






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


UpperCAmelCase__							  =						{
    """distilbert""": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    """roberta""": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    """bert""": (BertConfig, BertForMaskedLM, BertTokenizer),
    """gpt2""": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def 		_a       ( a					:Optional[Any]					)				->     int:
 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       ( a					:List[Any]      ,				a					:Tuple					)				->     Optional[int]:



 if args.student_type == "roberta":
  a						    = False
 elif args.student_type == "gpt2":
  a						    = False
def 		_a       ( a					:List[Any]      ,				a					:Optional[Any]					)				->     Dict:



 if args.student_type == "roberta":
  a						    = False
def 		_a       ( )				->     Any:
 a						    = 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=500      ,				help='''Tensorboard logging interval.'''					)
 parser.add_argument('''--checkpoint_interval'''      ,				type=a      ,				default=4_000      ,				help='''Checkpoint interval.'''					)
 a						    = 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					)

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

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

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

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

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

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

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

 # TEACHER #
 a						    = 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()
 a						    = 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"""



def 	SCREAMING_SNAKE_CASE__       (  snake_case       : int			,					snake_case       : int				)->		str:






     '''simple docstring'''



     if not isinstance(snake_case			,					snake_case				):
          raise ValueError("iterations must be defined as integers"				)
     if not isinstance(snake_case			,					snake_case				) or not number >= 1:
          raise ValueError(
              "starting number must be\n                         and integer and be more than 0"				)
     if not iterations >= 1:
          raise ValueError("Iterations must be done more than 0 times to play FizzBuzz"				)

     UpperCAmelCase__						:    str							=							""
     while number <= iterations:
          if number % 3 == 0:
               out += "Fizz"
          if number % 5 == 0:
               out += "Buzz"
          if 0 not in (number % 3, number % 5):
               out += str(snake_case				)

          # print(out)
          number += 1
          out += " "
     return out


if __name__ == "__main__":
 import doctest

 doctest.testmod()


