"""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


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







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->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 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Dict:
  if args.student_type == "roberta":
    a__:					Union[str, Any]      							=     False
  elif args.student_type == "gpt2":
    a__:					List[str]      							=     False







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Optional[int]:
  if args.student_type == "roberta":
    a__:					str      							=     False







def 						__a       (     )     ->List[Any]:
  a__:					str      							=     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__:					Optional[int]      							=     MODEL_CLASSES[args.student_type]
  a__							,   a__							,   a__:					List[str]      							=     MODEL_CLASSES[args.teacher_type]

  # TOKENIZER #
  a__:					str      							=     teacher_tokenizer_class.from_pretrained(args.teacher_name       )
  a__:					str      							=     {}
  for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
    a__:					int      							=     tokenizer.all_special_tokens.index(_SCREAMING_SNAKE_CASE       )
    a__:					str      							=     tokenizer.all_special_ids[idx]
  logger.info(F'Special tokens {special_tok_ids}'       )
  a__:					Optional[int]      							=     special_tok_ids
  a__:					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:
    a__:					str      							=     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__:					str      							=     pickle.load(_SCREAMING_SNAKE_CASE       )

    a__:					int      							=     np.maximum(_SCREAMING_SNAKE_CASE		,					1       ) ** -args.mlm_smoothing
    for idx in special_tok_ids.values():
      a__:					Any      							=     0.0  # do not predict special tokens
    a__:					Tuple      							=     torch.from_numpy(_SCREAMING_SNAKE_CASE       )
  else:
    a__:					Optional[Any]      							=     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__:					Optional[int]      							=     student_config_class.from_pretrained(args.student_config       )
  a__:					List[Any]      							=     True

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

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

  # TEACHER #
  a__:					int      							=     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__:					int      							=     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()



"""simple docstring"""
def 						__a       (     _SCREAMING_SNAKE_CASE = 1000000       )     ->int:
  a__:					Any      							=     limit + 1
  a__:					List[str]      							=     [0] * limit
  for first_term in range(1		,					_SCREAMING_SNAKE_CASE       ):
    for n in range(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       ):
      a__:					Any      							=     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__:					Optional[int]      							=     sum(1 for x in frequency[1:limit] if x == 10       )

  return count


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


