

'''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


__lowerCamelCase									=     {
    '''distilbert''': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    '''roberta''': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    '''bert''': (BertConfig, BertForMaskedLM, BertTokenizer),
    '''gpt2''': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      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    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      List[str]:
       if args.student_type == "roberta":
              A_						       =   False
       elif args.student_type == "gpt2":
              A_						       =   False
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      Optional[Any]:
       if args.student_type == "roberta":
              A_						       =   False
def    UpperCAmelCase__     (   )      ->      Optional[int]:
       A_						       =   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=5_00,							help="""Tensorboard logging interval."""					)
       parser.add_argument("""--checkpoint_interval""",							type=UpperCAmelCase__,							default=40_00,							help="""Checkpoint interval."""					)
       A_						       =   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					)

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

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

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

       A_						       =   LmSeqsDataset(params=UpperCAmelCase__,							data=UpperCAmelCase__					)
       logger.info("""Data loader created."""					)

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

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

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

       # TEACHER #
       A_						       =   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()
       A_						       =   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()



'''simple docstring'''







import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""",							[None, 4_00 * 2**20, 6_00 * 2**20]					)
@pytest.mark.parametrize("""input_in_memory_max_size""",							["""default""", 0, 1_00 * 2**20, 9_00 * 2**20]					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Dict:
       if input_in_memory_max_size != "default":
              monkeypatch.setattr(datasets.config,							"""IN_MEMORY_MAX_SIZE""",							UpperCAmelCase__					)
       A_						       =   datasets.config.IN_MEMORY_MAX_SIZE
       if input_in_memory_max_size == "default":
              assert in_memory_max_size == 0
       else:
              assert in_memory_max_size == input_in_memory_max_size
       if dataset_size and in_memory_max_size:
              A_						       =   dataset_size < in_memory_max_size
       else:
              A_						       =   False
       A_						       =   is_small_dataset(UpperCAmelCase__					)
       assert result == expected
