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







def 						A__  (					__A	):







    '''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__  (					__A   , __A	):







    '''simple docstring'''
    if args.student_type == "roberta":
        _lowerCamelCase     :		Tuple 						=						False
    elif args.student_type == "gpt2":
        _lowerCamelCase     :		Union[str, Any] 						=						False







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    if args.student_type == "roberta":
        _lowerCamelCase     :		Dict 						=						False







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		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=__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=4_000   , help="""Checkpoint interval."""	)
    _lowerCamelCase     :		Dict 						=						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	)

    _lowerCamelCase     :		List[str] 						=						MODEL_CLASSES[args.student_type]
    _lowerCamelCase     :		int 						=						MODEL_CLASSES[args.teacher_type]

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

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

    _lowerCamelCase     :		Tuple 						=						LmSeqsDataset(params=__A   , data=__A	)
    logger.info("""Data loader created."""	)

    # STUDENT #
    logger.info(F"""Loading student config from {args.student_config}"""	)
    _lowerCamelCase     :		Dict 						=						student_config_class.from_pretrained(args.student_config	)
    _lowerCamelCase     :		Optional[int] 						=						True

    if args.student_pretrained_weights is not None:
        logger.info(F"""Loading pretrained weights from {args.student_pretrained_weights}"""	)
        _lowerCamelCase     :		Any 						=						student_model_class.from_pretrained(args.student_pretrained_weights   , config=__A	)
    else:
        _lowerCamelCase     :		Dict 						=						student_model_class(__A	)

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

    # TEACHER #
    _lowerCamelCase     :		Optional[Any] 						=						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()
    _lowerCamelCase     :		Optional[Any] 						=						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()





from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      def __init__(					self     :				Dict    ,  _UpperCamelCase     :				NestedDataStructureLike[PathLike]    ,  _UpperCamelCase     :				Optional[NamedSplit] = None    ,  _UpperCamelCase     :				Optional[Features] = None    ,  _UpperCamelCase     :				str = None    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				Optional[int] = None    ,  **_UpperCamelCase     :				Tuple    ,  )				->Union[str, Any]:


          """simple docstring"""


          super().__init__(
              _UpperCamelCase    ,  split=_UpperCamelCase    ,  features=_UpperCamelCase    ,  cache_dir=_UpperCamelCase    ,  keep_in_memory=_UpperCamelCase    ,  streaming=_UpperCamelCase    ,  num_proc=_UpperCamelCase    ,  **_UpperCamelCase    ,  )
          _lowerCamelCase     :		List[Any] 						=						path_or_paths if isinstance(_UpperCamelCase    ,  _UpperCamelCase) else {self.split: path_or_paths}
          _lowerCamelCase     :		Any 						=						Text(
              cache_dir=_UpperCamelCase    ,  data_files=_UpperCamelCase    ,  features=_UpperCamelCase    ,  **_UpperCamelCase    ,  )




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->Optional[Any]:


          """simple docstring"""


          if self.streaming:
              _lowerCamelCase     :		Tuple 						=						self.builder.as_streaming_dataset(split=self.split)
          # Build regular (map-style) dataset
          else:
              _lowerCamelCase     :		List[Any] 						=						None
              _lowerCamelCase     :		Any 						=						None
              _lowerCamelCase     :		List[str] 						=						None
              _lowerCamelCase     :		Dict 						=						None

              self.builder.download_and_prepare(
                  download_config=_UpperCamelCase    ,  download_mode=_UpperCamelCase    ,  verification_mode=_UpperCamelCase    ,  base_path=_UpperCamelCase    ,  num_proc=self.num_proc    ,  )
              _lowerCamelCase     :		Optional[int] 						=						self.builder.as_dataset(
                  split=self.split    ,  verification_mode=_UpperCamelCase    ,  in_memory=self.keep_in_memory)
          return dataset




