





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__:				Union[str, 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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[str]       , UpperCamelCase__:				int     ):
		if args.student_type == "roberta":
				SCREAMING_SNAKE_CASE__								=   False
		elif args.student_type == "gpt2":
				SCREAMING_SNAKE_CASE__								=   False






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[Any]       , UpperCamelCase__:				int     ):
		if args.student_type == "roberta":
				SCREAMING_SNAKE_CASE__								=   False






def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   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=0.1_5       , 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=0.0_5       , 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=0.0_2       , 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=500       , help="""Tensorboard logging interval."""     )
		parser.add_argument("""--checkpoint_interval"""       , type=UpperCamelCase__       , default=4_000       , help="""Checkpoint interval."""     )
		SCREAMING_SNAKE_CASE__								=   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     )

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

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

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

		SCREAMING_SNAKE_CASE__								=   LmSeqsDataset(params=UpperCamelCase__       , data=UpperCamelCase__     )
		logger.info("""Data loader created."""     )

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

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

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

		# TEACHER #
		SCREAMING_SNAKE_CASE__								=   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()
		SCREAMING_SNAKE_CASE__								=   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 collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
     import jieba

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


snake_case   					=				logging.get_logger(__name__)

snake_case   					=				{'''vocab_file''': '''vocab.txt'''}

snake_case   					=				{
    '''vocab_file''': {
        '''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
    },
}

snake_case   					=				{
    '''openbmb/cpm-ant-10b''': 1_0_2_4,
}




def 			snake_case		(		lowerCAmelCase_	)						->				int:
  _snake_case   	=      collections.OrderedDict()
  with open(lowerCAmelCase_  ,						'''r'''  ,						encoding='''utf-8'''	) as reader:
    _snake_case   	=      reader.readlines()
  for index, token in enumerate(lowerCAmelCase_	):
    _snake_case   	=      token.rstrip('''\n'''	)
    _snake_case   	=      index
  return vocab



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):



   def __init__(       self    :	Union[str, Any]							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	List[str]="<unk>"							,       __lowerCamelCase    :	Tuple=2_0_0				):



     """simple docstring"""
     _snake_case   	=      vocab
     _snake_case   	=      unk_token
     _snake_case   	=      max_input_chars_per_word





   def    __UpperCAmelCase    (       self    :	Any							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     _snake_case   	=      list(__lowerCamelCase				)
     if len(__lowerCamelCase				) > self.max_input_chars_per_word:
       return [self.unk_token]

     _snake_case   	=      0
     _snake_case   	=      []
     while start < len(__lowerCamelCase				):
       _snake_case   	=      len(__lowerCamelCase				)
       _snake_case   	=      None
       while start < end:
         _snake_case   	=      ''''''.join(chars[start:end]				)
         if substr in self.vocab:
           _snake_case   	=      substr
           break
         end -= 1
       if cur_substr is None:
         sub_tokens.append(self.unk_token				)
         start += 1
       else:
         sub_tokens.append(__lowerCamelCase				)
         _snake_case   	=      end

     return sub_tokens



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	List[str]           =  VOCAB_FILES_NAMES
   A__		:	str           =  PRETRAINED_VOCAB_FILES_MAP
   A__		:	str           =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__		:	Union[str, Any]           =  ['''input_ids''', '''attention_mask''']
   A__		:	Optional[int]           =  False



   def __init__(       self    :	Dict							,       __lowerCamelCase    :	Optional[Any]							,       __lowerCamelCase    :	str="<d>"							,       __lowerCamelCase    :	Tuple="</d>"							,       __lowerCamelCase    :	Tuple="<s>"							,       __lowerCamelCase    :	int="</s>"							,       __lowerCamelCase    :	List[str]="<pad>"							,       __lowerCamelCase    :	int="<unk>"							,       __lowerCamelCase    :	int="</n>"							,       __lowerCamelCase    :	Tuple="</_>"							,       __lowerCamelCase    :	Optional[Any]="left"							,       **__lowerCamelCase    :	str							,       ):



     """simple docstring"""
     requires_backends(self							,       ['''jieba''']				)
     super().__init__(
         bod_token=__lowerCamelCase							,       eod_token=__lowerCamelCase							,       bos_token=__lowerCamelCase							,       eos_token=__lowerCamelCase							,       pad_token=__lowerCamelCase							,       unk_token=__lowerCamelCase							,       line_token=__lowerCamelCase							,       space_token=__lowerCamelCase							,       padding_side=__lowerCamelCase							,       **__lowerCamelCase							,       )
     _snake_case   	=      bod_token
     _snake_case   	=      eod_token
     _snake_case   	=      load_vocab(__lowerCamelCase				)
     _snake_case   	=      self.encoder[space_token]
     _snake_case   	=      self.encoder[line_token]

     del self.encoder[space_token]
     del self.encoder[line_token]

     _snake_case   	=      collections.OrderedDict(sorted(self.encoder.items()							,       key=lambda __lowerCamelCase				: x[1]				)				)
     _snake_case   	=      {v: k for k, v in self.encoder.items()}

     _snake_case   	=      WordpieceTokenizer(vocab=self.encoder							,       unk_token=self.unk_token				)



   @property
   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     return self.encoder[self.bod_token]



   @property
   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     return self.encoder[self.eod_token]



   @property
   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     return self.encoder["\n"]



   @property
   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     return len(self.encoder				)



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     return dict(self.encoder							,       **self.added_tokens_encoder				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      []
     for x in jieba.cut(__lowerCamelCase							,       cut_all=__lowerCamelCase				):
       output_tokens.extend(self.wordpiece_tokenizer.tokenize(__lowerCamelCase				)				)
     return output_tokens



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	Union[str, Any]							,       **__lowerCamelCase    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      [i for i in token_ids if i >= 0]
     _snake_case   	=      [
         x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
     ]
     return super()._decode(__lowerCamelCase							,       **__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	Union[str, Any]				):



     """simple docstring"""
     return token in self.encoder



   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	List[str]				):



     """simple docstring"""
     return "".join(__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     return self.encoder.get(__lowerCamelCase							,       self.encoder.get(self.unk_token				)				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     return self.decoder.get(__lowerCamelCase							,       self.unk_token				)



   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	Optional[str] = None				):



     """simple docstring"""
     if os.path.isdir(__lowerCamelCase				):
       _snake_case   	=      os.path.join(
           __lowerCamelCase							,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)
     else:
       _snake_case   	=      (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
     _snake_case   	=      0
     if " " in self.encoder:
       _snake_case   	=      self.encoder[''' ''']
       del self.encoder[" "]
     if "\n" in self.encoder:
       _snake_case   	=      self.encoder['''\n''']
       del self.encoder["\n"]
     _snake_case   	=      collections.OrderedDict(sorted(self.encoder.items()							,       key=lambda __lowerCamelCase				: x[1]				)				)
     with open(__lowerCamelCase							,       '''w'''							,       encoding='''utf-8'''				) as writer:
       for token, token_index in self.encoder.items():
         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!'''				)
           _snake_case   	=      token_index
         writer.write(token + '''\n'''				)
         index += 1
     return (vocab_file,)



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	List[int]							,       __lowerCamelCase    :	List[int] = None				):



     """simple docstring"""
     if token_ids_a is None:
       return [self.bos_token_id] + token_ids_a
     return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a





   def    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	List[int]							,       __lowerCamelCase    :	Optional[List[int]] = None							,       __lowerCamelCase    :	bool = False				):



     """simple docstring"""

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

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





