





'''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


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


def 	__a(SCREAMING_SNAKE_CASE_  :					Optional[int]    ):



    '''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 	__a(SCREAMING_SNAKE_CASE_  :					Optional[int]		,       SCREAMING_SNAKE_CASE_  :					Optional[int]    ):



    '''simple docstring'''





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


def 	__a(SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					List[str]    ):



    '''simple docstring'''





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


def 	__a():



    '''simple docstring'''





    _lowerCAmelCase				=      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."    )
    _lowerCAmelCase				=      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    )

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

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

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

    _lowerCAmelCase				=      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}'''    )
    _lowerCAmelCase				=      student_config_class.from_pretrained(args.student_config    )
    _lowerCAmelCase				=      True

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

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

    # TEACHER #
    _lowerCAmelCase				=      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()
    _lowerCAmelCase				=      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()









'''simple docstring'''


import math


def 	__a(SCREAMING_SNAKE_CASE_  :					int = 100    ):



    '''simple docstring'''





    _lowerCAmelCase				=      sum(i * i for i in range(1		,       n + 1    )    )
    _lowerCAmelCase				=      int(math.pow(sum(range(1		,       n + 1    )    )		,       2    )    )
    return square_of_sum - sum_of_squares


if __name__ == "__main__":
       print(f'''{solution() = }''')


