






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


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






def 						a    (		lowerCamelCase_       ):






				'''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    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''







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






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''







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






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    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=0.15	,     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=0.05	,     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=0.02	,     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.'''       )
				lowercase__     				=    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       )

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

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

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

				lowercase__     				=    LmSeqsDataset(params=__UpperCAmelCase	,     data=__UpperCAmelCase       )
				logger.info('''Data loader created.'''       )

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

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

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

				# TEACHER #
				lowercase__     				=    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()
				lowercase__     				=    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()












from __future__ import annotations






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    0.00
				lowercase__     				=    0
				for resistor in resistors:
								if resistor <= 0:
												lowercase__     				=    F"""Resistor at index {index} has a negative or zero value!"""
												raise ValueError(lowerCamelCase_       )
								first_sum += 1 / float(lowerCamelCase_       )
								index += 1
				return 1 / first_sum






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    0.00
				lowercase__     				=    0
				for resistor in resistors:
								sum_r += resistor
								if resistor < 0:
												lowercase__     				=    F"""Resistor at index {index} has a negative value!"""
												raise ValueError(lowerCamelCase_       )
								index += 1
				return sum_r


if __name__ == "__main__":
							import doctest

							doctest.testmod()




