


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


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




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     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     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     int:




						'''simple docstring'''







						if args.student_type == "roberta":
												__UpperCAmelCase			:							Any	    =	False
						elif args.student_type == "gpt2":
												__UpperCAmelCase			:							List[Any]	    =	False




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     List[str]:




						'''simple docstring'''







						if args.student_type == "roberta":
												__UpperCAmelCase			:							Union[str, Any]	    =	False




def     __SCREAMING_SNAKE_CASE							(       )		->     List[Any]:




						'''simple docstring'''







						__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=_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=0.1_5      ,       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=0.0_5      ,       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=0.0_2      ,       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.'''      )
						__UpperCAmelCase			:							str	    =	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			:							Optional[Any]	    =	MODEL_CLASSES[args.student_type]
						__UpperCAmelCase			:							Tuple	    =	MODEL_CLASSES[args.teacher_type]

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

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

						__UpperCAmelCase			:							Dict	    =	LmSeqsDataset(params=_UpperCAmelCase      ,       data=_UpperCAmelCase      )
						logger.info('''Data loader created.'''      )

						# STUDENT #
						logger.info(f"Loading student config from {args.student_config}"      )
						__UpperCAmelCase			:							List[str]	    =	student_config_class.from_pretrained(args.student_config      )
						__UpperCAmelCase			:							Optional[int]	    =	True

						if args.student_pretrained_weights is not None:
												logger.info(f"Loading pretrained weights from {args.student_pretrained_weights}"      )
												__UpperCAmelCase			:							str	    =	student_model_class.from_pretrained(args.student_pretrained_weights      ,       config=_UpperCAmelCase      )
						else:
												__UpperCAmelCase			:							List[str]	    =	student_model_class(_UpperCAmelCase      )

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

						# TEACHER #
						__UpperCAmelCase			:							Optional[int]	    =	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			:							List[Any]	    =	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 ...configuration_utils import PretrainedConfig
from ...utils import logging


A__    :   Optional[Any]           =					logging.get_logger(__name__)

A__    :   List[str]           =					{
    '''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= '''cvt'''
 def __init__(   self						:							Optional[Any]      ,     __a						:							Union[str, Any]=3      ,     __a						:							List[Any]=[7, 3, 3]      ,     __a						:							Optional[int]=[4, 2, 2]      ,     __a						:							Dict=[2, 1, 1]      ,     __a						:							Union[str, Any]=[64, 192, 384]      ,     __a						:							int=[1, 3, 6]      ,     __a						:							List[str]=[1, 2, 10]      ,     __a						:							Optional[Any]=[4.0, 4.0, 4.0]      ,     __a						:							Any=[0.0, 0.0, 0.0]      ,     __a						:							List[str]=[0.0, 0.0, 0.0]      ,     __a						:							List[Any]=[0.0, 0.0, 0.1]      ,     __a						:							List[str]=[True, True, True]      ,     __a						:							int=[False, False, True]      ,     __a						:							Dict=["dw_bn", "dw_bn", "dw_bn"]      ,     __a						:							List[str]=[3, 3, 3]      ,     __a						:							Union[str, Any]=[1, 1, 1]      ,     __a						:							Optional[int]=[2, 2, 2]      ,     __a						:							Optional[Any]=[1, 1, 1]      ,     __a						:							List[str]=[1, 1, 1]      ,     __a						:							List[str]=0.0_2      ,     __a						:							List[str]=1e-12      ,     **__a						:							List[str]      ,     )      ->   Dict:







       '''simple docstring'''
       super().__init__(**__a  )
       __snake_case :   int													=    num_channels
       __snake_case :   Union[str, Any]													=    patch_sizes
       __snake_case :   Any													=    patch_stride
       __snake_case :   List[str]													=    patch_padding
       __snake_case :   Optional[Any]													=    embed_dim
       __snake_case :   Union[str, Any]													=    num_heads
       __snake_case :   Dict													=    depth
       __snake_case :   Optional[Any]													=    mlp_ratio
       __snake_case :   List[str]													=    attention_drop_rate
       __snake_case :   Optional[int]													=    drop_rate
       __snake_case :   Optional[int]													=    drop_path_rate
       __snake_case :   Any													=    qkv_bias
       __snake_case :   int													=    cls_token
       __snake_case :   Optional[int]													=    qkv_projection_method
       __snake_case :   List[Any]													=    kernel_qkv
       __snake_case :   List[Any]													=    padding_kv
       __snake_case :   int													=    stride_kv
       __snake_case :   List[str]													=    padding_q
       __snake_case :   Dict													=    stride_q
       __snake_case :   Tuple													=    initializer_range
       __snake_case :   List[Any]													=    layer_norm_eps



