


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







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   Union[str, Any]:
  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__)       ->   str:
  if args.student_type == "roberta":
    __snake_case:						Optional[Any]			  =						False
  elif args.student_type == "gpt2":
    __snake_case:						str			  =						False







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)       ->   List[str]:
  if args.student_type == "roberta":
    __snake_case:						Optional[int]			  =						False







def       A__							(   )       ->   Tuple:
  __snake_case:						Optional[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=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=0.15 , 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=0.05 , 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=0.02 , 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.""")
  __snake_case:						List[Any]			  =						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)

  __snake_case:						str			  =						MODEL_CLASSES[args.student_type]
  __snake_case:						Union[str, Any]			  =						MODEL_CLASSES[args.teacher_type]

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

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

  __snake_case:						Union[str, Any]			  =						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}''')
  __snake_case:						Tuple			  =						student_config_class.from_pretrained(args.student_config)
  __snake_case:						List[str]			  =						True

  if args.student_pretrained_weights is not None:
    logger.info(F'''Loading pretrained weights from {args.student_pretrained_weights}''')
    __snake_case:						Optional[int]			  =						student_model_class.from_pretrained(args.student_pretrained_weights , config=SCREAMING_SNAKE_CASE__)
  else:
    __snake_case:						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 #
  __snake_case:						Optional[int]			  =						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()
  __snake_case:						List[str]			  =						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()













from typing import TYPE_CHECKING

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


__UpperCAmelCase	:       List[str]    	=							{
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Optional[Any]    	=							["RobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Tuple    	=							[
          "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaForCausalLM",
          "RobertaForMaskedLM",
          "RobertaForMultipleChoice",
          "RobertaForQuestionAnswering",
          "RobertaForSequenceClassification",
          "RobertaForTokenClassification",
          "RobertaModel",
          "RobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Optional[int]    	=							[
          "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaForCausalLM",
          "TFRobertaForMaskedLM",
          "TFRobertaForMultipleChoice",
          "TFRobertaForQuestionAnswering",
          "TFRobertaForSequenceClassification",
          "TFRobertaForTokenClassification",
          "TFRobertaMainLayer",
          "TFRobertaModel",
          "TFRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       List[Any]    	=							[
          "FlaxRobertaForCausalLM",
          "FlaxRobertaForMaskedLM",
          "FlaxRobertaForMultipleChoice",
          "FlaxRobertaForQuestionAnswering",
          "FlaxRobertaForSequenceClassification",
          "FlaxRobertaForTokenClassification",
          "FlaxRobertaModel",
          "FlaxRobertaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

      __UpperCAmelCase	:       str    	=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






