





"""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


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


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""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       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


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


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


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


def       a__					(					):




		"""simple docstring"""


		UpperCamelCase   =	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=_SCREAMING_SNAKE_CASE  ,	required=_SCREAMING_SNAKE_CASE  ,	help="The output directory (log, checkpoints, parameters, etc.)"    )
		parser.add_argument(
		    "--data_file"  ,	type=_SCREAMING_SNAKE_CASE  ,	required=_SCREAMING_SNAKE_CASE  ,	help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence."  ,	)

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

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

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

		parser.add_argument("--log_interval"  ,	type=_SCREAMING_SNAKE_CASE  ,	default=500  ,	help="Tensorboard logging interval."    )
		parser.add_argument("--checkpoint_interval"  ,	type=_SCREAMING_SNAKE_CASE  ,	default=4_000  ,	help="Checkpoint interval."    )
		UpperCamelCase   =	parser.parse_args()
		sanity_checks(_SCREAMING_SNAKE_CASE    )

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

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

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

		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   =	pickle.load(_SCREAMING_SNAKE_CASE    )

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

		UpperCamelCase   =	LmSeqsDataset(params=_SCREAMING_SNAKE_CASE  ,	data=_SCREAMING_SNAKE_CASE    )
		logger.info("Data loader created."    )

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

		if args.student_pretrained_weights is not None:
				logger.info(F"Loading pretrained weights from {args.student_pretrained_weights}"    )
				UpperCamelCase   =	student_model_class.from_pretrained(args.student_pretrained_weights  ,	config=_SCREAMING_SNAKE_CASE    )
		else:
				UpperCamelCase   =	student_model_class(_SCREAMING_SNAKE_CASE    )

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

		# TEACHER #
		UpperCamelCase   =	teacher_model_class.from_pretrained(args.teacher_name  ,	output_hidden_states=_SCREAMING_SNAKE_CASE    )
		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(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )
		if args.freeze_token_type_embds:
				freeze_token_type_embeddings(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )

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


if __name__ == "__main__":
						main()













"""simple docstring"""





def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		return number & 1 == 0


if __name__ == "__main__":
						import doctest

						doctest.testmod()






