





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







def _lowerCAmelCase     (      _lowerCAmelCase    )->     Tuple:
 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     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     List[str]:
 if args.student_type == "roberta":
  __UpperCAmelCase					=   False
 elif args.student_type == "gpt2":
  __UpperCAmelCase					=   False







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     Optional[int]:
 if args.student_type == "roberta":
  __UpperCAmelCase					=   False







def _lowerCAmelCase     (      )->     Optional[Any]:
 __UpperCAmelCase					=   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.'    )
 __UpperCAmelCase					=   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    )

 __UpperCAmelCase					=   MODEL_CLASSES[args.student_type]
 __UpperCAmelCase					=   MODEL_CLASSES[args.teacher_type]

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

  __UpperCAmelCase					=   np.maximum(UpperCamelCase__       ,    1    ) ** -args.mlm_smoothing
  for idx in special_tok_ids.values():
   __UpperCAmelCase					=   0.0  # do not predict special tokens
  __UpperCAmelCase					=   torch.from_numpy(UpperCamelCase__    )
 else:
  __UpperCAmelCase					=   None

 __UpperCAmelCase					=   LmSeqsDataset(params=UpperCamelCase__       ,    data=UpperCamelCase__    )
 logger.info('Data loader created.'    )

 # STUDENT #
 logger.info(F'Loading student config from {args.student_config}'    )
 __UpperCAmelCase					=   student_config_class.from_pretrained(args.student_config    )
 __UpperCAmelCase					=   True

 if args.student_pretrained_weights is not None:
  logger.info(F'Loading pretrained weights from {args.student_pretrained_weights}'    )
  __UpperCAmelCase					=   student_model_class.from_pretrained(args.student_pretrained_weights       ,    config=UpperCamelCase__    )
 else:
  __UpperCAmelCase					=   student_model_class(UpperCamelCase__    )

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

 # TEACHER #
 __UpperCAmelCase					=   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()
 __UpperCAmelCase					=   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 __future__ import annotations

from collections.abc import Sequence
from typing import Literal







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str | Literal[False]:
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 __UpperCAmelCase					=   0
 for i in range(len(_lowerCAmelCase    )    ):
  if lista[i] != lista[i]:
   count += 1
   __UpperCAmelCase					=   '_'
 if count > 1:
  return False
 else:
  return "".join(_lowerCAmelCase    )







def _lowerCAmelCase     (      _lowerCAmelCase    )->     list[str]:
 __UpperCAmelCase					=   []
 while True:
  __UpperCAmelCase					=   ['$'] * len(_lowerCAmelCase    )
  __UpperCAmelCase					=   []
  for i in range(len(_lowerCAmelCase    )    ):
   for j in range(i + 1       ,    len(_lowerCAmelCase    )    ):
    __UpperCAmelCase					=   compare_string(binary[i]       ,    binary[j]    )
    if k is False:
     __UpperCAmelCase					=   '*'
     __UpperCAmelCase					=   '*'
     temp.append('X'    )
  for i in range(len(_lowerCAmelCase    )    ):
   if checka[i] == "$":
    pi.append(binary[i]    )
  if len(_lowerCAmelCase    ) == 0:
   return pi
  __UpperCAmelCase					=   list(set(_lowerCAmelCase    )    )







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     list[str]:
 __UpperCAmelCase					=   []
 for minterm in minterms:
  __UpperCAmelCase					=   ''
  for _ in range(_lowerCAmelCase    ):
   __UpperCAmelCase					=   str(minterm % 2    ) + string
   minterm //= 2
  temp.append(_lowerCAmelCase    )
 return temp







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     bool:
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 __UpperCAmelCase					=   0
 for i in range(len(_lowerCAmelCase    )    ):
  if lista[i] != lista[i]:
   count_n += 1
 return count_n == count







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     list[str]:
 __UpperCAmelCase					=   []
 __UpperCAmelCase					=   [0] * len(_lowerCAmelCase    )
 for i in range(len(chart[0]    )    ):
  __UpperCAmelCase					=   0
  __UpperCAmelCase					=   -1
  for j in range(len(_lowerCAmelCase    )    ):
   if chart[j][i] == 1:
    count += 1
    __UpperCAmelCase					=   j
  if count == 1:
   __UpperCAmelCase					=   1
 for i in range(len(_lowerCAmelCase    )    ):
  if select[i] == 1:
   for j in range(len(chart[0]    )    ):
    if chart[i][j] == 1:
     for k in range(len(_lowerCAmelCase    )    ):
      __UpperCAmelCase					=   0
   temp.append(prime_implicants[i]    )
 while True:
  __UpperCAmelCase					=   0
  __UpperCAmelCase					=   -1
  __UpperCAmelCase					=   0
  for i in range(len(_lowerCAmelCase    )    ):
   __UpperCAmelCase					=   chart[i].count(1    )
   if count_n > max_n:
    __UpperCAmelCase					=   count_n
    __UpperCAmelCase					=   i

  if max_n == 0:
   return temp

  temp.append(prime_implicants[rem]    )

  for i in range(len(chart[0]    )    ):
   if chart[rem][i] == 1:
    for j in range(len(_lowerCAmelCase    )    ):
     __UpperCAmelCase					=   0







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     list[list[int]]:
 __UpperCAmelCase					=   [[0 for x in range(len(_lowerCAmelCase    )    )] for x in range(len(_lowerCAmelCase    )    )]
 for i in range(len(_lowerCAmelCase    )    ):
  __UpperCAmelCase					=   prime_implicants[i].count('_'    )
  for j in range(len(_lowerCAmelCase    )    ):
   if is_for_table(prime_implicants[i]       ,    binary[j]       ,    _lowerCAmelCase    ):
    __UpperCAmelCase					=   1

 return chart







def _lowerCAmelCase     (      )->     None:
 __UpperCAmelCase					=   int(input('Enter the no. of variables\n'    )    )
 __UpperCAmelCase					=   [
     float(_lowerCAmelCase    )
     for x in input(
         'Enter the decimal representation of Minterms \'Spaces Separated\'\n'    ).split()
 ]
 __UpperCAmelCase					=   decimal_to_binary(_lowerCAmelCase       ,    _lowerCAmelCase    )

 __UpperCAmelCase					=   check(_lowerCAmelCase    )
 print('Prime Implicants are:'    )
 print(_lowerCAmelCase    )
 __UpperCAmelCase					=   prime_implicant_chart(_lowerCAmelCase       ,    _lowerCAmelCase    )

 __UpperCAmelCase					=   selection(_lowerCAmelCase       ,    _lowerCAmelCase    )
 print('Essential Prime Implicants are:'    )
 print(_lowerCAmelCase    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()





