


'''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


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

def    lowercase_	(	_A    :					Union[str, 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    lowercase_	(	_A    :					Optional[Any]					,      _A    :					str		):

      """simple docstring"""




      if args.student_type == "roberta":
            lowerCamelCase__	:       List[Any]					=						False
      elif args.student_type == "gpt2":
            lowerCamelCase__	:       Optional[int]					=						False

def    lowercase_	(	_A    :					List[Any]					,      _A    :					str		):

      """simple docstring"""




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

def    lowercase_	(	):

      """simple docstring"""




      lowerCamelCase__	:       Optional[Any]					=						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."		)
      lowerCamelCase__	:       Union[str, Any]					=						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		)

      lowerCamelCase__	:       str					=						MODEL_CLASSES[args.student_type]
      lowerCamelCase__	:       str					=						MODEL_CLASSES[args.teacher_type]

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

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

      lowerCamelCase__	:       int					=						LmSeqsDataset(params=UpperCamelCase__					,      data=UpperCamelCase__		)
      logger.info("Data loader created."		)

      # STUDENT #
      logger.info(F"Loading student config from {args.student_config}"		)
      lowerCamelCase__	:       List[Any]					=						student_config_class.from_pretrained(args.student_config		)
      lowerCamelCase__	:       Union[str, Any]					=						True

      if args.student_pretrained_weights is not None:
            logger.info(F"Loading pretrained weights from {args.student_pretrained_weights}"		)
            lowerCamelCase__	:       str					=						student_model_class.from_pretrained(args.student_pretrained_weights					,      config=UpperCamelCase__		)
      else:
            lowerCamelCase__	:       Optional[Any]					=						student_model_class(UpperCamelCase__		)

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

      # TEACHER #
      lowerCamelCase__	:       str					=						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()
      lowerCamelCase__	:       Dict					=						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()






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A   :					Dict	=      logging.get_logger(__name__)

A   :					Union[str, Any]	=      {
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "roberta"





       def __init__(     self							:    int      ,   __lowerCamelCase							:    Dict=50265      ,   __lowerCamelCase							:    Optional[int]=768      ,   __lowerCamelCase							:    Optional[Any]=12      ,   __lowerCamelCase							:    Optional[int]=12      ,   __lowerCamelCase							:    int=3072      ,   __lowerCamelCase							:    Dict="gelu"      ,   __lowerCamelCase							:    Union[str, Any]=0.1      ,   __lowerCamelCase							:    int=0.1      ,   __lowerCamelCase							:    Tuple=512      ,   __lowerCamelCase							:    List[str]=2      ,   __lowerCamelCase							:    Any=0.0_2      ,   __lowerCamelCase							:    Optional[int]=1E-1_2      ,   __lowerCamelCase							:    List[Any]=1      ,   __lowerCamelCase							:    int=0      ,   __lowerCamelCase							:    Any=2      ,   __lowerCamelCase							:    Tuple="absolute"      ,   __lowerCamelCase							:    Tuple=True      ,   __lowerCamelCase							:    str=None      ,   **__lowerCamelCase							:    Optional[Any]      ,   ):


             '''simple docstring'''



             super().__init__(pad_token_id=__lowerCamelCase      ,   bos_token_id=__lowerCamelCase      ,   eos_token_id=__lowerCamelCase      ,   **__lowerCamelCase )

             lowerCamelCase__	:       List[Any]					=						vocab_size
             lowerCamelCase__	:       str					=						hidden_size
             lowerCamelCase__	:       int					=						num_hidden_layers
             lowerCamelCase__	:       Optional[Any]					=						num_attention_heads
             lowerCamelCase__	:       Optional[Any]					=						hidden_act
             lowerCamelCase__	:       Any					=						intermediate_size
             lowerCamelCase__	:       Tuple					=						hidden_dropout_prob
             lowerCamelCase__	:       Any					=						attention_probs_dropout_prob
             lowerCamelCase__	:       Tuple					=						max_position_embeddings
             lowerCamelCase__	:       int					=						type_vocab_size
             lowerCamelCase__	:       Any					=						initializer_range
             lowerCamelCase__	:       Dict					=						layer_norm_eps
             lowerCamelCase__	:       int					=						position_embedding_type
             lowerCamelCase__	:       Any					=						use_cache
             lowerCamelCase__	:       int					=						classifier_dropout







class     _lowercase				(							lowercase__):


       """simple docstring"""




       @property
       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             if self.task == "multiple-choice":
                   lowerCamelCase__	:       int					=						{0: "batch", 1: "choice", 2: "sequence"}
             else:
                   lowerCamelCase__	:       Optional[Any]					=						{0: "batch", 1: "sequence"}
             return OrderedDict(
                 [
                     ("input_ids", dynamic_axis),
                     ("attention_mask", dynamic_axis),
                 ] )


