




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


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







def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Any				)  ->							Dict:
    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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						Any				)  ->							Any:





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







def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Any	,					__UpperCamelCase      :						Dict				)  ->							Tuple:





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







def  SCREAMING_SNAKE_CASE     (	)  ->							Union[str, Any]:
    UpperCAmelCase_										=					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=0.15	,					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=0.05	,					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=0.02	,					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.'''				)
    UpperCAmelCase_										=					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				)

    UpperCAmelCase_										=					MODEL_CLASSES[args.student_type]
    UpperCAmelCase_										=					MODEL_CLASSES[args.teacher_type]

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

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

    UpperCAmelCase_										=					LmSeqsDataset(params=A__	,					data=A__				)
    logger.info('''Data loader created.'''				)

    # STUDENT #
    logger.info(f'Loading student config from {args.student_config}'				)
    UpperCAmelCase_										=					student_config_class.from_pretrained(args.student_config				)
    UpperCAmelCase_										=					True

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

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

    # TEACHER #
    UpperCAmelCase_										=					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()
    UpperCAmelCase_										=					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()











def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Dict=2_8123				)  ->							str:
    UpperCAmelCase_										=					[1] * (limit + 1)

    for i in range(2	,					int(limit**0.5				) + 1				):
        sum_divs[i * i] += i
        for k in range(i + 1	,					limit // i + 1				):
            sum_divs[k * i] += k + i

    UpperCAmelCase_										=					set()
    UpperCAmelCase_										=					0

    for n in range(1	,					limit + 1				):
        if sum_divs[n] > n:
            abundants.add(__UpperCamelCase				)

        if not any((n - a in abundants) for a in abundants				):
            res += n

    return res


if __name__ == "__main__":
 print(solution())





