





"""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


__A											=		{
    """distilbert""": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    """roberta""": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    """bert""": (BertConfig, BertForMaskedLM, BertTokenizer),
    """gpt2""": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def  __A   (_SCREAMING_SNAKE_CASE			)					->int:





  """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  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Optional[int]:





  """simple docstring"""


  if args.student_type == "roberta":
    lowerCAmelCase__			:str											=							False
  elif args.student_type == "gpt2":
    lowerCAmelCase__			:List[Any]											=							False
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Optional[int]:





  """simple docstring"""


  if args.student_type == "roberta":
    lowerCAmelCase__			:List[str]											=							False
def  __A   ()					->Optional[int]:





  """simple docstring"""


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

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

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

  parser.add_argument('--temperature'     ,   default=2.0     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Temperature for the softmax temperature.'			)
  parser.add_argument(
      '--alpha_ce'     ,   default=0.5     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Linear weight for the distillation loss. Must be >=0.'			)
  parser.add_argument(
      '--alpha_mlm'     ,   default=0.0     ,   type=_SCREAMING_SNAKE_CASE     ,   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=_SCREAMING_SNAKE_CASE     ,   help='Linear weight for the CLM loss. Must be >=0.'			)
  parser.add_argument('--alpha_mse'     ,   default=0.0     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Linear weight of the MSE loss. Must be >=0.'			)
  parser.add_argument(
      '--alpha_cos'     ,   default=0.0     ,   type=_SCREAMING_SNAKE_CASE     ,   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=_SCREAMING_SNAKE_CASE     ,   help='Proportion of tokens for which we need to make a prediction.'     ,   )
  parser.add_argument('--word_mask'     ,   default=0.8     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Proportion of tokens to mask out.'			)
  parser.add_argument('--word_keep'     ,   default=0.1     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Proportion of tokens to keep.'			)
  parser.add_argument('--word_rand'     ,   default=0.1     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Proportion of tokens to randomly replace.'			)
  parser.add_argument(
      '--mlm_smoothing'     ,   default=0.7     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'     ,   )
  parser.add_argument('--token_counts'     ,   type=_SCREAMING_SNAKE_CASE     ,   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=_SCREAMING_SNAKE_CASE     ,   default=3     ,   help='Number of pass on the whole dataset.'			)
  parser.add_argument('--batch_size'     ,   type=_SCREAMING_SNAKE_CASE     ,   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=_SCREAMING_SNAKE_CASE     ,   default=50     ,   help='Gradient accumulation for larger training batches.'     ,   )
  parser.add_argument('--warmup_prop'     ,   default=0.0_5     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Linear warmup proportion.'			)
  parser.add_argument('--weight_decay'     ,   default=0.0     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Weight decay if we apply some.'			)
  parser.add_argument('--learning_rate'     ,   default=5e-4     ,   type=_SCREAMING_SNAKE_CASE     ,   help='The initial learning rate for Adam.'			)
  parser.add_argument('--adam_epsilon'     ,   default=1e-6     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Epsilon for Adam optimizer.'			)
  parser.add_argument('--max_grad_norm'     ,   default=5.0     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Max gradient norm.'			)
  parser.add_argument('--initializer_range'     ,   default=0.0_2     ,   type=_SCREAMING_SNAKE_CASE     ,   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=_SCREAMING_SNAKE_CASE     ,   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=_SCREAMING_SNAKE_CASE     ,   default=1     ,   help='Number of GPUs in the node.'			)
  parser.add_argument('--local_rank'     ,   type=_SCREAMING_SNAKE_CASE     ,   default=-1     ,   help='Distributed training - Local rank'			)
  parser.add_argument('--seed'     ,   type=_SCREAMING_SNAKE_CASE     ,   default=56     ,   help='Random seed'			)

  parser.add_argument('--log_interval'     ,   type=_SCREAMING_SNAKE_CASE     ,   default=500     ,   help='Tensorboard logging interval.'			)
  parser.add_argument('--checkpoint_interval'     ,   type=_SCREAMING_SNAKE_CASE     ,   default=4000     ,   help='Checkpoint interval.'			)
  lowerCAmelCase__			:List[str]											=							parser.parse_args()
  sanity_checks(_SCREAMING_SNAKE_CASE			)

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

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

  # TOKENIZER #
  lowerCAmelCase__			:Optional[Any]											=							teacher_tokenizer_class.from_pretrained(args.teacher_name			)
  lowerCAmelCase__			:List[str]											=							{}
  for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
    lowerCAmelCase__			:Optional[Any]											=							tokenizer.all_special_tokens.index(_SCREAMING_SNAKE_CASE			)
    lowerCAmelCase__			:Any											=							tokenizer.all_special_ids[idx]
  logger.info(F"Special tokens {special_tok_ids}"			)
  lowerCAmelCase__			:Union[str, Any]											=							special_tok_ids
  lowerCAmelCase__			:str											=							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:
    lowerCAmelCase__			:Optional[Any]											=							pickle.load(_SCREAMING_SNAKE_CASE			)

  if args.mlm:
    logger.info(F"Loading token counts from {args.token_counts} (already pre-computed)"			)
    with open(args.token_counts     ,   'rb'			) as fp:
      lowerCAmelCase__			:Optional[int]											=							pickle.load(_SCREAMING_SNAKE_CASE			)

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

  lowerCAmelCase__			:Optional[int]											=							LmSeqsDataset(params=_SCREAMING_SNAKE_CASE     ,   data=_SCREAMING_SNAKE_CASE			)
  logger.info('Data loader created.'			)

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

  if args.student_pretrained_weights is not None:
    logger.info(F"Loading pretrained weights from {args.student_pretrained_weights}"			)
    lowerCAmelCase__			:int											=							student_model_class.from_pretrained(args.student_pretrained_weights     ,   config=_SCREAMING_SNAKE_CASE			)
  else:
    lowerCAmelCase__			:Union[str, Any]											=							student_model_class(_SCREAMING_SNAKE_CASE			)

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

  # TEACHER #
  lowerCAmelCase__			:Dict											=							teacher_model_class.from_pretrained(args.teacher_name     ,   output_hidden_states=_SCREAMING_SNAKE_CASE			)
  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(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
  if args.freeze_token_type_embds:
    freeze_token_type_embeddings(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)

  # 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()
  lowerCAmelCase__			:Dict											=							Distiller(
      params=_SCREAMING_SNAKE_CASE     ,   dataset=_SCREAMING_SNAKE_CASE     ,   token_probs=_SCREAMING_SNAKE_CASE     ,   student=_SCREAMING_SNAKE_CASE     ,   teacher=_SCREAMING_SNAKE_CASE			)
  distiller.train()
  logger.info('Let\'s go get some drinks.'			)


if __name__ == "__main__":
  main()







"""simple docstring"""





from __future__ import annotations

__A											=		1.6_021e-19  # units = C
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   )					->tuple[str, float]:





  """simple docstring"""


  if (conductivity, electron_conc, mobility).count(0			) != 1:
    raise ValueError('You cannot supply more or less than 2 values'			)
  elif conductivity < 0:
    raise ValueError('Conductivity cannot be negative'			)
  elif electron_conc < 0:
    raise ValueError('Electron concentration cannot be negative'			)
  elif mobility < 0:
    raise ValueError('mobility cannot be negative'			)
  elif conductivity == 0:
    return (
        "conductivity",
        mobility * electron_conc * ELECTRON_CHARGE,
    )
  elif electron_conc == 0:
    return (
        "electron_conc",
        conductivity / (mobility * ELECTRON_CHARGE),
    )
  else:
    return (
        "mobility",
        conductivity / (electron_conc * ELECTRON_CHARGE),
    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
