




import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


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







def        lowerCAmelCase_			(  snake_case_  ):
						assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
						assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
						if args.mlm:
												assert os.path.isfile(args.token_counts  )
												assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
						else:
												assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

						assert args.teacher_type == args.student_type or (
						    args.student_type == "distilbert" and args.teacher_type == "bert"
						)
						assert os.path.isfile(args.student_config  )
						if args.student_pretrained_weights is not None:
												assert os.path.isfile(args.student_pretrained_weights  )

						if args.freeze_token_type_embds:
												assert args.student_type in ["roberta"]

						assert args.alpha_ce >= 0.0
						assert args.alpha_mlm >= 0.0
						assert args.alpha_clm >= 0.0
						assert args.alpha_mse >= 0.0
						assert args.alpha_cos >= 0.0
						assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
						if args.student_type == "roberta":
												_A							:  Optional[Any]    =		False
						elif args.student_type == "gpt2":
												_A							:  Optional[Any]    =		False







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
						if args.student_type == "roberta":
												_A							:  Optional[int]    =		False







def        lowerCAmelCase_			(  ):
						_A							:  Optional[int]    =		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=SCREAMING_SNAKE_CASE_,required=SCREAMING_SNAKE_CASE_,help="""The output directory (log, checkpoints, parameters, etc.)"""  )
						parser.add_argument(
						    """--data_file""",type=SCREAMING_SNAKE_CASE_,required=SCREAMING_SNAKE_CASE_,help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence.""",)

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

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

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

						parser.add_argument("""--log_interval""",type=SCREAMING_SNAKE_CASE_,default=500,help="""Tensorboard logging interval."""  )
						parser.add_argument("""--checkpoint_interval""",type=SCREAMING_SNAKE_CASE_,default=4000,help="""Checkpoint interval."""  )
						_A							:  Dict    =		parser.parse_args()
						sanity_checks(SCREAMING_SNAKE_CASE_  )

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

						_A    ,     _A    ,     _A							:  Union[str, Any]    =		MODEL_CLASSES[args.student_type]
						_A    ,     _A    ,     _A							:  List[Any]    =		MODEL_CLASSES[args.teacher_type]

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

						if args.mlm:
												logger.info(f'''Loading token counts from {args.token_counts} (already pre-computed)'''  )
												with open(args.token_counts,"""rb"""  ) as fp:
																		_A							:  List[str]    =		pickle.load(SCREAMING_SNAKE_CASE_  )

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

						_A							:  Tuple    =		LmSeqsDataset(params=SCREAMING_SNAKE_CASE_,data=SCREAMING_SNAKE_CASE_  )
						logger.info("""Data loader created."""  )

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

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

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

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

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


if __name__ == "__main__":
		main()







def        lowerCAmelCase_			(  snake_case_ = 1000000  ):
      _A							:  Any    =		limit + 1
      _A							:  Tuple    =		[0] * limit
      for first_term in range(1,snake_case_  ):
            for n in range(snake_case_,snake_case_,snake_case_  ):
                  _A							:  Optional[int]    =		first_term + n / first_term
                  if common_difference % 4:  # d must be divisble by 4
                        continue
                  else:
                        common_difference /= 4
                        if (
                            first_term > common_difference
                            and first_term < 4 * common_difference
                        ):  # since x,y,z are positive integers
                              frequency[n] += 1  # so z>0 and a>d ,also 4d<a

      _A							:  List[str]    =		sum(1 for x in frequency[1:limit] if x == 10  )

      return count


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

