelCase :       List[Any] 							=		teacher_model_class.from_pretrained(args.teacher_name   ,      output_hidden_states=_lowerCAmelCase  )
							if args.n_gpu > 0:
														teacher.to(f"""cuda:{args.local_rank}"""  )
							logger.info(f"""Teacher loaded from {args.teacher_name}."""  )

							# FREEZING #
							if args.freeze_pos_embs:
														freeze_pos_embeddings(_lowerCAmelCase   ,      _lowerCAmelCase  )
							if args.freeze_token_type_embds:
														freeze_token_type_embeddings(_lowerCAmelCase   ,      _lowerCAmelCase  )

							# SANITY CHECKS #
							assert student.config.vocab_size == teacher.config.vocab_size
							assert student.config.hidden_size == teacher.config.hidden_size
							assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
							if args.mlm:
														assert token_probs.size(0  ) == stu_architecture_config.vocab_size

							# DISTILLER #
							torch.cuda.empty_cache()
							UpperCAmelCase :       Union[str, Any] 							=		Distiller(
							    params=_lowerCAmelCase   ,      dataset=_lowerCAmelCase   ,      token_probs=_lowerCAmelCase   ,      student=_lowerCAmelCase   ,      teacher=_lowerCAmelCase  )
							distiller.train()
							logger.info('''Let\'s go get some drinks.'''  )


if __name__ == "__main__":
						main()






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

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowercase__					       =	transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Any:
  if isinstance(_SCREAMING_SNAKE_CASE		,     torch.Tensor   ):
    return image
  elif isinstance(_SCREAMING_SNAKE_CASE		,     PIL.Image.Image   ):
    a__:     Optional[int]										=	[image]

  a__:     str										=	[trans(img.convert('RGB'   )   ) for img in image]
  a__:     Any										=	torch.stack(_SCREAMING_SNAKE_CASE   )
  return image




class    __snake_case       (     __lowerCAmelCase				):



      def __init__(						self    ,			lowercase    ,			lowercase)	->			Optional[int]:





        '''simple docstring'''





        super().__init__()

        # make sure scheduler can always be converted to DDIM
        a__:     Dict										=	DDIMScheduler.from_config(scheduler.config)

        self.register_modules(unet=lowercase    ,			scheduler=lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			int:





        '''simple docstring'''





        if strength < 0 or strength > 1:
          raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}')



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase)	->			Dict:





        '''simple docstring'''





        a__:     int										=	min(int(num_inference_steps * strength)    ,			lowercase)

        a__:     Any										=	max(num_inference_steps - init_timestep    ,			0)
        a__:     Union[str, Any]										=	self.scheduler.timesteps[t_start:]

        return timesteps, num_inference_steps - t_start



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase=None)	->			List[Any]:





        '''simple docstring'''





        if not isinstance(lowercase    ,			(torch.Tensor, PIL.Image.Image, list)):
          raise ValueError(
              f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase)}')

        a__:     Tuple										=	image.to(device=lowercase    ,			dtype=lowercase)

        if isinstance(lowercase    ,			lowercase) and len(lowercase) != batch_size:
          raise ValueError(
              f'You have passed a list of generators of length {len(lowercase)}, but requested an effective batch'
              f' size of {batch_size}. Make sure the batch size matches the length of the generators.')

        a__:     List[str]										=	init_latents.shape
        a__:     List[Any]										=	randn_tensor(lowercase    ,			generator=lowercase    ,			device=lowercase    ,			dtype=lowercase)

        # get latents
        print('add noise to latents at timestep'    ,			lowercase)
        a__:     int										=	self.scheduler.add_noise(lowercase    ,			lowercase    ,			lowercase)
        a__:     Dict										=	init_latents

        return latents



      @torch.no_grad()
      def __call__(						self    ,			lowercase = None    ,			lowercase = 0.8    ,			lowercase = 1    ,			lowercase = None    ,			lowercase = 0.0    ,			lowercase = 50    ,			lowercase = None    ,			lowercase = "pil"    ,			lowercase = True    ,			)	->			Union[ImagePipelineOutput, Tuple]:





        '''simple docstring'''





        self.check_inputs(lowercase)

        # 2. Preprocess image
        a__:     Tuple										=	preprocess(lowercase)

        # 3. set timesteps
        self.scheduler.set_timesteps(lowercase    ,			device=self.device)
        a__							,    a__:     Union[str, Any]										=	self.get_timesteps(lowercase    ,			lowercase    ,			self.device)
        a__:     Optional[int]										=	timesteps[:1].repeat(lowercase)

        # 4. Prepare latent variables
        a__:     Union[str, Any]										=	self.prepare_latents(lowercase    ,			lowercase    ,			lowercase    ,			self.unet.dtype    ,			self.device    ,			lowercase)
        a__:     Optional[Any]										=	latents

        # 5. Denoising loop
        for t in self.progress_bar(lowercase):
          # 1. predict noise model_output
          a__:     Dict										=	self.unet(lowercase    ,			lowercase).sample

          # 2. predict previous mean of image x_t-1 and add variance depending on eta
          # eta corresponds to η in paper and should be between [0, 1]
          # do x_t -> x_t-1
          a__:     Optional[Any]										=	self.scheduler.step(
              lowercase    ,			lowercase    ,			lowercase    ,			eta=lowercase    ,			use_clipped_model_output=lowercase    ,			generator=lowercase    ,			).prev_sample

        a__:     Union[str, Any]										=	(image / 2 + 0.5).clamp(0    ,			1)
        a__:     Optional[int]										=	image.cpu().permute(0    ,			2    ,			3    ,			1).numpy()
        if output_type == "pil":
          a__:     Dict										=	self.numpy_to_pil(lowercase)

        if not return_dict:
          return (image, latent_timestep.item())

        return ImagePipelineOutput(images=lowercase)


