





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


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



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   int:


      '''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    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   Dict:


      '''simple docstring'''


      if args.student_type == "roberta":
            SCREAMING_SNAKE_CASE		=						False
      elif args.student_type == "gpt2":
            SCREAMING_SNAKE_CASE		=						False



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   Union[str, Any]:


      '''simple docstring'''


      if args.student_type == "roberta":
            SCREAMING_SNAKE_CASE		=						False



def    __lowercase   (      ) ->   Any:


      '''simple docstring'''


      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=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."""	)
      SCREAMING_SNAKE_CASE		=						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	)

      SCREAMING_SNAKE_CASE		=						MODEL_CLASSES[args.student_type]
      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(A_	)
            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(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:
                  SCREAMING_SNAKE_CASE		=						pickle.load(A_	)

            SCREAMING_SNAKE_CASE		=						np.maximum(A_ ,						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(A_	)
      else:
            SCREAMING_SNAKE_CASE		=						None

      SCREAMING_SNAKE_CASE		=						LmSeqsDataset(params=A_ ,						data=A_	)
      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=A_	)
      else:
            SCREAMING_SNAKE_CASE		=						student_model_class(A_	)

      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=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()
      SCREAMING_SNAKE_CASE		=						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()





"""simple docstring"""





from collections import defaultdict
from math import ceil, sqrt

def   __SCREAMING_SNAKE_CASE  (      A_ = 1_00_00_00						,     A_ = 10 ):
	lowerCAmelCase__	:						defaultdict  				=	defaultdict(A_ )

	for outer_width in range(3						,     (t_limit // 4) + 2 ):
		if outer_width * outer_width > t_limit:
			lowerCAmelCase__	:						int  				=	max(
			    ceil(sqrt(outer_width * outer_width - t_limit ) )						,     1 )
		else:
			lowerCAmelCase__	:						Tuple  				=	1

		hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

		for hole_width in range(A_						,     outer_width - 1						,     2 ):
			count[outer_width * outer_width - hole_width * hole_width] += 1

	return sum(1 for n in count.values() if 1 <= n <= 10 )


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




