



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


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






def      lowerCAmelCase__		(   lowerCamelCase_		:		int):





				'''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      lowerCAmelCase__		(   lowerCamelCase_		:		Union[str, Any]	,lowerCamelCase_		:		Any):





				'''simple docstring'''

				if args.student_type == "roberta":
								lowerCAmelCase__					:    Optional[int]												=					False
				elif args.student_type == "gpt2":
								lowerCAmelCase__					:    Tuple												=					False






def      lowerCAmelCase__		(   lowerCamelCase_		:		Union[str, Any]	,lowerCamelCase_		:		List[str]):





				'''simple docstring'''

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






def      lowerCAmelCase__		(   ):





				'''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=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=500	,help='''Tensorboard logging interval.''')
				parser.add_argument('''--checkpoint_interval'''	,type=lowerCamelCase_	,default=4000	,help='''Checkpoint interval.''')
				lowerCAmelCase__					:    str												=					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)

				lowerCAmelCase__					:    Optional[Any]												=					MODEL_CLASSES[args.student_type]
				lowerCAmelCase__					:    Optional[int]												=					MODEL_CLASSES[args.teacher_type]

				# TOKENIZER #
				lowerCAmelCase__					:    int												=					teacher_tokenizer_class.from_pretrained(args.teacher_name)
				lowerCAmelCase__					:    int												=					{}
				for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
								lowerCAmelCase__					:    Optional[int]												=					tokenizer.all_special_tokens.index(lowerCamelCase_)
								lowerCAmelCase__					:    Optional[Any]												=					tokenizer.all_special_ids[idx]
				logger.info(f"""Special tokens {special_tok_ids}""")
				lowerCAmelCase__					:    Tuple												=					special_tok_ids
				lowerCAmelCase__					:    int												=					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__					:    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:
												lowerCAmelCase__					:    Optional[Any]												=					pickle.load(lowerCamelCase_)

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

				lowerCAmelCase__					:    Union[str, Any]												=					LmSeqsDataset(params=lowerCamelCase_	,data=lowerCamelCase_)
				logger.info('''Data loader created.''')

				# STUDENT #
				logger.info(f"""Loading student config from {args.student_config}""")
				lowerCAmelCase__					:    List[str]												=					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[int]												=					student_model_class.from_pretrained(args.student_pretrained_weights	,config=lowerCamelCase_)
				else:
								lowerCAmelCase__					:    int												=					student_model_class(lowerCamelCase_)

				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=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()
				lowerCAmelCase__					:    List[Any]												=					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()










import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize






def      lowerCAmelCase__		(   lowerCamelCase_		:		ndarray):





				'''simple docstring'''

				return np.dot(lowerCamelCase_	,lowerCamelCase_)



class 	lowerCamelCase__      :




		'''simple docstring'''



		def __init__(self		,*,
		    __lowerCamelCase = np.inf		,__lowerCamelCase = "linear"		,__lowerCamelCase = 0.0		,)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					regularization
						lowerCAmelCase__					:    str												=					gamma
						if kernel == "linear":
										lowerCAmelCase__					:    Dict												=					self.__linear
						elif kernel == "rbf":
										if self.gamma == 0:
														raise ValueError('''rbf kernel requires gamma'''			)
										if not isinstance(self.gamma		,(float, int)			):
														raise ValueError('''gamma must be float or int'''			)
										if not self.gamma > 0:
														raise ValueError('''gamma must be > 0'''			)
										lowerCAmelCase__					:    Optional[Any]												=					self.__rbf
										# in the future, there could be a default value like in sklearn
										# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
										# previously it was 1/(n_features)
						else:
										lowerCAmelCase__					:    List[str]												=					f"""Unknown kernel: {kernel}"""
										raise ValueError(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      float:

						"""simple docstring"""






						return np.dot(__lowerCamelCase		,__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      float:

						"""simple docstring"""






						return np.exp(-(self.gamma * norm_squared(vectora - vectora			))			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					observations
						lowerCAmelCase__					:    Optional[int]												=					classes

						# using Wolfe's Dual to calculate w.
						# Primal problem: minimize 1/2*norm_squared(w)
						#   constraint: yn(w . xn + b) >= 1
						#
						# With l a vector
						# Dual problem: maximize sum_n(ln) -
						#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
						#   constraint: self.C >= ln >= 0
						#           and sum_n(ln*yn) = 0
						# Then we get w using w = sum_n(ln*yn*xn)
						# At the end we can get b ~= mean(yn - w . xn)
						#
						# Since we use kernels, we only need l_star to calculate b
						# and to classify observations

						((lowerCAmelCase__)						,		)					:    List[str]												=					np.shape(__lowerCamelCase			)

						def to_minimize(__lowerCamelCase			) -> float:
										lowerCAmelCase__					:    List[str]												=					0
										((lowerCAmelCase__)						,		)					:    str												=					np.shape(__lowerCamelCase			)
										for i in range(__lowerCamelCase			):
														for j in range(__lowerCamelCase			):
																		s += (
																		    candidate[i]
																		    * candidate[j]
																		    * classes[i]
																		    * classes[j]
																		    * self.kernel(observations[i]		,observations[j]			)
																		)
										return 1 / 2 * s - sum(__lowerCamelCase			)

						lowerCAmelCase__					:    List[str]												=					LinearConstraint(__lowerCamelCase		,0		,0			)
						lowerCAmelCase__					:    List[str]												=					Bounds(0		,self.regularization			)

						lowerCAmelCase__					:    int												=					minimize(
						    __lowerCamelCase		,np.ones(__lowerCamelCase			)		,bounds=__lowerCamelCase		,constraints=[ly_contraint]			).x
						lowerCAmelCase__					:    List[Any]												=					l_star

						# calculating mean offset of separation plane to points
						lowerCAmelCase__					:    Optional[Any]												=					0
						for i in range(__lowerCamelCase			):
										for j in range(__lowerCamelCase			):
														s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
														    observations[i]		,observations[j]			)
						lowerCAmelCase__					:    Dict												=					s / n



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					sum(
						    self.optimum[n]
						    * self.classes[n]
						    * self.kernel(self.observations[n]		,__lowerCamelCase			)
						    for n in range(len(self.classes			)			)			)
						return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
							import doctest

							doctest.testmod()

