



'''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


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





def   a_   (		_UpperCAmelCase    :      Union[str, Any]				)    ->  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   a_   (		_UpperCAmelCase    :      Optional[Any] ,_UpperCAmelCase    :      Optional[Any]				)    ->  int:
      if args.student_type == "roberta":
            __snake_case :   Any													=    False
      elif args.student_type == "gpt2":
            __snake_case :   List[Any]													=    False





def   a_   (		_UpperCAmelCase    :      int ,_UpperCAmelCase    :      Tuple				)    ->  List[str]:
      if args.student_type == "roberta":
            __snake_case :   Union[str, Any]													=    False





def   a_   (		)    ->  List[Any]:
      __snake_case :   Dict													=    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=5_00 ,help='Tensorboard logging interval.'				)
      parser.add_argument('--checkpoint_interval' ,type=_UpperCAmelCase ,default=40_00 ,help='Checkpoint interval.'				)
      __snake_case :   str													=    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				)

      __snake_case							,     __snake_case							,     __snake_case :   Optional[Any]													=    MODEL_CLASSES[args.student_type]
      __snake_case							,     __snake_case							,     __snake_case :   Tuple													=    MODEL_CLASSES[args.teacher_type]

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

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

      __snake_case :   Dict													=    LmSeqsDataset(params=_UpperCAmelCase ,data=_UpperCAmelCase				)
      logger.info('Data loader created.'				)

      # STUDENT #
      logger.info(f'''Loading student config from {args.student_config}'''				)
      __snake_case :   List[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 :   str													=    student_model_class.from_pretrained(args.student_pretrained_weights ,config=_UpperCAmelCase				)
      else:
            __snake_case :   List[str]													=    student_model_class(_UpperCAmelCase				)

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

      # TEACHER #
      __snake_case :   Optional[int]													=    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()
      __snake_case :   List[Any]													=    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 re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= ['''image_processor''', '''tokenizer''']
 A__				= '''AutoImageProcessor'''
 A__				= '''AutoTokenizer'''
 def __init__(   self						:							List[str]      ,     __a						:							Optional[int]=None      ,     __a						:							Any=None      ,     **__a						:							Optional[int]  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   str													=    None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'      ,     __a      ,     )
             __snake_case :   Tuple													=    kwargs.pop('feature_extractor'  )

       __snake_case :   Union[str, Any]													=    image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.'  )
       if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.'  )

       super().__init__(__a      ,     __a  )
       __snake_case :   Any													=    self.image_processor
       __snake_case :   int													=    False
 def __call__(   self						:							str      ,     *__a						:							List[str]      ,     **__a						:							List[str]  )      ->   Dict:







       '''simple docstring'''
       # For backward compatibility
       if self._in_target_context_manager:
             return self.current_processor(*__a      ,     **__a  )

       __snake_case :   List[Any]													=    kwargs.pop('images'      ,     __a  )
       __snake_case :   Any													=    kwargs.pop('text'      ,     __a  )
       if len(__a  ) > 0:
             __snake_case :   List[Any]													=    args[0]
             __snake_case :   Dict													=    args[1:]

       if images is None and text is None:
             raise ValueError('You need to specify either an `images` or `text` input to process.'  )

       if images is not None:
             __snake_case :   int													=    self.image_processor(__a      ,     *__a      ,     **__a  )
       if text is not None:
             __snake_case :   Tuple													=    self.tokenizer(__a      ,     **__a  )

       if text is None:
             return inputs
       elif images is None:
             return encodings
       else:
             __snake_case :   List[str]													=    encodings['input_ids']
             return inputs
 def A_   (   self						:							str      ,     *__a						:							Union[str, Any]      ,     **__a						:							Optional[Any]  )      ->   List[str]:







       '''simple docstring'''
       return self.tokenizer.batch_decode(*__a      ,     **__a  )
 def A_   (   self						:							Any      ,     *__a						:							List[Any]      ,     **__a						:							Dict  )      ->   List[Any]:







       '''simple docstring'''
       return self.tokenizer.decode(*__a      ,     **__a  )
 @contextmanager
 def A_   (   self						:							str  )      ->   List[Any]:







       '''simple docstring'''
       warnings.warn(
           '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
           'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
           'your images inputs, or in a separate call.'  )
       __snake_case :   Union[str, Any]													=    True
       __snake_case :   List[Any]													=    self.tokenizer
       yield
       __snake_case :   str													=    self.image_processor
       __snake_case :   Tuple													=    False
 def A_   (   self						:							List[str]      ,     __a						:							Tuple      ,     __a						:							Union[str, Any]=False      ,     __a						:							str=None  )      ->   List[str]:







       '''simple docstring'''
       if added_vocab is None:
             __snake_case :   List[Any]													=    self.tokenizer.get_added_vocab()

       __snake_case :   Tuple													=    {}

       while tokens:
             __snake_case :   Optional[Any]													=    re.search(r'<s_(.*?)>'      ,     __a      ,     re.IGNORECASE  )
             if start_token is None:
                   break
             __snake_case :   Any													=    start_token.group(1  )
             __snake_case :   Dict													=    re.search(rf'''</s_{key}>'''      ,     __a      ,     re.IGNORECASE  )
             __snake_case :   List[str]													=    start_token.group()
             if end_token is None:
                   __snake_case :   List[Any]													=    tokens.replace(__a      ,     ''  )
             else:
                   __snake_case :   int													=    end_token.group()
                   __snake_case :   Optional[int]													=    re.escape(__a  )
                   __snake_case :   List[str]													=    re.escape(__a  )
                   __snake_case :   Optional[int]													=    re.search(f'''{start_token_escaped}(.*?){end_token_escaped}'''      ,     __a      ,     re.IGNORECASE  )
                   if content is not None:
                         __snake_case :   Tuple													=    content.group(1  ).strip()
                         if r"<s_" in content and r"</s_" in content:  # non-leaf node
                               __snake_case :   str													=    self.tokenajson(__a      ,     is_inner_value=__a      ,     added_vocab=__a  )
                               if value:
                                     if len(__a  ) == 1:
                                           __snake_case :   int													=    value[0]
                                     __snake_case :   Union[str, Any]													=    value
                         else:  # leaf nodes
                               __snake_case :   Any													=    []
                               for leaf in content.split(r'<sep/>'  ):
                                     __snake_case :   str													=    leaf.strip()
                                     if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                                           __snake_case :   Any													=    leaf[1:-2]  # for categorical special tokens
                                     output[key].append(__a  )
                               if len(output[key]  ) == 1:
                                     __snake_case :   int													=    output[key][0]

                   __snake_case :   Union[str, Any]													=    tokens[tokens.find(__a  ) + len(__a  ) :].strip()
                   if tokens[:6] == r"<sep/>":  # non-leaf nodes
                         return [output] + self.tokenajson(tokens[6:]      ,     is_inner_value=__a      ,     added_vocab=__a  )

       if len(__a  ):
             return [output] if is_inner_value else output
       else:
             return [] if is_inner_value else {"text_sequence": tokens}
 @property
 def A_   (   self						:							Optional[int]  )      ->   Any:







       '''simple docstring'''
       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'      ,     __a      ,     )
       return self.image_processor_class





 @property
 def A_   (   self						:							Union[str, Any]  )      ->   Optional[int]:







       '''simple docstring'''
       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'      ,     __a      ,     )
       return self.image_processor



