




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






def 						_snake_case					( _snake_case							: Tuple							)   -> Union[str, 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					( _snake_case							: Any    ,  _snake_case							: List[str]							)   -> Union[str, Any]:






      '''simple docstring'''


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






def 						_snake_case					( _snake_case							: int    ,  _snake_case							: Any							)   -> Any:






      '''simple docstring'''


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






def 						_snake_case					( )   -> Union[str, Any]:






      '''simple docstring'''


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

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

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

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

      parser.add_argument('--log_interval'    ,  type=_snake_case    ,  default=5_00    ,  help='Tensorboard logging interval.'							)
      parser.add_argument('--checkpoint_interval'    ,  type=_snake_case    ,  default=40_00    ,  help='Checkpoint interval.'							)
      _A        =       parser.parse_args()
      sanity_checks(_snake_case							)

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

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

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

      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        =       pickle.load(_snake_case							)

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

      _A        =       LmSeqsDataset(params=_snake_case    ,  data=_snake_case							)
      logger.info('Data loader created.'							)

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

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

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

      # TEACHER #
      _A        =       teacher_model_class.from_pretrained(args.teacher_name    ,  output_hidden_states=_snake_case							)
      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(_snake_case    ,  _snake_case							)
      if args.freeze_token_type_embds:
            freeze_token_type_embeddings(_snake_case    ,  _snake_case							)

      # 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        =       Distiller(
          params=_snake_case    ,  dataset=_snake_case    ,  token_probs=_snake_case    ,  student=_snake_case    ,  teacher=_snake_case							)
      distiller.train()
      logger.info('Let\'s go get some drinks.'							)


if __name__ == "__main__":
       main()







"""simple docstring"""

import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


a         =      logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__							)
class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					bool				  =     field(default=__lowerCAmelCase      ,     metadata={'''help''': '''Whether to use SortishSampler or not.'''}							)
 UpperCAmelCase   :					bool				  =     field(
     default=__lowerCAmelCase      ,     metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}							)
 UpperCAmelCase   :					Optional[int]				  =     field(
     default=__lowerCAmelCase      ,     metadata={
         '''help''': (
             '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
             '''to the `max_length` value of the model configuration.'''
         )
     }      ,     )
 UpperCAmelCase   :					Optional[int]				  =     field(
     default=__lowerCAmelCase      ,     metadata={
         '''help''': (
             '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
             '''to the `num_beams` value of the model configuration.'''
         )
     }      ,     )
 UpperCAmelCase   :					Optional[Union[str, Path, GenerationConfig]]				  =     field(
     default=__lowerCAmelCase      ,     metadata={
         '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
     }      ,     )

 def       lowerCAmelCase_							(			self	:	int		):
       _A        =       super().to_dict()
       for k, v in d.items():
             if isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		):
                   _A        =       v.to_dict()
       return d

