





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







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    int:
  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__)   ->    Union[str, Any]:
  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__)   ->    Union[str, Any]:
  if args.student_type == "roberta":
    __snake_case:						Optional[Any]   =						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:						Optional[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  ,   __snake_case  ,   __snake_case:						List[str]   =						MODEL_CLASSES[args.student_type]
  __snake_case  ,   __snake_case  ,   __snake_case:						List[str]   =						MODEL_CLASSES[args.teacher_type]

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

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

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

  if args.student_pretrained_weights is not None:
    logger.info(F'''Loading pretrained weights from {args.student_pretrained_weights}''')
    __snake_case:						List[Any]   =						student_model_class.from_pretrained(args.student_pretrained_weights					,					config=SCREAMING_SNAKE_CASE__)
  else:
    __snake_case:						str   =						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:						Union[str, Any]   =						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 dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput







@dataclass
class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     None
       lowerCAmelCase__      		=     None
       lowerCAmelCase__      		=     None
       lowerCAmelCase__      		=     None







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    Any							,				A :    List[str]=1							,				A :    str=0							,				A :    List[Any]=2							,				A :    Union[str, Any]=512							,				A :    Tuple="cls"							,				A :    Union[str, Any]=False							,				A :    Optional[Any]=True							,				**A :    Optional[int]							,				):
         super().__init__(pad_token_id=A							,				bos_token_id=A							,				eos_token_id=A							,				**A		)
         __snake_case:						str   =						project_dim
         __snake_case:						Optional[int]   =						pooler_fn
         __snake_case:						Dict   =						learn_encoder
         __snake_case:						str   =						use_attention_mask







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     [R"""pooler""", R"""logit_scale"""]
       lowerCAmelCase__      		=     [R"""position_ids""", R"""predictions.decoder.bias"""]
       lowerCAmelCase__      		=     """roberta"""
       lowerCAmelCase__      		=     RobertaSeriesConfig




       def __init__(  self :    Dict							,				A :    Dict		):
         super().__init__(A		)
         __snake_case:						Optional[Any]   =						XLMRobertaModel(A		)
         __snake_case:						List[Any]   =						nn.Linear(config.hidden_size							,				config.project_dim		)
         __snake_case:						Optional[int]   =						getattr(A							,				"""has_pre_transformation"""							,				A		)
         if self.has_pre_transformation:
           __snake_case:						Optional[Any]   =						nn.Linear(config.hidden_size							,				config.project_dim		)
           __snake_case:						Optional[Any]   =						nn.LayerNorm(config.hidden_size							,				eps=config.layer_norm_eps		)
         self.post_init()






       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[bool] = None							,				A :    Optional[bool] = None							,				A :    Optional[bool] = None							,				):
         __snake_case:						Any   =						return_dict if return_dict is not None else self.config.use_return_dict

         __snake_case:						Optional[int]   =						self.base_model(
             input_ids=A							,				attention_mask=A							,				token_type_ids=A							,				position_ids=A							,				head_mask=A							,				inputs_embeds=A							,				encoder_hidden_states=A							,				encoder_attention_mask=A							,				output_attentions=A							,				output_hidden_states=True if self.has_pre_transformation else output_hidden_states							,				return_dict=A							,				)

         if self.has_pre_transformation:
           __snake_case:						int   =						outputs["""hidden_states"""][-2]
           __snake_case:						List[str]   =						self.pre_LN(A		)
           __snake_case:						List[str]   =						self.transformation_pre(A		)

           return TransformationModelOutput(
               projection_state=A							,				last_hidden_state=outputs.last_hidden_state							,				hidden_states=outputs.hidden_states							,				attentions=outputs.attentions							,				)
         else:
           __snake_case:						Optional[int]   =						self.transformation(outputs.last_hidden_state		)
           return TransformationModelOutput(
               projection_state=A							,				last_hidden_state=outputs.last_hidden_state							,				hidden_states=outputs.hidden_states							,				attentions=outputs.attentions							,				)






