





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







def 					lowerCAmelCase__(		lowercase				:			Optional[int] )       ->			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 					lowerCAmelCase__(		lowercase				:			int      ,   lowercase				:			int )       ->			List[str]:





  if args.student_type == "roberta":
    __snake_case      : int						=				False
  elif args.student_type == "gpt2":
    __snake_case      : Optional[int]						=				False







def 					lowerCAmelCase__(		lowercase				:			str      ,   lowercase				:			Union[str, Any] )       ->			Tuple:





  if args.student_type == "roberta":
    __snake_case      : str						=				False







def 					lowerCAmelCase__(		)       ->			int:
  __snake_case      : int						=				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=__a      ,   required=__a      ,   help="The output directory (log, checkpoints, parameters, etc.)" )
  parser.add_argument(
      "--data_file"      ,   type=__a      ,   required=__a      ,   help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence."      ,   )

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

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

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

  parser.add_argument("--log_interval"      ,   type=__a      ,   default=500      ,   help="Tensorboard logging interval." )
  parser.add_argument("--checkpoint_interval"      ,   type=__a      ,   default=4000      ,   help="Checkpoint interval." )
  __snake_case      : int						=				parser.parse_args()
  sanity_checks(__a )

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

  __snake_case      : int						=				MODEL_CLASSES[args.student_type]
  __snake_case      : Tuple						=				MODEL_CLASSES[args.teacher_type]

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

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

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

  __snake_case      : Tuple						=				LmSeqsDataset(params=__a      ,   data=__a )
  logger.info("Data loader created." )

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

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

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

  # TEACHER #
  __snake_case      : Tuple						=				teacher_model_class.from_pretrained(args.teacher_name      ,   output_hidden_states=__a )
  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(__a      ,   __a )
  if args.freeze_token_type_embds:
    freeze_token_type_embeddings(__a      ,   __a )

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


if __name__ == "__main__":
     main()








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A									=				{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				["DistilBertTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DistilBertForMaskedLM",
         "DistilBertForMultipleChoice",
         "DistilBertForQuestionAnswering",
         "DistilBertForSequenceClassification",
         "DistilBertForTokenClassification",
         "DistilBertModel",
         "DistilBertPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFDistilBertForMaskedLM",
         "TFDistilBertForMultipleChoice",
         "TFDistilBertForQuestionAnswering",
         "TFDistilBertForSequenceClassification",
         "TFDistilBertForTokenClassification",
         "TFDistilBertMainLayer",
         "TFDistilBertModel",
         "TFDistilBertPreTrainedModel",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "FlaxDistilBertForMaskedLM",
         "FlaxDistilBertForMultipleChoice",
         "FlaxDistilBertForQuestionAnswering",
         "FlaxDistilBertForSequenceClassification",
         "FlaxDistilBertForTokenClassification",
         "FlaxDistilBertModel",
         "FlaxDistilBertPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_distilbert import (
         DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         DistilBertConfig,
         DistilBertOnnxConfig,
     )
     from .tokenization_distilbert import DistilBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_distilbert_fast import DistilBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_distilbert import (
              DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DistilBertForMaskedLM,
              DistilBertForMultipleChoice,
              DistilBertForQuestionAnswering,
              DistilBertForSequenceClassification,
              DistilBertForTokenClassification,
              DistilBertModel,
              DistilBertPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_distilbert import (
              TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFDistilBertForMaskedLM,
              TFDistilBertForMultipleChoice,
              TFDistilBertForQuestionAnswering,
              TFDistilBertForSequenceClassification,
              TFDistilBertForTokenClassification,
              TFDistilBertMainLayer,
              TFDistilBertModel,
              TFDistilBertPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_distilbert import (
              FlaxDistilBertForMaskedLM,
              FlaxDistilBertForMultipleChoice,
              FlaxDistilBertForQuestionAnswering,
              FlaxDistilBertForSequenceClassification,
              FlaxDistilBertForTokenClassification,
              FlaxDistilBertModel,
              FlaxDistilBertPreTrainedModel,
          )

else:
     import sys

     __A									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

