






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





def 	lowerCAmelCase_		(lowercase__						:       int       )		->    List[Any]:


				'''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 	lowerCAmelCase_		(lowercase__						:       List[Any]     ,					lowercase__						:       Dict       )		->    Tuple:


				'''simple docstring'''




				if args.student_type == "roberta":
								lowerCAmelCase__      			=						False
				elif args.student_type == "gpt2":
								lowerCAmelCase__      			=						False





def 	lowerCAmelCase_		(lowercase__						:       str     ,					lowercase__						:       Any       )		->    Dict:


				'''simple docstring'''




				if args.student_type == "roberta":
								lowerCAmelCase__      			=						False





def 	lowerCAmelCase_		()		->    Union[str, Any]:


				'''simple docstring'''




				lowerCAmelCase__      			=						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=5_00     ,					help='''Tensorboard logging interval.'''       )
				parser.add_argument('''--checkpoint_interval'''     ,					type=lowercase__     ,					default=40_00     ,					help='''Checkpoint interval.'''       )
				lowerCAmelCase__      			=						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       )

				lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						MODEL_CLASSES[args.student_type]
				lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						MODEL_CLASSES[args.teacher_type]

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

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

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

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

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

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

				# TEACHER #
				lowerCAmelCase__      			=						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()
				lowerCAmelCase__      			=						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()













from ...configuration_utils import PretrainedConfig
class  lowerCAmelCase_       (				snake_case__					):
							UpperCamelCase_		:List[str]		      =    'bert-generation'




							def __init__(   self     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   List[str]=50_358 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=1_024 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=24 ,							SCREAMING_SNAKE_CASE_     :   Any=16 ,							SCREAMING_SNAKE_CASE_     :   Tuple=4_096 ,							SCREAMING_SNAKE_CASE_     :   str="gelu" ,							SCREAMING_SNAKE_CASE_     :   List[str]=0.1 ,							SCREAMING_SNAKE_CASE_     :   Tuple=0.1 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=512 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=0.02 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=1e-12 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=0 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=2 ,							SCREAMING_SNAKE_CASE_     :   int=1 ,							SCREAMING_SNAKE_CASE_     :   str="absolute" ,							SCREAMING_SNAKE_CASE_     :   Tuple=True ,							**SCREAMING_SNAKE_CASE_     :   str ,							):
											super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_ ,							bos_token_id=SCREAMING_SNAKE_CASE_ ,							eos_token_id=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						vocab_size
											lowerCAmelCase__      			=						hidden_size
											lowerCAmelCase__      			=						num_hidden_layers
											lowerCAmelCase__      			=						num_attention_heads
											lowerCAmelCase__      			=						hidden_act
											lowerCAmelCase__      			=						intermediate_size
											lowerCAmelCase__      			=						hidden_dropout_prob
											lowerCAmelCase__      			=						attention_probs_dropout_prob
											lowerCAmelCase__      			=						max_position_embeddings
											lowerCAmelCase__      			=						initializer_range
											lowerCAmelCase__      			=						layer_norm_eps
											lowerCAmelCase__      			=						position_embedding_type
											lowerCAmelCase__      			=						use_cache





