






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


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


def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  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   lowerCamelCase__			(     lowercase						,     lowercase				):

  """simple docstring"""






  if args.student_type == "roberta":
    SCREAMING_SNAKE_CASE       :      Tuple							=    False
  elif args.student_type == "gpt2":
    SCREAMING_SNAKE_CASE       :      Any							=    False


def   lowerCamelCase__			(     lowercase						,     lowercase				):

  """simple docstring"""






  if args.student_type == "roberta":
    SCREAMING_SNAKE_CASE       :      List[Any]							=    False


def   lowerCamelCase__			(     ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Tuple							=    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=lowercase						,     required=lowercase						,     help="The output directory (log, checkpoints, parameters, etc.)"				)
  parser.add_argument(
      "--data_file"						,     type=lowercase						,     required=lowercase						,     help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence."						,     )

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

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

  parser.add_argument("--temperature"						,     default=2.0						,     type=lowercase						,     help="Temperature for the softmax temperature."				)
  parser.add_argument(
      "--alpha_ce"						,     default=0.5						,     type=lowercase						,     help="Linear weight for the distillation loss. Must be >=0."				)
  parser.add_argument(
      "--alpha_mlm"						,     default=0.0						,     type=lowercase						,     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=lowercase						,     help="Linear weight for the CLM loss. Must be >=0."				)
  parser.add_argument("--alpha_mse"						,     default=0.0						,     type=lowercase						,     help="Linear weight of the MSE loss. Must be >=0."				)
  parser.add_argument(
      "--alpha_cos"						,     default=0.0						,     type=lowercase						,     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=0.15						,     type=lowercase						,     help="Proportion of tokens for which we need to make a prediction."						,     )
  parser.add_argument("--word_mask"						,     default=0.8						,     type=lowercase						,     help="Proportion of tokens to mask out."				)
  parser.add_argument("--word_keep"						,     default=0.1						,     type=lowercase						,     help="Proportion of tokens to keep."				)
  parser.add_argument("--word_rand"						,     default=0.1						,     type=lowercase						,     help="Proportion of tokens to randomly replace."				)
  parser.add_argument(
      "--mlm_smoothing"						,     default=0.7						,     type=lowercase						,     help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."						,     )
  parser.add_argument("--token_counts"						,     type=lowercase						,     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=lowercase						,     default=3						,     help="Number of pass on the whole dataset."				)
  parser.add_argument("--batch_size"						,     type=lowercase						,     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=lowercase						,     default=50						,     help="Gradient accumulation for larger training batches."						,     )
  parser.add_argument("--warmup_prop"						,     default=0.05						,     type=lowercase						,     help="Linear warmup proportion."				)
  parser.add_argument("--weight_decay"						,     default=0.0						,     type=lowercase						,     help="Weight decay if we apply some."				)
  parser.add_argument("--learning_rate"						,     default=5E-4						,     type=lowercase						,     help="The initial learning rate for Adam."				)
  parser.add_argument("--adam_epsilon"						,     default=1E-6						,     type=lowercase						,     help="Epsilon for Adam optimizer."				)
  parser.add_argument("--max_grad_norm"						,     default=5.0						,     type=lowercase						,     help="Max gradient norm."				)
  parser.add_argument("--initializer_range"						,     default=0.02						,     type=lowercase						,     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=lowercase						,     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=lowercase						,     default=1						,     help="Number of GPUs in the node."				)
  parser.add_argument("--local_rank"						,     type=lowercase						,     default=-1						,     help="Distributed training - Local rank"				)
  parser.add_argument("--seed"						,     type=lowercase						,     default=56						,     help="Random seed"				)

  parser.add_argument("--log_interval"						,     type=lowercase						,     default=500						,     help="Tensorboard logging interval."				)
  parser.add_argument("--checkpoint_interval"						,     type=lowercase						,     default=4000						,     help="Checkpoint interval."				)
  SCREAMING_SNAKE_CASE       :      Tuple							=    parser.parse_args()
  sanity_checks(lowercase				)

  # ARGS #
  init_gpu_params(lowercase				)
  set_seed(lowercase				)
  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(lowercase				)						,     lowercase						,     indent=4				)
    git_log(args.dump_path				)

  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Tuple							=    MODEL_CLASSES[args.student_type]
  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      List[str]							=    MODEL_CLASSES[args.teacher_type]

  # TOKENIZER #
  SCREAMING_SNAKE_CASE       :      Optional[int]							=    teacher_tokenizer_class.from_pretrained(args.teacher_name				)
  SCREAMING_SNAKE_CASE       :      str							=    {}
  for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
    SCREAMING_SNAKE_CASE       :      str							=    tokenizer.all_special_tokens.index(lowercase				)
    SCREAMING_SNAKE_CASE       :      int							=    tokenizer.all_special_ids[idx]
  logger.info(F'''Special tokens {special_tok_ids}'''				)
  SCREAMING_SNAKE_CASE       :      Dict							=    special_tok_ids
  SCREAMING_SNAKE_CASE       :      int							=    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:
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    pickle.load(lowercase				)

  if args.mlm:
    logger.info(F'''Loading token counts from {args.token_counts} (already pre-computed)'''				)
    with open(args.token_counts						,     "rb"				) as fp:
      SCREAMING_SNAKE_CASE       :      Dict							=    pickle.load(lowercase				)

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

  SCREAMING_SNAKE_CASE       :      Optional[int]							=    LmSeqsDataset(params=lowercase						,     data=lowercase				)
  logger.info("Data loader created."				)

  # STUDENT #
  logger.info(F'''Loading student config from {args.student_config}'''				)
  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    student_config_class.from_pretrained(args.student_config				)
  SCREAMING_SNAKE_CASE       :      Optional[int]							=    True

  if args.student_pretrained_weights is not None:
    logger.info(F'''Loading pretrained weights from {args.student_pretrained_weights}'''				)
    SCREAMING_SNAKE_CASE       :      Any							=    student_model_class.from_pretrained(args.student_pretrained_weights						,     config=lowercase				)
  else:
    SCREAMING_SNAKE_CASE       :      Optional[int]							=    student_model_class(lowercase				)

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

  # TEACHER #
  SCREAMING_SNAKE_CASE       :      List[str]							=    teacher_model_class.from_pretrained(args.teacher_name						,     output_hidden_states=lowercase				)
  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(lowercase						,     lowercase				)
  if args.freeze_token_type_embds:
    freeze_token_type_embeddings(lowercase						,     lowercase				)

  # 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()
  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    Distiller(
      params=lowercase						,     dataset=lowercase						,     token_probs=lowercase						,     student=lowercase						,     teacher=lowercase				)
  distiller.train()
  logger.info("Let's go get some drinks."				)


if __name__ == "__main__":
   main()














# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		torch.FloatTensor
     UpperCamelCase_							:		Optional[torch.FloatTensor]			=	None





def   lowerCamelCase__			(     lowercase						,     lowercase=0.999						,     lowercase="cosine"						,     ):

  """simple docstring"""






  if alpha_transform_type == "cosine":

    def alpha_bar_fn(lowercase				):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(lowercase				):
      return math.exp(t * -12.0				)

  else:
    raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''				)

  SCREAMING_SNAKE_CASE       :      Optional[int]							=    []
  for i in range(lowercase				):
    SCREAMING_SNAKE_CASE       :      str							=    i / num_diffusion_timesteps
    SCREAMING_SNAKE_CASE       :      List[str]							=    (i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(lowercase				) / alpha_bar_fn(lowercase				)						,     lowercase				)				)
  return torch.tensor(lowercase						,     dtype=torch.floataa				)


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		List[Any]			=	1


     @register_to_config
     def __init__(				self       :	Union[str, Any]					, UpperCAmelCase_       :	int = 1000					, UpperCAmelCase_       :	float = 0.0_001					, UpperCAmelCase_       :	float = 0.02					, UpperCAmelCase_       :	str = "linear"					, UpperCAmelCase_       :	Optional[Union[np.ndarray, List[float]]] = None					, UpperCAmelCase_       :	bool = True					, UpperCAmelCase_       :	bool = True					, UpperCAmelCase_       :	int = 0					, UpperCAmelCase_       :	str = "epsilon"					, UpperCAmelCase_       :	float = 1.0					, **UpperCAmelCase_       :	Dict					, ):
       if kwargs.get("set_alpha_to_one"					, UpperCAmelCase_      ) is not None:
         SCREAMING_SNAKE_CASE       :      int							=    (
             "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
         )
         deprecate("set_alpha_to_one"					, "1.0.0"					, UpperCAmelCase_					, standard_warn=UpperCAmelCase_      )
         SCREAMING_SNAKE_CASE       :      int							=    kwargs["set_alpha_to_one"]
       if trained_betas is not None:
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    torch.tensor(UpperCAmelCase_					, dtype=torch.floataa      )
       elif beta_schedule == "linear":
         SCREAMING_SNAKE_CASE       :      List[str]							=    torch.linspace(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, dtype=torch.floataa      )
       elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    (
             torch.linspace(beta_start**0.5					, beta_end**0.5					, UpperCAmelCase_					, dtype=torch.floataa      ) ** 2
         )
       elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         SCREAMING_SNAKE_CASE       :      int							=    betas_for_alpha_bar(UpperCAmelCase_      )
       else:
         raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''      )

       SCREAMING_SNAKE_CASE       :      Tuple							=    1.0 - self.betas
       SCREAMING_SNAKE_CASE       :      Any							=    torch.cumprod(self.alphas					, dim=0      )

       # At every step in inverted ddim, we are looking into the next alphas_cumprod
       # For the final step, there is no next alphas_cumprod, and the index is out of bounds
       # `set_alpha_to_zero` decides whether we set this parameter simply to zero
       # in this case, self.step() just output the predicted noise
       # or whether we use the final alpha of the "non-previous" one.
       SCREAMING_SNAKE_CASE       :      Dict							=    torch.tensor(0.0      ) if set_alpha_to_zero else self.alphas_cumprod[-1]

       # standard deviation of the initial noise distribution
       SCREAMING_SNAKE_CASE       :      List[str]							=    1.0

       # setable values
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    None
       SCREAMING_SNAKE_CASE       :      List[str]							=    torch.from_numpy(np.arange(0					, UpperCAmelCase_      ).copy().astype(np.intaa      )      )


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	torch.FloatTensor					, UpperCAmelCase_       :	Optional[int] = None      ):
       return sample


     def 				_A       (				self       :	List[Any]					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	Union[str, torch.device] = None      ):

       if num_inference_steps > self.config.num_train_timesteps:
         raise ValueError(
             f'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
             f''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
             f''' maximal {self.config.num_train_timesteps} timesteps.'''      )

       SCREAMING_SNAKE_CASE       :      Dict							=    num_inference_steps
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.config.num_train_timesteps // self.num_inference_steps
       # creates integer timesteps by multiplying by ratio
       # casting to int to avoid issues when num_inference_step is power of 3
       SCREAMING_SNAKE_CASE       :      str							=    (np.arange(0					, UpperCAmelCase_      ) * step_ratio).round().copy().astype(np.intaa      )
       SCREAMING_SNAKE_CASE       :      str							=    torch.from_numpy(UpperCAmelCase_      ).to(UpperCAmelCase_      )
       self.timesteps += self.config.steps_offset


     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	torch.FloatTensor					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	torch.FloatTensor					, UpperCAmelCase_       :	float = 0.0					, UpperCAmelCase_       :	bool = False					, UpperCAmelCase_       :	Optional[torch.FloatTensor] = None					, UpperCAmelCase_       :	bool = True					, ):
       # 1. get previous step value (=t+1)
       SCREAMING_SNAKE_CASE       :      Tuple							=    timestep + self.config.num_train_timesteps // self.num_inference_steps

       # 2. compute alphas, betas
       # change original implementation to exactly match noise levels for analogous forward process
       SCREAMING_SNAKE_CASE       :      List[str]							=    self.alphas_cumprod[timestep]
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    (
           self.alphas_cumprod[prev_timestep]
           if prev_timestep < self.config.num_train_timesteps
           else self.final_alpha_cumprod
       )

       SCREAMING_SNAKE_CASE       :      Tuple							=    1 - alpha_prod_t

       # 3. compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       if self.config.prediction_type == "epsilon":
         SCREAMING_SNAKE_CASE       :      Any							=    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         SCREAMING_SNAKE_CASE       :      List[Any]							=    model_output
       elif self.config.prediction_type == "sample":
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    model_output
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
       elif self.config.prediction_type == "v_prediction":
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
         SCREAMING_SNAKE_CASE       :      Tuple							=    (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
       else:
         raise ValueError(
             f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
             " `v_prediction`"      )

       # 4. Clip or threshold "predicted x_0"
       if self.config.clip_sample:
         SCREAMING_SNAKE_CASE       :      List[str]							=    pred_original_sample.clamp(
             -self.config.clip_sample_range					, self.config.clip_sample_range      )

       # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       SCREAMING_SNAKE_CASE       :      Dict							=    (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

       # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       SCREAMING_SNAKE_CASE       :      str							=    alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

       if not return_dict:
         return (prev_sample, pred_original_sample)
       return DDIMSchedulerOutput(prev_sample=UpperCAmelCase_					, pred_original_sample=UpperCAmelCase_      )






     def __len__(				self       :	Dict      ):
       return self.config.num_train_timesteps






