
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


__lowerCAmelCase			:     Union[str, Any]           =   logging.get_logger(__name__)

__lowerCAmelCase			:     List[str]           =   {'vocab_file': 'spiece.model'}

__lowerCAmelCase			:     List[Any]           =   {
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    }
}




class        UpperCAmelCase_			( snake_case_     ):


  '''simple docstring'''







  def __init__( self			: Optional[Any]			,							UpperCamelCase__			: Dict			,							UpperCamelCase__			: List[str]=False			,							UpperCamelCase__			: List[str]=True			,							UpperCamelCase__			: List[Any]=False			,							UpperCamelCase__			: List[str]="<s>"			,							UpperCamelCase__			: Dict="</s>"			,							UpperCamelCase__			: str="<unk>"			,							UpperCamelCase__			: Dict="<sep>"			,							UpperCamelCase__			: int="<pad>"			,							UpperCamelCase__			: Optional[Any]="<cls>"			,							UpperCamelCase__			: Optional[Any]="<mask>"			,							UpperCamelCase__			: Optional[int]=["<eop>", "<eod>"]			,							UpperCamelCase__			: Optional[int] = None			,							**UpperCamelCase__			: Union[str, Any]			,							)			->	None:







    """simple docstring"""






    __magic_name__				     =       AddedToken(UpperCamelCase__			,							lstrip=UpperCamelCase__			,							rstrip=UpperCamelCase__    ) if isinstance(UpperCamelCase__			,							UpperCamelCase__    ) else mask_token

    __magic_name__				     =       {} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        do_lower_case=UpperCamelCase__			,							remove_space=UpperCamelCase__			,							keep_accents=UpperCamelCase__			,							bos_token=UpperCamelCase__			,							eos_token=UpperCamelCase__			,							unk_token=UpperCamelCase__			,							sep_token=UpperCamelCase__			,							pad_token=UpperCamelCase__			,							cls_token=UpperCamelCase__			,							mask_token=UpperCamelCase__			,							additional_special_tokens=UpperCamelCase__			,							sp_model_kwargs=self.sp_model_kwargs			,							**UpperCamelCase__			,							)

    __magic_name__				     =       3

    __magic_name__				     =       do_lower_case
    __magic_name__				     =       remove_space
    __magic_name__				     =       keep_accents
    __magic_name__				     =       vocab_file

    __magic_name__				     =       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
    self.sp_model.Load(UpperCamelCase__    )

    try:
      import jieba
    except ModuleNotFoundError as error:
      raise error.__class__(
          """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
          """See https://pypi.org/project/jieba/ for installation."""    )
    __magic_name__				     =       jieba
    __magic_name__				     =       str.maketrans(""" \n"""			,							"""\u2582\u2583"""    )
  @property
  # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
  def 							_lowercase							( self			: Union[str, Any]    )			->	Tuple:







    """simple docstring"""






    return len(self.sp_model    )
  def 							_lowercase							( self			: Dict    )			->	str:







    """simple docstring"""






    __magic_name__				     =       {self.convert_ids_to_tokens(UpperCamelCase__    ): i for i in range(self.vocab_size    )}
    vocab.update(self.added_tokens_encoder    )
    return vocab
  def __getstate__( self			: Any    )			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__				     =       self.__dict__.copy()
    __magic_name__				     =       None
    return state
  def __setstate__( self			: Union[str, Any]			,							UpperCamelCase__			: str    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       d

    # for backward compatibility
    if not hasattr(self			,							"""sp_model_kwargs"""    ):
      __magic_name__				     =       {}

    __magic_name__				     =       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
    self.sp_model.Load(self.vocab_file    )
  def 							_lowercase							( self			: List[Any]			,							UpperCamelCase__			: Any    )			->	Any:







    """simple docstring"""






    if self.remove_space:
      __magic_name__				     =       " ".join(inputs.strip().split()    )
    else:
      __magic_name__				     =       inputs
    __magic_name__				     =       outputs.replace("""``"""			,							"""\""""    ).replace("""''"""			,							"""\""""    )

    if not self.keep_accents:
      __magic_name__				     =       unicodedata.normalize("""NFKD"""			,							UpperCamelCase__    )
      __magic_name__				     =       "".join([c for c in outputs if not unicodedata.combining(UpperCamelCase__    )]    )
    if self.do_lower_case:
      __magic_name__				     =       outputs.lower()

    return outputs
  def 							_lowercase							( self			: List[Any]			,							UpperCamelCase__			: Tuple    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       self.preprocess_text(UpperCamelCase__    )
    __magic_name__				     =       self.sp_model.encode(UpperCamelCase__			,							out_type=UpperCamelCase__    )
    __magic_name__				     =       []
    for piece in pieces:
      if len(UpperCamelCase__    ) > 1 and piece[-1] == str(""","""    ) and piece[-2].isdigit():
        __magic_name__				     =       self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCamelCase__			,							""""""    )    )
        if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
          if len(cur_pieces[0]    ) == 1:
            __magic_name__				     =       cur_pieces[1:]
          else:
            __magic_name__				     =       cur_pieces[0][1:]
        cur_pieces.append(piece[-1]    )
        new_pieces.extend(UpperCamelCase__    )
      else:
        new_pieces.append(UpperCamelCase__    )

    return new_pieces
  def 							_lowercase							( self			: Optional[int]			,							UpperCamelCase__			: int    )			->	int:







    """simple docstring"""






    return self.sp_model.PieceToId(UpperCamelCase__    )
  def 							_lowercase							( self			: Optional[Any]			,							UpperCamelCase__			: List[str]    )			->	Tuple:







    """simple docstring"""






    return self.sp_model.IdToPiece(UpperCamelCase__    )
  def 							_lowercase							( self			: Optional[int]			,							UpperCamelCase__			: Optional[Any]    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       "".join(UpperCamelCase__    ).replace(UpperCamelCase__			,							""" """    ).strip()
    return out_string
  def 							_lowercase							( self			: Optional[int]			,							UpperCamelCase__			: str			,							UpperCamelCase__			: Optional[Any] = None    )			->	List[int]:







    """simple docstring"""






    __magic_name__				     =       [self.sep_token_id]
    __magic_name__				     =       [self.cls_token_id]
    if token_ids_a is None:
      return token_ids_a + sep + cls
    return token_ids_a + sep + token_ids_a + sep + cls
  def 							_lowercase							( self			: Optional[Any]			,							UpperCamelCase__			: List[Any]			,							UpperCamelCase__			: Any = None			,							UpperCamelCase__			: Optional[Any] = False    )			->	List[int]:







    """simple docstring"""






    if already_has_special_tokens:
      return super().get_special_tokens_mask(
          token_ids_a=UpperCamelCase__			,							token_ids_a=UpperCamelCase__			,							already_has_special_tokens=UpperCamelCase__    )

    if token_ids_a is not None:
      return ([0] * len(UpperCamelCase__    )) + [1] + ([0] * len(UpperCamelCase__    )) + [1, 1]
    return ([0] * len(UpperCamelCase__    )) + [1, 1]
  def 							_lowercase							( self			: Union[str, Any]			,							UpperCamelCase__			: str			,							UpperCamelCase__			: List[str] = None    )			->	List[int]:







    """simple docstring"""






    __magic_name__				     =       [self.sep_token_id]
    __magic_name__				     =       [2]

    if token_ids_a is None:
      return len(token_ids_a + sep    ) * [0] + cls_segment_id
    return len(token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1] + cls_segment_id
  def 							_lowercase							( self			: Union[str, Any]			,							UpperCamelCase__			: str			,							UpperCamelCase__			: int = None    )			->	Tuple[str]:







    """simple docstring"""






    if not os.path.isdir(UpperCamelCase__    ):
      logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
      return
    __magic_name__				     =       os.path.join(
        UpperCamelCase__			,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )

    if os.path.abspath(self.vocab_file    ) != os.path.abspath(UpperCamelCase__    ) and os.path.isfile(self.vocab_file    ):
      copyfile(self.vocab_file			,							UpperCamelCase__    )
    elif not os.path.isfile(self.vocab_file    ):
      with open(UpperCamelCase__			,							"""wb"""    ) as fi:
        __magic_name__				     =       self.sp_model.serialized_model_proto()
        fi.write(UpperCamelCase__    )

    return (out_vocab_file,)





  def 							_lowercase							( self			: str			,							*UpperCamelCase__			: Tuple			,							**UpperCamelCase__			: Union[str, Any]    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       super()._decode(*UpperCamelCase__			,							**UpperCamelCase__    )
    __magic_name__				     =       text.replace(""" """			,							""""""    ).replace("""\u2582"""			,							""" """    ).replace("""\u2583"""			,							"""\n"""    )
    return text





"""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


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







def   __lowerCAmelCase						(						lowercase  :  Any )	->						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   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  Dict )	->						Tuple:


    """simple docstring"""





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







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]						,							lowercase  :  Optional[int] )	->						Optional[Any]:


    """simple docstring"""





    if args.student_type == "roberta":
        snake_case    :      Optional[Any]      					=    False







def   __lowerCAmelCase						(						)	->						int:


    """simple docstring"""





    snake_case    :      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=lowercase						,							required=lowercase						,							help="The output directory (log, checkpoints, parameters, etc.)" )
    parser.add_argument(
        "--data_file"						,							type=lowercase						,							required=lowercase						,							help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence."						,							)

    parser.add_argument(
        "--student_type"						,							type=lowercase						,							choices=["distilbert", "roberta", "gpt2"]						,							required=lowercase						,							help="The student type (DistilBERT, RoBERTa)."						,							)
    parser.add_argument("--student_config"						,							type=lowercase						,							required=lowercase						,							help="Path to the student configuration." )
    parser.add_argument(
        "--student_pretrained_weights"						,							default=lowercase						,							type=lowercase						,							help="Load student initialization checkpoint." )

    parser.add_argument(
        "--teacher_type"						,							choices=["bert", "roberta", "gpt2"]						,							required=lowercase						,							help="Teacher type (BERT, RoBERTa)." )
    parser.add_argument("--teacher_name"						,							type=lowercase						,							required=lowercase						,							help="The teacher model." )

    parser.add_argument("--temperature"						,							default=2.0						,							type=lowercase						,							help="Temperature for the softmax temperature." )
    parser.add_argument(
        "--alpha_ce"						,							default=0.5						,							type=lowercase						,							help="Linear weight for the distillation loss. Must be >=0." )
    parser.add_argument(
        "--alpha_mlm"						,							default=0.0						,							type=lowercase						,							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=lowercase						,							help="Linear weight for the CLM loss. Must be >=0." )
    parser.add_argument("--alpha_mse"						,							default=0.0						,							type=lowercase						,							help="Linear weight of the MSE loss. Must be >=0." )
    parser.add_argument(
        "--alpha_cos"						,							default=0.0						,							type=lowercase						,							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=lowercase						,							help="Proportion of tokens for which we need to make a prediction."						,							)
    parser.add_argument("--word_mask"						,							default=0.8						,							type=lowercase						,							help="Proportion of tokens to mask out." )
    parser.add_argument("--word_keep"						,							default=0.1						,							type=lowercase						,							help="Proportion of tokens to keep." )
    parser.add_argument("--word_rand"						,							default=0.1						,							type=lowercase						,							help="Proportion of tokens to randomly replace." )
    parser.add_argument(
        "--mlm_smoothing"						,							default=0.7						,							type=lowercase						,							help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."						,							)
    parser.add_argument("--token_counts"						,							type=lowercase						,							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=lowercase						,							default=3						,							help="Number of pass on the whole dataset." )
    parser.add_argument("--batch_size"						,							type=lowercase						,							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=lowercase						,							default=50						,							help="Gradient accumulation for larger training batches."						,							)
    parser.add_argument("--warmup_prop"						,							default=0.05						,							type=lowercase						,							help="Linear warmup proportion." )
    parser.add_argument("--weight_decay"						,							default=0.0						,							type=lowercase						,							help="Weight decay if we apply some." )
    parser.add_argument("--learning_rate"						,							default=5e-4						,							type=lowercase						,							help="The initial learning rate for Adam." )
    parser.add_argument("--adam_epsilon"						,							default=1e-6						,							type=lowercase						,							help="Epsilon for Adam optimizer." )
    parser.add_argument("--max_grad_norm"						,							default=5.0						,							type=lowercase						,							help="Max gradient norm." )
    parser.add_argument("--initializer_range"						,							default=0.02						,							type=lowercase						,							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=lowercase						,							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=lowercase						,							default=1						,							help="Number of GPUs in the node." )
    parser.add_argument("--local_rank"						,							type=lowercase						,							default=-1						,							help="Distributed training - Local rank" )
    parser.add_argument("--seed"						,							type=lowercase						,							default=56						,							help="Random seed" )

    parser.add_argument("--log_interval"						,							type=lowercase						,							default=500						,							help="Tensorboard logging interval." )
    parser.add_argument("--checkpoint_interval"						,							type=lowercase						,							default=4000						,							help="Checkpoint interval." )
    snake_case    :      str      					=    parser.parse_args()
    sanity_checks(lowercase )

    # ARGS #
    init_gpu_params(lowercase )
    set_seed(lowercase )
    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(lowercase )						,							lowercase						,							indent=4 )
        git_log(args.dump_path )

    snake_case					,snake_case					,snake_case    :      int      					=    MODEL_CLASSES[args.student_type]
    snake_case					,snake_case					,snake_case    :      int      					=    MODEL_CLASSES[args.teacher_type]

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

    if args.mlm:
        logger.info(F'Loading token counts from {args.token_counts} (already pre-computed)' )
        with open(args.token_counts						,							"rb" ) as fp:
            snake_case    :      Dict      					=    pickle.load(lowercase )

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

    snake_case    :      str      					=    LmSeqsDataset(params=lowercase						,							data=lowercase )
    logger.info("Data loader created." )

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

    if args.student_pretrained_weights is not None:
        logger.info(F'Loading pretrained weights from {args.student_pretrained_weights}' )
        snake_case    :      Optional[int]      					=    student_model_class.from_pretrained(args.student_pretrained_weights						,							config=lowercase )
    else:
        snake_case    :      Union[str, Any]      					=    student_model_class(lowercase )

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

    # TEACHER #
    snake_case    :      List[Any]      					=    teacher_model_class.from_pretrained(args.teacher_name						,							output_hidden_states=lowercase )
    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(lowercase						,							lowercase )
    if args.freeze_token_type_embds:
        freeze_token_type_embeddings(lowercase						,							lowercase )

    # 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()
    snake_case    :      Optional[Any]      					=    Distiller(
        params=lowercase						,							dataset=lowercase						,							token_probs=lowercase						,							student=lowercase						,							teacher=lowercase )
    distiller.train()
    logger.info("Let's go get some drinks." )


if __name__ == "__main__":
  main()





