





"""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


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





def 	a_    (							_lowercase       ):
						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_    (							_lowercase	,						_lowercase       ):
						if args.student_type == "roberta":
												_UpperCamelCase      : int         =   False
						elif args.student_type == "gpt2":
												_UpperCamelCase      : Any         =   False





def 	a_    (							_lowercase	,						_lowercase       ):
						if args.student_type == "roberta":
												_UpperCamelCase      : int         =   False





def 	a_    (							):
						_UpperCamelCase      : Dict         =   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=_lowercase	,						required=_lowercase	,						help='''The output directory (log, checkpoints, parameters, etc.)'''       )
						parser.add_argument(
						    '''--data_file'''	,						type=_lowercase	,						required=_lowercase	,						help='''The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'''	,						)

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

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

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

						parser.add_argument('''--log_interval'''	,						type=_lowercase	,						default=500	,						help='''Tensorboard logging interval.'''       )
						parser.add_argument('''--checkpoint_interval'''	,						type=_lowercase	,						default=4000	,						help='''Checkpoint interval.'''       )
						_UpperCamelCase      : Dict         =   parser.parse_args()
						sanity_checks(_lowercase       )

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

						_UpperCamelCase      : Dict         =   MODEL_CLASSES[args.student_type]
						_UpperCamelCase      : int         =   MODEL_CLASSES[args.teacher_type]

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

						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      : List[str]         =   pickle.load(_lowercase       )

												_UpperCamelCase      : Dict         =   np.maximum(_lowercase	,						1       ) ** -args.mlm_smoothing
												for idx in special_tok_ids.values():
																		_UpperCamelCase      : Optional[int]         =   0.0  # do not predict special tokens
												_UpperCamelCase      : List[Any]         =   torch.from_numpy(_lowercase       )
						else:
												_UpperCamelCase      : Optional[int]         =   None

						_UpperCamelCase      : str         =   LmSeqsDataset(params=_lowercase	,						data=_lowercase       )
						logger.info('''Data loader created.'''       )

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

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

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

						# TEACHER #
						_UpperCamelCase      : Tuple         =   teacher_model_class.from_pretrained(args.teacher_name	,						output_hidden_states=_lowercase       )
						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(_lowercase	,						_lowercase       )
						if args.freeze_token_type_embds:
												freeze_token_type_embeddings(_lowercase	,						_lowercase       )

						# 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      : Union[str, Any]         =   Distiller(
						    params=_lowercase	,						dataset=_lowercase	,						token_probs=_lowercase	,						student=_lowercase	,						teacher=_lowercase       )
						distiller.train()
						logger.info('''Let\'s go get some drinks.'''       )


if __name__ == "__main__":
						main()












"""simple docstring"""




from functools import lru_cache





@lru_cache
def 	a_    (							_lowercase       ):
						if num < 0:
												raise ValueError('''Number should not be negative.'''       )

						return 1 if num in (0, 1) else num * factorial(num - 1       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



