


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



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int				)	->						Dict:
     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      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[int] ,       UpperCAmelCase_       :							List[Any]				)	->						Union[str, Any]:
     if args.student_type == "roberta":
          __lowerCamelCase  :      List[str]														=						False
     elif args.student_type == "gpt2":
          __lowerCamelCase  :      Any														=						False



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[int] ,       UpperCAmelCase_       :							List[Any]				)	->						str:
     if args.student_type == "roberta":
          __lowerCamelCase  :      Optional[Any]														=						False



def      UpperCAmelCase__   (      )	->						Union[str, Any]:
     __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=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=0.15 ,       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=0.05 ,       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=0.02 ,       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.'				)
     __lowerCamelCase  :      Optional[int]														=						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				)

     __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      int														=						MODEL_CLASSES[args.student_type]
     __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      Dict														=						MODEL_CLASSES[args.teacher_type]

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

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

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

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

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

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

     # TEACHER #
     __lowerCamelCase  :      Tuple														=						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()
     __lowerCamelCase  :      Dict														=						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'''



from math import isqrt



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int     )	->      bool:

   """simple docstring"""
   return all(number % divisor != 0 for divisor in range(2,       isqrt(SCREAMING_SNAKE_CASE__     ) + 1     )     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int = 10**6     )	->      int:

   """simple docstring"""
   __a					  =     0
   __a					  =     1
   __a					  =     7
   while prime_candidate < max_prime:
      primes_count += is_prime(SCREAMING_SNAKE_CASE__     )

      cube_index += 1
      prime_candidate += 6 * cube_index

   return primes_count


if __name__ == "__main__":
 print(f"""{solution() = }""")