


'''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


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





def      UpperCAmelCase      (		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      UpperCAmelCase      (		lowerCamelCase_  :List[str]				,      lowerCamelCase_  :Optional[Any] ):







		'''simple docstring'''






		if args.student_type == "roberta":
				snake_case_		:							Dict			       =						False
		elif args.student_type == "gpt2":
				snake_case_		:							Union[str, Any]			       =						False





def      UpperCAmelCase      (		lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :Dict ):







		'''simple docstring'''






		if args.student_type == "roberta":
				snake_case_		:							Any			       =						False





def      UpperCAmelCase      (		):







		'''simple docstring'''
		snake_case_		:							Optional[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=_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=5_00				,      help="""Tensorboard logging interval.""" )
		parser.add_argument("""--checkpoint_interval"""				,      type=_UpperCamelCase				,      default=40_00				,      help="""Checkpoint interval.""" )
		snake_case_		:							int			       =						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 )

		snake_case_       ,      snake_case_       ,      snake_case_		:							List[str]			       =						MODEL_CLASSES[args.student_type]
		snake_case_       ,      snake_case_       ,      snake_case_		:							int			       =						MODEL_CLASSES[args.teacher_type]

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

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

		snake_case_		:							str			       =						LmSeqsDataset(params=_UpperCamelCase				,      data=_UpperCamelCase )
		logger.info("""Data loader created.""" )

		# STUDENT #
		logger.info(F'''Loading student config from {args.student_config}''' )
		snake_case_		:							Dict			       =						student_config_class.from_pretrained(args.student_config )
		snake_case_		:							Union[str, Any]			       =						True

		if args.student_pretrained_weights is not None:
				logger.info(F'''Loading pretrained weights from {args.student_pretrained_weights}''' )
				snake_case_		:							Any			       =						student_model_class.from_pretrained(args.student_pretrained_weights				,      config=_UpperCamelCase )
		else:
				snake_case_		:							Any			       =						student_model_class(_UpperCamelCase )

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

		# TEACHER #
		snake_case_		:							List[str]			       =						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()
		snake_case_		:							Union[str, Any]			       =						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()



'''simple docstring'''






import torch

from transformers import AutoModel
class 						__UpperCamelCase	(						torch.nn.Module							):





							def __init__(  self		:Union[str, Any]  ,_UpperCamelCase		:Tuple="sayef/fsner-bert-base-uncased"			):
									super(_UpperCamelCase  ,self			).__init__()

									snake_case_		:							Dict			       =						AutoModel.from_pretrained(_UpperCamelCase  ,return_dict=_UpperCamelCase			)
									snake_case_		:							Dict			       =						torch.nn.CosineSimilarity(3  ,1E-0_8			)
									snake_case_		:							int			       =						torch.nn.Softmax(dim=1			)





							def 		a__   (  self		:Optional[Any]  ,**_UpperCamelCase		:Optional[Any]			):
									return self.bert(**_UpperCamelCase			).last_hidden_state





							def 		a__   (  self		:List[Any]  ,_UpperCamelCase		:Dict			):
									return token_embeddings.sum(2  ,keepdim=_UpperCamelCase			)





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:List[str]=1			):
									return self.softmax(T * self.cos(_UpperCamelCase  ,_UpperCamelCase			)			)





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:int			):
									snake_case_		:							Union[str, Any]			       =						W_supports["""sizes"""].tolist()
									snake_case_		:							Dict			       =						W_supports["""start_token_id"""].item()
									snake_case_		:							int			       =						W_supports["""end_token_id"""].item()

									del W_supports["sizes"]
									del W_supports["start_token_id"]
									del W_supports["end_token_id"]

									snake_case_		:							int			       =						self.BERT(**_UpperCamelCase			)
									snake_case_		:							int			       =						self.BERT(**_UpperCamelCase			)

									snake_case_		:							Any			       =						None
									snake_case_		:							Any			       =						None

									snake_case_		:							Union[str, Any]			       =						W_supports["""input_ids"""] == start_token_id
									snake_case_		:							Tuple			       =						W_supports["""input_ids"""] == end_token_id

									for i, size in enumerate(_UpperCamelCase			):
											if i == 0:
													snake_case_		:							str			       =						0
											else:
													snake_case_		:							Optional[Any]			       =						support_sizes[i - 1]

											snake_case_		:							Dict			       =						S[s : s + size][start_token_masks[s : s + size]]
											snake_case_		:							Optional[Any]			       =						S[s : s + size][end_token_masks[s : s + size]]

											snake_case_		:							int			       =						torch.matmul(q[i]  ,s_start.T			).sum(1			).softmax(0			)
											snake_case_		:							List[str]			       =						torch.matmul(q[i]  ,s_end.T			).sum(1			).softmax(0			)

											if p_starts is not None:
													snake_case_		:							List[str]			       =						torch.vstack((p_starts, p_start)			)
													snake_case_		:							Tuple			       =						torch.vstack((p_ends, p_end)			)
											else:
													snake_case_		:							Any			       =						p_start
													snake_case_		:							List[str]			       =						p_end

									return p_starts, p_ends