



"""simple docstring"""






import torch

from transformers import AutoModel






class 	__snake_case	(     torch.nn.Module):




  def __init__(			self					:						Dict						, __lowerCAmelCase					:						Any="sayef/fsner-bert-base-uncased"		):






         """simple docstring"""






         super(__lowerCAmelCase						, self		).__init__()

         _lowerCamelCase						:    Tuple         =			AutoModel.from_pretrained(__lowerCAmelCase						, return_dict=__lowerCAmelCase		)
         _lowerCamelCase						:    Any         =			torch.nn.CosineSimilarity(3						, 1E-08		)
         _lowerCamelCase						:    Optional[Any]         =			torch.nn.Softmax(dim=1		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]						, **__lowerCAmelCase					:						Dict		):






         """simple docstring"""






         return self.bert(**__lowerCAmelCase		).last_hidden_state




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]						, __lowerCAmelCase					:						Tuple		):






         """simple docstring"""






         return token_embeddings.sum(2						, keepdim=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						int						, __lowerCAmelCase					:						Dict						, __lowerCAmelCase					:						Any						, __lowerCAmelCase					:						Any=1		):






         """simple docstring"""






         return self.softmax(T * self.cos(__lowerCAmelCase						, __lowerCAmelCase		)		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple						, __lowerCAmelCase					:						str						, __lowerCAmelCase					:						Union[str, Any]		):






         """simple docstring"""






         _lowerCamelCase						:    List[str]         =			W_supports['sizes'].tolist()
         _lowerCamelCase						:    Optional[int]         =			W_supports['start_token_id'].item()
         _lowerCamelCase						:    List[Any]         =			W_supports['end_token_id'].item()

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

         _lowerCamelCase						:    Dict         =			self.BERT(**__lowerCAmelCase		)
         _lowerCamelCase						:    Union[str, Any]         =			self.BERT(**__lowerCAmelCase		)

         _lowerCamelCase						:    Optional[Any]         =			None
         _lowerCamelCase						:    Any         =			None

         _lowerCamelCase						:    int         =			W_supports['input_ids'] == start_token_id
         _lowerCamelCase						:    Optional[int]         =			W_supports['input_ids'] == end_token_id

         for i, size in enumerate(__lowerCAmelCase		):
                if i == 0:
                       _lowerCamelCase						:    Any         =			0
                else:
                       _lowerCamelCase						:    Any         =			support_sizes[i - 1]

                _lowerCamelCase						:    Optional[int]         =			S[s : s + size][start_token_masks[s : s + size]]
                _lowerCamelCase						:    int         =			S[s : s + size][end_token_masks[s : s + size]]

                _lowerCamelCase						:    List[Any]         =			torch.matmul(q[i]						, s_start.T		).sum(1		).softmax(0		)
                _lowerCamelCase						:    Tuple         =			torch.matmul(q[i]						, s_end.T		).sum(1		).softmax(0		)

                if p_starts is not None:
                       _lowerCamelCase						:    Optional[int]         =			torch.vstack((p_starts, p_start)		)
                       _lowerCamelCase						:    Dict         =			torch.vstack((p_ends, p_end)		)
                else:
                       _lowerCamelCase						:    str         =			p_start
                       _lowerCamelCase						:    Any         =			p_end

         return p_starts, p_ends










"""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 	snake_case_  (				A_ :		Any						):


       '''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 	snake_case_  (				A_ :		Dict,    A_ :		Any						):


       '''simple docstring'''





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






def 	snake_case_  (				A_ :		Optional[Any],    A_ :		List[Any]						):


       '''simple docstring'''





       if args.student_type == "roberta":
              _lowerCamelCase						:    Optional[int]         =			False






def 	snake_case_  (				):


       '''simple docstring'''





       _lowerCamelCase						:    Union[str, 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.'''						)
       _lowerCamelCase						:    List[Any]         =			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						)

       _lowerCamelCase      ,   _lowerCamelCase      ,   _lowerCamelCase						:    Any         =			MODEL_CLASSES[args.student_type]
       _lowerCamelCase      ,   _lowerCamelCase      ,   _lowerCamelCase						:    Dict         =			MODEL_CLASSES[args.teacher_type]

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

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

       _lowerCamelCase						:    Any         =			LmSeqsDataset(params=A_,    data=A_						)
       logger.info('''Data loader created.'''						)

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

       if args.student_pretrained_weights is not None:
              logger.info(F'''Loading pretrained weights from {args.student_pretrained_weights}'''						)
              _lowerCamelCase						:    Dict         =			student_model_class.from_pretrained(args.student_pretrained_weights,    config=A_						)
       else:
              _lowerCamelCase						:    Optional[Any]         =			student_model_class(A_						)

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

       # TEACHER #
       _lowerCamelCase						:    int         =			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()
       _lowerCamelCase						:    Optional[int]         =			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()


