



"""simple docstring"""






import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__      =    logging.get_logger(__name__)

lowerCAmelCase__      =    '''▁'''

lowerCAmelCase__      =    {'''vocab_file''': '''vocab.txt''', '''sentencepiece_model_ckpt''': '''sentencepiece.bpe.model'''}

lowerCAmelCase__      =    {
    '''sentencepiece_model_file''': '''sentencepiece.bpe.model''',
    '''vocab_file''': '''vocab.txt''',
}

lowerCAmelCase__      =    {
    '''vocab_file''': {
        '''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
        '''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
    },
    '''sentencepiece_model_file''': {
        '''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
        '''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
    },
}

lowerCAmelCase__      =    {
    '''ernie-m-base''': 514,
    '''ernie-m-large''': 514,
}

lowerCAmelCase__      =    {
    '''ernie-m-base''': {'''do_lower_case''': False},
    '''ernie-m-large''': {'''do_lower_case''': False},
}






class 	__snake_case	(     _lowercase):
  snake_case__    :       Union[str, Any]        =						["input_ids"]

  snake_case__    :       Optional[int]        =						VOCAB_FILES_NAMES
  snake_case__    :       Optional[int]        =						PRETRAINED_INIT_CONFIGURATION
  snake_case__    :       int        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  snake_case__    :       List[str]        =						PRETRAINED_VOCAB_FILES_MAP
  snake_case__    :       Tuple        =						RESOURCE_FILES_NAMES




  def __init__(			self					:						Dict						, __lowerCAmelCase					:						List[Any]						, __lowerCAmelCase					:						Dict=None						, __lowerCAmelCase					:						Dict=False						, __lowerCAmelCase					:						int="utf8"						, __lowerCAmelCase					:						Dict="[UNK]"						, __lowerCAmelCase					:						Optional[int]="[SEP]"						, __lowerCAmelCase					:						str="[PAD]"						, __lowerCAmelCase					:						Tuple="[CLS]"						, __lowerCAmelCase					:						Optional[int]="[MASK]"						, __lowerCAmelCase					:						Optional[Dict[str, Any]] = None						, **__lowerCAmelCase					:						Tuple						, ):






         """simple docstring"""






         _lowerCamelCase						:    Dict         =			{} if sp_model_kwargs is None else sp_model_kwargs
         super().__init__(
             do_lower_case=__A						, unk_token=__A						, sep_token=__A						, pad_token=__A						, cls_token=__A						, mask_token=__A						, vocab_file=__A						, encoding=__A						, sp_model_kwargs=self.sp_model_kwargs						, **__A						, )
         _lowerCamelCase						:    List[Any]         =			do_lower_case
         _lowerCamelCase						:    Any         =			sentencepiece_model_ckpt
         _lowerCamelCase						:    Optional[Any]         =			spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.Load(__A		)

         # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
         if vocab_file is not None:
                _lowerCamelCase						:    int         =			self.load_vocab(filepath=__A		)
         else:
                _lowerCamelCase						:    Union[str, Any]         =			{self.sp_model.id_to_piece(__A		): id for id in range(self.sp_model.get_piece_size()		)}
         _lowerCamelCase						:    Dict         =			{v: k for k, v in self.vocab.items()}




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]						, __lowerCAmelCase					:						Optional[int]		):






         """simple docstring"""






         if text is None:
                return None

         _lowerCamelCase						:    List[Any]         =			self.tokenize(__A		)
         _lowerCamelCase      ,   _lowerCamelCase						:    str         =			'''''', []

         for i, ch in enumerate(__A		):
                if ch in self.SP_CHAR_MAPPING:
                       _lowerCamelCase						:    Tuple         =			self.SP_CHAR_MAPPING.get(__A		)
                else:
                       _lowerCamelCase						:    Optional[int]         =			unicodedata.normalize('''NFKC'''						, __A		)
                if self.is_whitespace(__A		):
                       continue
                normalized_text += ch
                char_mapping.extend([i] * len(__A		)		)

         _lowerCamelCase      ,   _lowerCamelCase      ,   _lowerCamelCase						:    Dict         =			normalized_text, [], 0

         if self.do_lower_case:
                _lowerCamelCase						:    Dict         =			text.lower()

         for token in split_tokens:
                if token[:1] == "▁":
                       _lowerCamelCase						:    Tuple         =			token[1:]
                _lowerCamelCase						:    Any         =			text[offset:].index(__A		) + offset
                _lowerCamelCase						:    Dict         =			start + len(__A		)

                token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)		)
                _lowerCamelCase						:    List[Any]         =			end
         return token_mapping




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]		):






         """simple docstring"""






         return len(self.vocab		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple		):






         """simple docstring"""






         return dict(self.vocab						, **self.added_tokens_encoder		)




  def __getstate__(			self					:						int		):






         """simple docstring"""






         _lowerCamelCase						:    List[str]         =			self.__dict__.copy()
         _lowerCamelCase						:    List[Any]         =			None
         return state




  def __setstate__(			self					:						Optional[Any]						, __lowerCAmelCase					:						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:    str         =			d

         # for backward compatibility
         if not hasattr(self						, '''sp_model_kwargs'''		):
                _lowerCamelCase						:    Tuple         =			{}

         _lowerCamelCase						:    Any         =			spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.Load(self.sentencepiece_model_ckpt		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						str						, __lowerCAmelCase					:						str		):






         """simple docstring"""






         return "".join((self.SP_CHAR_MAPPING.get(__A						, __A		) for c in text)		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]						, __lowerCAmelCase					:						List[Any]						, __lowerCAmelCase					:						List[Any]=False						, __lowerCAmelCase					:						List[Any]=6_4						, __lowerCAmelCase					:						Tuple=0.1		):






         """simple docstring"""






         if self.sp_model_kwargs.get('''enable_sampling'''		) is True:
                _lowerCamelCase						:    Any         =			True
         if self.sp_model_kwargs.get('''alpha'''		) is not None:
                _lowerCamelCase						:    str         =			self.sp_model_kwargs.get('''alpha'''		)
         if self.sp_model_kwargs.get('''nbest_size'''		) is not None:
                _lowerCamelCase						:    Any         =			self.sp_model_kwargs.get('''nbest_size'''		)

         if not enable_sampling:
                _lowerCamelCase						:    Optional[int]         =			self.sp_model.EncodeAsPieces(__A		)
         else:
                _lowerCamelCase						:    Optional[int]         =			self.sp_model.SampleEncodeAsPieces(__A						, __A						, __A		)
         _lowerCamelCase						:    Dict         =			[]
         for pi, piece in enumerate(__A		):
                if piece == SPIECE_UNDERLINE:
                       if not pieces[pi + 1].startswith(__A		) and pi != 0:
                              new_pieces.append(__A		)
                              continue
                       else:
                              continue
                _lowerCamelCase						:    List[Any]         =			0
                for i, chunk in enumerate(__A		):
                       if chunk == SPIECE_UNDERLINE:
                              continue
                       if self.is_ch_char(__A		) or self.is_punct(__A		):
                              if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                                     new_pieces.append(piece[lst_i:i]		)
                              new_pieces.append(__A		)
                              _lowerCamelCase						:    Dict         =			i + 1
                       elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
                              if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                                     new_pieces.append(piece[lst_i:i]		)
                              _lowerCamelCase						:    Any         =			i
                       elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
                              if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                                     new_pieces.append(piece[lst_i:i]		)
                              _lowerCamelCase						:    Optional[int]         =			i
                if len(__A		) > lst_i:
                       new_pieces.append(piece[lst_i:]		)
         return new_pieces




  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple						, __lowerCAmelCase					:						str		):






         """simple docstring"""






         _lowerCamelCase						:    str         =			''''''.join(__A		).replace(__A						, ''' '''		).strip()
         return out_string




  def 						SCREAMING_SNAKE_CASE    (			self					:						str						, __lowerCAmelCase					:						Dict		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[Any]         =			self.convert_ids_to_tokens(__A		)
         _lowerCamelCase						:    Dict         =			''''''.join(__A		).replace(__A						, ''' '''		).strip()
         return out_string




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]						, __lowerCAmelCase					:						Any		):






         """simple docstring"""






         return self.vocab.get(__A						, self.vocab.get(self.unk_token		)		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple						, __lowerCAmelCase					:						Optional[int]		):






         """simple docstring"""






         return self.reverse_vocab.get(__A						, self.unk_token		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]						, __lowerCAmelCase					:						Optional[int]						, __lowerCAmelCase					:						Dict=None		):






         """simple docstring"""






         if token_ids_a is None:
                return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         _lowerCamelCase						:    Union[str, Any]         =			[self.cls_token_id]
         _lowerCamelCase						:    List[str]         =			[self.sep_token_id]
         return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep




  def 						SCREAMING_SNAKE_CASE    (			self					:						Dict						, __lowerCAmelCase					:						Optional[int]						, __lowerCAmelCase					:						Union[str, Any]=None		):






         """simple docstring"""






         if offset_mapping_a is None:
                return [(0, 0)] + offset_mapping_a + [(0, 0)]

         return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]




  def 						SCREAMING_SNAKE_CASE    (			self					:						int						, __lowerCAmelCase					:						Optional[int]						, __lowerCAmelCase					:						int=None						, __lowerCAmelCase					:						str=False		):






         """simple docstring"""






         if already_has_special_tokens:
                if token_ids_a is not None:
                       raise ValueError(
                           '''You should not supply a second sequence if the provided sequence of '''
                           '''ids is already formatted with special tokens for the model.'''		)
                return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

         if token_ids_a is not None:
                return [1] + ([0] * len(__A		)) + [1, 1] + ([0] * len(__A		)) + [1]
         return [1] + ([0] * len(__A		)) + [1]




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]						, __lowerCAmelCase					:						List[int]						, __lowerCAmelCase					:						Optional[List[int]] = None		):






         """simple docstring"""






         if token_ids_a is None:
                # [CLS] X [SEP]
                return (len(__A		) + 2) * [0]

         # [CLS] A [SEP] [SEP] B [SEP]
         return [0] * (len(__A		) + 1) + [1] * (len(__A		) + 3)




  def 						SCREAMING_SNAKE_CASE    (			self					:						str						, __lowerCAmelCase					:						Dict		):






         """simple docstring"""






         if "\u4e00" <= char <= "\u9fff":
                return True
         return False




  def 						SCREAMING_SNAKE_CASE    (			self					:						int						, __lowerCAmelCase					:						List[str]		):






         """simple docstring"""






         if ("a" <= char <= "z") or ("A" <= char <= "Z"):
                return True
         return False




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]						, __lowerCAmelCase					:						Dict		):






         """simple docstring"""






         if char in ",;:.?!~，；：。？！《》【】":
                return True
         return False




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any						, __lowerCAmelCase					:						Optional[Any]		):






         """simple docstring"""






         if char == " " or char == "\t" or char == "\n" or char == "\r":
                return True
         if len(__A		) == 1:
                _lowerCamelCase						:    Union[str, Any]         =			unicodedata.category(__A		)
                if cat == "Zs":
                       return True
         return False




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]						, __lowerCAmelCase					:						List[Any]		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[int]         =			{}
         with io.open(__A						, '''r'''						, encoding='''utf-8'''		) as f:
                for index, line in enumerate(__A		):
                       _lowerCamelCase						:    List[Any]         =			line.rstrip('''\n'''		)
                       _lowerCamelCase						:    Optional[Any]         =			int(__A		)

         return token_to_idx




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]						, __lowerCAmelCase					:						str						, __lowerCAmelCase					:						Optional[str] = None		):






         """simple docstring"""






         _lowerCamelCase						:    Dict         =			0
         if os.path.isdir(__A		):
                _lowerCamelCase						:    Any         =			os.path.join(
                    __A						, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)
         else:
                _lowerCamelCase						:    str         =			(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
         with open(__A						, '''w'''						, encoding='''utf-8'''		) as writer:
                for token, token_index in sorted(self.vocab.items()						, key=lambda __lowerCAmelCase		: kv[1]		):
                       if index != token_index:
                              logger.warning(
                                  f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
                                  ''' Please check that the vocabulary is not corrupted!'''		)
                              _lowerCamelCase						:    Dict         =			token_index
                       writer.write(token + '''\n'''		)
                       index += 1

         _lowerCamelCase						:    Optional[int]         =			os.path.join(__A						, '''sentencepiece.bpe.model'''		)
         with open(__A						, '''wb'''		) as fi:
                _lowerCamelCase						:    Tuple         =			self.sp_model.serialized_model_proto()
                fi.write(__A		)

         return (vocab_file,)










"""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 	snake_case_  (				A_ :		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 	snake_case_  (				A_ :		Dict,    A_ :		Any						):


       '''simple docstring'''





       if args.student_type == "roberta":
              _lowerCamelCase						:    List[str]         =			False
       elif args.student_type == "gpt2":
              _lowerCamelCase						:    Any         =			False






def 	snake_case_  (				A_ :		Optional[Any],    A_ :		List[Any]						):


       '''simple docstring'''





       if args.student_type == "roberta":
              _lowerCamelCase						:    Optional[int]         =			False






def 	snake_case_  (				):


       '''simple docstring'''





       _lowerCamelCase						:    Union[str, 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=5_00,    help='''Tensorboard logging interval.'''						)
       parser.add_argument('''--checkpoint_interval''',    type=A_,    default=40_00,    help='''Checkpoint interval.'''						)
       _lowerCamelCase						:    List[Any]         =			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      ,   _lowerCamelCase      ,   _lowerCamelCase						:    Any         =			MODEL_CLASSES[args.student_type]
       _lowerCamelCase      ,   _lowerCamelCase      ,   _lowerCamelCase						:    Dict         =			MODEL_CLASSES[args.teacher_type]

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

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

       _lowerCamelCase						:    Any         =			LmSeqsDataset(params=A_,    data=A_						)
       logger.info('''Data loader created.'''						)

       # STUDENT #
       logger.info(F'''Loading student config from {args.student_config}'''						)
       _lowerCamelCase						:    str         =			student_config_class.from_pretrained(args.student_config						)
       _lowerCamelCase						:    Union[str, Any]         =			True

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

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

       # TEACHER #
       _lowerCamelCase						:    int         =			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[int]         =			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()


