
"""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


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




def       A_  (				__lowercase	):
 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       A_  (				__lowercase						,	__lowercase	):
 if args.student_type == "roberta":
  UpperCamelCase_     :					Dict   	=False
 elif args.student_type == "gpt2":
  UpperCamelCase_     :					Optional[Any]   	=False




def       A_  (				__lowercase						,	__lowercase	):
 if args.student_type == "roberta":
  UpperCamelCase_     :					str   	=False




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

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

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

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

 parser.add_argument('--log_interval'						,	type=__lowercase						,	default=5_00						,	help='Tensorboard logging interval.'	)
 parser.add_argument('--checkpoint_interval'						,	type=__lowercase						,	default=40_00						,	help='Checkpoint interval.'	)
 UpperCamelCase_     :					List[str]   	=parser.parse_args()
 sanity_checks(__lowercase	)

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

 UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_     :					str   	=MODEL_CLASSES[args.student_type]
 UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_     :					Optional[Any]   	=MODEL_CLASSES[args.teacher_type]

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

 if args.mlm:
  logger.info(F'''Loading token counts from {args.token_counts} (already pre-computed)'''	)
  with open(args.token_counts						,	'rb'	) as fp:
   UpperCamelCase_     :					Dict   	=pickle.load(__lowercase	)

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

 UpperCamelCase_     :					List[str]   	=LmSeqsDataset(params=__lowercase						,	data=__lowercase	)
 logger.info('Data loader created.'	)

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

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

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

 # TEACHER #
 UpperCamelCase_     :					List[Any]   	=teacher_model_class.from_pretrained(args.teacher_name						,	output_hidden_states=__lowercase	)
 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(__lowercase						,	__lowercase	)
 if args.freeze_token_type_embds:
  freeze_token_type_embeddings(__lowercase						,	__lowercase	)

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


if __name__ == "__main__":
 main()


"""simple docstring"""






# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')





class 				a__					:



     def __init__(							self :List[str]      ,  _lowerCamelCase :Any      ,  _lowerCamelCase :Optional[Any]      ,  _lowerCamelCase :bool = True      ,  _lowerCamelCase :bool = False   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=scheduler
      UpperCamelCase_     :					Any   	=optimizers if isinstance(_lowerCamelCase      ,  (list, tuple)   ) else [optimizers]
      UpperCamelCase_     :					Dict   	=split_batches
      UpperCamelCase_     :					str   	=step_with_optimizer
      UpperCamelCase_     :					Dict   	=GradientState()



     def  lowerCamelCase_						(							self :Optional[int]      ,  *_lowerCamelCase :str      ,  **_lowerCamelCase :Union[str, Any]   ):







      '''simple docstring'''



      if not self.step_with_optimizer:
       # No link between scheduler and optimizer -> just step
       self.scheduler.step(*_lowerCamelCase      ,  **_lowerCamelCase   )
       return

      # Otherwise, first make sure the optimizer was stepped.
      if not self.gradient_state.sync_gradients:
       if self.gradient_state.adjust_scheduler:
        self.scheduler._step_count += 1
       return

      for opt in self.optimizers:
       if opt.step_was_skipped:
        return
      if self.split_batches:
       # Split batches -> the training dataloader batch size is not changed so one step per training step
       self.scheduler.step(*_lowerCamelCase      ,  **_lowerCamelCase   )
      else:
       # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
       # num_processes steps per training step
       UpperCamelCase_     :					Optional[Any]   	=AcceleratorState().num_processes
       for _ in range(_lowerCamelCase   ):
        # Special case when using OneCycle and `drop_last` was not used
        if hasattr(self.scheduler      ,  'total_steps'   ):
         if self.scheduler._step_count <= self.scheduler.total_steps:
          self.scheduler.step(*_lowerCamelCase      ,  **_lowerCamelCase   )
        else:
         self.scheduler.step(*_lowerCamelCase      ,  **_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Dict   ):







      '''simple docstring'''



      return self.scheduler.get_last_lr()



     def  lowerCamelCase_						(							self :Tuple   ):







      '''simple docstring'''



      return self.scheduler.state_dict()



     def  lowerCamelCase_						(							self :Any      ,  _lowerCamelCase :Any   ):







      '''simple docstring'''



      self.scheduler.load_state_dict(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Any   ):







      '''simple docstring'''



      return self.scheduler.get_lr()




     def  lowerCamelCase_						(							self :Optional[int]      ,  *_lowerCamelCase :Union[str, Any]      ,  **_lowerCamelCase :Optional[int]   ):







      '''simple docstring'''



      return self.scheduler.print_lr(*_lowerCamelCase      ,  **_lowerCamelCase   )
