


'''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 		_UpperCAmelCase   (							__A							:   Optional[int]      ):
				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 		_UpperCAmelCase   (							__A							:   List[str]						,		__A							:   List[Any]      ):
				if args.student_type == "roberta":
								a_		:  int											=      False
				elif args.student_type == "gpt2":
								a_		:  List[Any]											=      False





def 		_UpperCAmelCase   (							__A							:   Union[str, Any]						,		__A							:   str      ):
				if args.student_type == "roberta":
								a_		:  Optional[Any]											=      False





def 		_UpperCAmelCase   (							):
				a_		:  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=__A						,		required=__A						,		help='''The output directory (log, checkpoints, parameters, etc.)'''      )
				parser.add_argument(
				    '''--data_file'''						,		type=__A						,		required=__A						,		help='''The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'''						,		)

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

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

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

				parser.add_argument('''--log_interval'''						,		type=__A						,		default=5_00						,		help='''Tensorboard logging interval.'''      )
				parser.add_argument('''--checkpoint_interval'''						,		type=__A						,		default=40_00						,		help='''Checkpoint interval.'''      )
				a_		:  str											=      parser.parse_args()
				sanity_checks(__A      )

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

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

				# TOKENIZER #
				a_		:  List[str]											=      teacher_tokenizer_class.from_pretrained(args.teacher_name      )
				a_		:  str											=      {}
				for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
								a_		:  str											=      tokenizer.all_special_tokens.index(__A      )
								a_		:  str											=      tokenizer.all_special_ids[idx]
				logger.info(f'Special tokens {special_tok_ids}'      )
				a_		:  Union[str, Any]											=      special_tok_ids
				a_		:  Optional[Any]											=      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:
								a_		:  Union[str, Any]											=      pickle.load(__A      )

				if args.mlm:
								logger.info(f'Loading token counts from {args.token_counts} (already pre-computed)'      )
								with open(args.token_counts						,		'''rb'''      ) as fp:
												a_		:  Optional[int]											=      pickle.load(__A      )

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

				a_		:  Optional[int]											=      LmSeqsDataset(params=__A						,		data=__A      )
				logger.info('''Data loader created.'''      )

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

				if args.student_pretrained_weights is not None:
								logger.info(f'Loading pretrained weights from {args.student_pretrained_weights}'      )
								a_		:  Any											=      student_model_class.from_pretrained(args.student_pretrained_weights						,		config=__A      )
				else:
								a_		:  List[Any]											=      student_model_class(__A      )

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

				# TEACHER #
				a_		:  Tuple											=      teacher_model_class.from_pretrained(args.teacher_name						,		output_hidden_states=__A      )
				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(__A						,		__A      )
				if args.freeze_token_type_embds:
								freeze_token_type_embeddings(__A						,		__A      )

				# 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()
				a_		:  Optional[Any]											=      Distiller(
				    params=__A						,		dataset=__A						,		token_probs=__A						,		student=__A						,		teacher=__A      )
				distiller.train()
				logger.info('''Let\'s go get some drinks.'''      )


if __name__ == "__main__":
		main()












'''simple docstring'''







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase       		=      logging.get_logger(__name__)

__lowerCAmelCase       		=      {'vocab_file': 'sentencepiece.bpe.model'}

__lowerCAmelCase       		=      {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

__lowerCAmelCase       		=      {
    'moussaKam/mbarthez': 1_024,
    'moussaKam/barthez': 1_024,
    'moussaKam/barthez-orangesum-title': 1_024,
}

__lowerCAmelCase       		=      '▁'





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				VOCAB_FILES_NAMES
			snake_case__  =				PRETRAINED_VOCAB_FILES_MAP
			snake_case__  =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case__  =				["input_ids", "attention_mask"]






			def __init__( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			Dict="<s>"  ,						__SCREAMING_SNAKE_CASE      :			List[Any]="</s>"  ,						__SCREAMING_SNAKE_CASE      :			List[str]="</s>"  ,						__SCREAMING_SNAKE_CASE      :			List[str]="<s>"  ,						__SCREAMING_SNAKE_CASE      :			Dict="<unk>"  ,						__SCREAMING_SNAKE_CASE      :			int="<pad>"  ,						__SCREAMING_SNAKE_CASE      :			Tuple="<mask>"  ,						__SCREAMING_SNAKE_CASE      :			Optional[Dict[str, Any]] = None  ,						**__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						)			->			None:
							# Mask token behave like a normal word, i.e. include the space before it
							a_		:  Tuple											=      AddedToken(__SCREAMING_SNAKE_CASE  ,						lstrip=__SCREAMING_SNAKE_CASE  ,						rstrip=__SCREAMING_SNAKE_CASE					) if isinstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					) else mask_token

							a_		:  List[str]											=      {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=__SCREAMING_SNAKE_CASE  ,						eos_token=__SCREAMING_SNAKE_CASE  ,						unk_token=__SCREAMING_SNAKE_CASE  ,						sep_token=__SCREAMING_SNAKE_CASE  ,						cls_token=__SCREAMING_SNAKE_CASE  ,						pad_token=__SCREAMING_SNAKE_CASE  ,						mask_token=__SCREAMING_SNAKE_CASE  ,						sp_model_kwargs=self.sp_model_kwargs  ,						**__SCREAMING_SNAKE_CASE  ,						)

							a_		:  Tuple											=      vocab_file
							a_		:  Optional[Any]											=      spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(str(__SCREAMING_SNAKE_CASE					)					)

							a_		:  Optional[Any]											=      {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

							a_		:  Any											=      len(self.sp_model					) - 1
							a_		:  Optional[Any]											=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			List[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[List[int]] = None					)			->			List[int]:

							if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							a_		:  List[str]											=      [self.cls_token_id]
							a_		:  Optional[Any]											=      [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			List[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[List[int]] = None  ,						__SCREAMING_SNAKE_CASE      :			bool = False					)			->			List[int]:
							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=__SCREAMING_SNAKE_CASE  ,						token_ids_a=__SCREAMING_SNAKE_CASE  ,						already_has_special_tokens=__SCREAMING_SNAKE_CASE					)

							if token_ids_a is None:
											return [1] + ([0] * len(__SCREAMING_SNAKE_CASE					)) + [1]
							return [1] + ([0] * len(__SCREAMING_SNAKE_CASE					)) + [1, 1] + ([0] * len(__SCREAMING_SNAKE_CASE					)) + [1]






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			List[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[List[int]] = None					)			->			List[int]:
							a_		:  List[str]											=      [self.sep_token_id]
							a_		:  Any											=      [self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep					) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Union[str, Any]:
							return len(self.sp_model					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Tuple:
							a_		:  int											=      {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE					): i for i in range(self.vocab_size					)}
							vocab.update(self.added_tokens_encoder					)
							return vocab






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			str					)			->			List[str]:
							return self.sp_model.encode(__SCREAMING_SNAKE_CASE  ,						out_type=__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Dict:
							if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
							a_		:  Optional[int]											=      self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE					)

							return spm_id if spm_id else self.unk_token_id






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			int					)			->			str:
							if index in self.fairseq_ids_to_tokens:
											return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			Optional[int]:
							a_		:  Dict											=      []
							a_		:  List[Any]											=      ''''''
							a_		:  Dict											=      False
							for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
															if not prev_is_special:
																			out_string += " "
															out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE					) + token
															a_		:  Dict											=      True
															a_		:  Optional[Any]											=      []
											else:
															current_sub_tokens.append(__SCREAMING_SNAKE_CASE					)
															a_		:  Tuple											=      False
							out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE					)
							return out_string.strip()






			def __getstate__( self      :			Dict					)			->			int:
							a_		:  Dict											=      self.__dict__.copy()
							a_		:  List[str]											=      None
							return state






			def __setstate__( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			Any:
							a_		:  Optional[Any]											=      d

							# for backward compatibility
							if not hasattr(self  ,						'''sp_model_kwargs'''					):
											a_		:  Union[str, Any]											=      {}

							a_		:  int											=      spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(self.vocab_file					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[str] = None					)			->			Tuple[str]:
							if not os.path.isdir(__SCREAMING_SNAKE_CASE					):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
											return
							a_		:  Union[str, Any]											=      os.path.join(
							    __SCREAMING_SNAKE_CASE  ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

							if os.path.abspath(self.vocab_file					) != os.path.abspath(__SCREAMING_SNAKE_CASE					) and os.path.isfile(self.vocab_file					):
											copyfile(self.vocab_file  ,						__SCREAMING_SNAKE_CASE					)
							elif not os.path.isfile(self.vocab_file					):
											with open(__SCREAMING_SNAKE_CASE  ,						'''wb'''					) as fi:
															a_		:  Any											=      self.sp_model.serialized_model_proto()
															fi.write(__SCREAMING_SNAKE_CASE					)

							return (out_vocab_file,)




