




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


_snake_case				:				Optional[Any]       							=		{
    "distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    "roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    "bert": (BertConfig, BertForMaskedLM, BertTokenizer),
    "gpt2": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def 		snake_case_   (UpperCamelCase	:  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 		snake_case_   (UpperCamelCase	:  Union[str, Any]			,						UpperCamelCase	:  Any				):





							'''simple docstring'''


							if args.student_type == "roberta":
														_a     					=    False
							elif args.student_type == "gpt2":
														_a     					=    False
def 		snake_case_   (UpperCamelCase	:  Union[str, Any]			,						UpperCamelCase	:  List[str]				):





							'''simple docstring'''


							if args.student_type == "roberta":
														_a     					=    False
def 		snake_case_   ():





							'''simple docstring'''


							_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=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=500			,						help='''Tensorboard logging interval.'''				)
							parser.add_argument('''--checkpoint_interval'''			,						type=UpperCamelCase			,						default=4000			,						help='''Checkpoint interval.'''				)
							_a     					=    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				)

							_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(UpperCamelCase				)
														_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(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:
																					_a     					=    pickle.load(UpperCamelCase				)

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

							_a     					=    LmSeqsDataset(params=UpperCamelCase			,						data=UpperCamelCase				)
							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=UpperCamelCase				)
							else:
														_a     					=    student_model_class(UpperCamelCase				)

							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=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()
							_a     					=    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 ..utils import DummyObject, requires_backends

class       A   (		metaclass=_a   ):
							lowercase_          =    ['torch', 'scipy']







							def __init__(  self	:  Tuple							,      *lowerCAmelCase_	:  str							,      **lowerCAmelCase_	:  Any )      ->							Tuple:


														"""simple docstring"""


														requires_backends(self							,      ['''torch''', '''scipy'''] )







							@classmethod
							def        __lowerCAmelCase      (  cls	:  Dict							,      *lowerCAmelCase_	:  Dict							,      **lowerCAmelCase_	:  str )      ->							Tuple:


														"""simple docstring"""


														requires_backends(cls							,      ['''torch''', '''scipy'''] )







							@classmethod
							def        __lowerCAmelCase      (  cls	:  Optional[int]							,      *lowerCAmelCase_	:  str							,      **lowerCAmelCase_	:  List[str] )      ->							str:


														"""simple docstring"""


														requires_backends(cls							,      ['''torch''', '''scipy'''] )

