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




def       _snake_case	(   lowerCAmelCase							:   Optional[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       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   Optional[int]	):
		"""simple docstring"""







		if args.student_type == "roberta":
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    False
		elif args.student_type == "gpt2":
				SCREAMING_SNAKE_CASE_  :		int				      =    False




def       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   int	):
		"""simple docstring"""







		if args.student_type == "roberta":
				SCREAMING_SNAKE_CASE_  :		str				      =    False




def       _snake_case	(   ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    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=5_0   , 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=5_6   , help="Random seed"	)

		parser.add_argument("--log_interval"   , type=lowerCAmelCase   , default=5_0_0   , help="Tensorboard logging interval."	)
		parser.add_argument("--checkpoint_interval"   , type=lowerCAmelCase   , default=4_0_0_0   , help="Checkpoint interval."	)
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    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_  :		List[Any]				      =    MODEL_CLASSES[args.student_type]
		SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		str				      =    MODEL_CLASSES[args.teacher_type]

		# TOKENIZER #
		SCREAMING_SNAKE_CASE_  :		Dict				      =    teacher_tokenizer_class.from_pretrained(args.teacher_name	)
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    {}
		for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
				SCREAMING_SNAKE_CASE_  :		List[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_  :		Tuple				      =    special_tok_ids
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    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_  :		Optional[int]				      =    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_  :		str				      =    pickle.load(lowerCAmelCase	)

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

		SCREAMING_SNAKE_CASE_  :		List[str]				      =    LmSeqsDataset(params=lowerCAmelCase   , data=lowerCAmelCase	)
		logger.info("Data loader created."	)

		# STUDENT #
		logger.info(f'Loading student config from {args.student_config}'	)
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    student_config_class.from_pretrained(args.student_config	)
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    True

		if args.student_pretrained_weights is not None:
				logger.info(f'Loading pretrained weights from {args.student_pretrained_weights}'	)
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    student_model_class.from_pretrained(args.student_pretrained_weights   , config=lowerCAmelCase	)
		else:
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    student_model_class(lowerCAmelCase	)

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

		# TEACHER #
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    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_  :		int				      =    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()








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


A__	:			Optional[int]			   = {}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__	:			Optional[int]			   = ['GPTSw3Tokenizer']


if TYPE_CHECKING:
							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_gpt_swa import GPTSwaTokenizer

else:
							import sys

							A__	:			Optional[int]			   = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




