


'''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


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






def __UpperCamelCase   (			__lowerCamelCase		: int				)      ->     Any:







     '''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 __UpperCamelCase   (			__lowerCamelCase		: List[str]      ,  __lowerCamelCase		: Optional[Any]				)      ->     int:







     '''simple docstring'''






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






def __UpperCamelCase   (			__lowerCamelCase		: Optional[Any]      ,  __lowerCamelCase		: Dict				)      ->     Tuple:







     '''simple docstring'''






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






def __UpperCamelCase   (			)      ->     Optional[int]:







     '''simple docstring'''






     _a						=		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=500      ,  help="Tensorboard logging interval."				)
     parser.add_argument("--checkpoint_interval"      ,  type=_UpperCamelCase      ,  default=4000      ,  help="Checkpoint interval."				)
     _a						=		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				)

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

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

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

     _a						=		LmSeqsDataset(params=_UpperCamelCase      ,  data=_UpperCamelCase				)
     logger.info("Data loader created."				)

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

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

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

     # TEACHER #
     _a						=		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()
     _a						=		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()









'''simple docstring'''

lowercase__				     =		65_521






def __UpperCamelCase   (			__lowerCamelCase		: str				)      ->     int:







     '''simple docstring'''






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

