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


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



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Tuple	)							->					Optional[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 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       List[str] ,     __UpperCamelCase    :       List[str]	)							->					Tuple:







  """simple docstring"""





  if args.student_type == "roberta":
    SCREAMING_SNAKE_CASE__										=      False
  elif args.student_type == "gpt2":
    SCREAMING_SNAKE_CASE__										=      False



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       Union[str, Any]	)							->					Tuple:







  """simple docstring"""





  if args.student_type == "roberta":
    SCREAMING_SNAKE_CASE__										=      False



def 	__SCREAMING_SNAKE_CASE  (					)							->					str:







  """simple docstring"""





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

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

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

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

  parser.add_argument("""--log_interval""" ,     type=__UpperCamelCase ,     default=5_00 ,     help="""Tensorboard logging interval."""	)
  parser.add_argument("""--checkpoint_interval""" ,     type=__UpperCamelCase ,     default=40_00 ,     help="""Checkpoint interval."""	)
  SCREAMING_SNAKE_CASE__										=      parser.parse_args()
  sanity_checks(__UpperCamelCase	)

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

  SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      MODEL_CLASSES[args.student_type]
  SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      MODEL_CLASSES[args.teacher_type]

  # TOKENIZER #
  SCREAMING_SNAKE_CASE__										=      teacher_tokenizer_class.from_pretrained(args.teacher_name	)
  SCREAMING_SNAKE_CASE__										=      {}
  for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
    SCREAMING_SNAKE_CASE__										=      tokenizer.all_special_tokens.index(__UpperCamelCase	)
    SCREAMING_SNAKE_CASE__										=      tokenizer.all_special_ids[idx]
  logger.info(f"""Special tokens {special_tok_ids}"""	)
  SCREAMING_SNAKE_CASE__										=      special_tok_ids
  SCREAMING_SNAKE_CASE__										=      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__										=      pickle.load(__UpperCamelCase	)

  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__										=      pickle.load(__UpperCamelCase	)

    SCREAMING_SNAKE_CASE__										=      np.maximum(__UpperCamelCase ,     1	) ** -args.mlm_smoothing
    for idx in special_tok_ids.values():
      SCREAMING_SNAKE_CASE__										=      0.0  # do not predict special tokens
    SCREAMING_SNAKE_CASE__										=      torch.from_numpy(__UpperCamelCase	)
  else:
    SCREAMING_SNAKE_CASE__										=      None

  SCREAMING_SNAKE_CASE__										=      LmSeqsDataset(params=__UpperCamelCase ,     data=__UpperCamelCase	)
  logger.info("""Data loader created."""	)

  # STUDENT #
  logger.info(f"""Loading student config from {args.student_config}"""	)
  SCREAMING_SNAKE_CASE__										=      student_config_class.from_pretrained(args.student_config	)
  SCREAMING_SNAKE_CASE__										=      True

  if args.student_pretrained_weights is not None:
    logger.info(f"""Loading pretrained weights from {args.student_pretrained_weights}"""	)
    SCREAMING_SNAKE_CASE__										=      student_model_class.from_pretrained(args.student_pretrained_weights ,     config=__UpperCamelCase	)
  else:
    SCREAMING_SNAKE_CASE__										=      student_model_class(__UpperCamelCase	)

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

  # TEACHER #
  SCREAMING_SNAKE_CASE__										=      teacher_model_class.from_pretrained(args.teacher_name ,     output_hidden_states=__UpperCamelCase	)
  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(__UpperCamelCase ,     __UpperCamelCase	)
  if args.freeze_token_type_embds:
    freeze_token_type_embeddings(__UpperCamelCase ,     __UpperCamelCase	)

  # 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__										=      Distiller(
      params=__UpperCamelCase ,     dataset=__UpperCamelCase ,     token_probs=__UpperCamelCase ,     student=__UpperCamelCase ,     teacher=__UpperCamelCase	)
  distiller.train()
  logger.info("""Let's go get some drinks."""	)


if __name__ == "__main__":
   main()

__lowerCamelCase			: List[Any]							   =		6_5521



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str	)							->					int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      1
  SCREAMING_SNAKE_CASE__										=      0
  for plain_chr in plain_text:
    SCREAMING_SNAKE_CASE__										=      (a + ord(__UpperCamelCase	)) % MOD_ADLER
    SCREAMING_SNAKE_CASE__										=      (b + a) % MOD_ADLER
  return (b << 16) | a
