
'''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


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




def lowercase_	(   _lowercase				)     ->							List[str]:
					'''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 lowercase_	(   _lowercase			,       _lowercase				)     ->							List[str]:
					'''simple docstring'''

					if args.student_type == "roberta":
										lowerCamelCase_					:      Optional[Any]     =		False
					elif args.student_type == "gpt2":
										lowerCamelCase_					:      List[str]     =		False




def lowercase_	(   _lowercase			,       _lowercase				)     ->							Any:
					'''simple docstring'''

					if args.student_type == "roberta":
										lowerCamelCase_					:      Dict     =		False




def lowercase_	(   )     ->							List[str]:
					'''simple docstring'''

					lowerCamelCase_					:      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=_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=500			,       help='''Tensorboard logging interval.'''				)
					parser.add_argument('''--checkpoint_interval'''			,       type=_lowercase			,       default=4_000			,       help='''Checkpoint interval.'''				)
					lowerCamelCase_					:      Any     =		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_					:      str     =		MODEL_CLASSES[args.student_type]
					lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      Optional[int]     =		MODEL_CLASSES[args.teacher_type]

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

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

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

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

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

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

					# TEACHER #
					lowerCamelCase_					:      Any     =		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_					:      Optional[Any]     =		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()




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__lowercase							:   int					=     {}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:   Any					=     ['''BartphoTokenizer''']

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

else:
					import sys

					__lowercase							:   Union[str, Any]					=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


