



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


def        lowerCAmelCase_     (			_lowerCamelCase:     List[Any]      ):
		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_     (			_lowerCamelCase:     Optional[Any] ,   _lowerCamelCase:     List[Any]      ):
		if args.student_type == "roberta":
				__SCREAMING_SNAKE_CASE :	List[str]					 =	False
		elif args.student_type == "gpt2":
				__SCREAMING_SNAKE_CASE :	Dict					 =	False


def        lowerCAmelCase_     (			_lowerCamelCase:     List[Any] ,   _lowerCamelCase:     str      ):
		if args.student_type == "roberta":
				__SCREAMING_SNAKE_CASE :	str					 =	False


def        lowerCAmelCase_     (			):
		__SCREAMING_SNAKE_CASE :	Any					 =	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=_lowerCamelCase ,   required=_lowerCamelCase ,   help="""The output directory (log, checkpoints, parameters, etc.)"""      )
		parser.add_argument(
		    """--data_file""" ,   type=_lowerCamelCase ,   required=_lowerCamelCase ,   help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence.""" ,   )

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

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

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

		parser.add_argument("""--log_interval""" ,   type=_lowerCamelCase ,   default=5_00 ,   help="""Tensorboard logging interval."""      )
		parser.add_argument("""--checkpoint_interval""" ,   type=_lowerCamelCase ,   default=40_00 ,   help="""Checkpoint interval."""      )
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	parser.parse_args()
		sanity_checks(_lowerCamelCase      )

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

		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	str					 =	MODEL_CLASSES[args.student_type]
		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	List[str]					 =	MODEL_CLASSES[args.teacher_type]

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

		if args.mlm:
				logger.info(F"Loading token counts from {args.token_counts} (already pre-computed)"      )
				with open(args.token_counts ,   """rb"""      ) as fp:
						__SCREAMING_SNAKE_CASE :	List[str]					 =	pickle.load(_lowerCamelCase      )

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

		__SCREAMING_SNAKE_CASE :	List[Any]					 =	LmSeqsDataset(params=_lowerCamelCase ,   data=_lowerCamelCase      )
		logger.info("""Data loader created."""      )

		# STUDENT #
		logger.info(F"Loading student config from {args.student_config}"      )
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	student_config_class.from_pretrained(args.student_config      )
		__SCREAMING_SNAKE_CASE :	Tuple					 =	True

		if args.student_pretrained_weights is not None:
				logger.info(F"Loading pretrained weights from {args.student_pretrained_weights}"      )
				__SCREAMING_SNAKE_CASE :	int					 =	student_model_class.from_pretrained(args.student_pretrained_weights ,   config=_lowerCamelCase      )
		else:
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	student_model_class(_lowerCamelCase      )

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

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

		# 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()
		__SCREAMING_SNAKE_CASE :	Dict					 =	Distiller(
		    params=_lowerCamelCase ,   dataset=_lowerCamelCase ,   token_probs=_lowerCamelCase ,   student=_lowerCamelCase ,   teacher=_lowerCamelCase      )
		distiller.train()
		logger.info("""Let's go get some drinks."""      )


if __name__ == "__main__":
						main()




'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


UpperCamelCase__ :			List[str]												=				logging.get_logger(__name__)






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			def __init__(							self  :				Union[str, Any]							,  *lowerCAmelCase__  :				str							,  **lowerCAmelCase__  :				Any		):





					"""simple docstring"""






					warnings.warn(
					    """The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
					    """ use DeiTImageProcessor instead."""							,  lowerCAmelCase__							,  )
					super().__init__(*lowerCAmelCase__							,  **lowerCAmelCase__		)