




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




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Any:
   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 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Tuple:
   if args.student_type == "roberta":
      snake_case				:    int           =	False
   elif args.student_type == "gpt2":
      snake_case				:    Any           =	False




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       List[str]:
   if args.student_type == "roberta":
      snake_case				:    Optional[Any]           =	False




def 						SCREAMING_SNAKE_CASE__ (   )  ->       Any:
   snake_case				:    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=500						,help="""Tensorboard logging interval."""					)
   parser.add_argument("""--checkpoint_interval"""						,type=lowercase						,default=4000						,help="""Checkpoint interval."""					)
   snake_case				:    Optional[Any]           =	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					)

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

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

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

   snake_case				:    Any           =	LmSeqsDataset(params=lowercase						,data=lowercase					)
   logger.info("""Data loader created."""					)

   # STUDENT #
   logger.info(f"""Loading student config from {args.student_config}"""					)
   snake_case				:    str           =	student_config_class.from_pretrained(args.student_config					)
   snake_case				:    Tuple           =	True

   if args.student_pretrained_weights is not None:
      logger.info(f"""Loading pretrained weights from {args.student_pretrained_weights}"""					)
      snake_case				:    str           =	student_model_class.from_pretrained(args.student_pretrained_weights						,config=lowercase					)
   else:
      snake_case				:    Optional[int]           =	student_model_class(lowercase					)

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

   # TEACHER #
   snake_case				:    Optional[int]           =	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()
   snake_case				:    Union[str, Any]           =	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()











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase	:	List[str]				=   {
    'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Optional[Any]				=   [
           'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
           'Swinv2ForImageClassification',
           'Swinv2ForMaskedImageModeling',
           'Swinv2Model',
           'Swinv2PreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_swinva import (
                  SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SwinvaForImageClassification,
                  SwinvaForMaskedImageModeling,
                  SwinvaModel,
                  SwinvaPreTrainedModel,
              )


else:
       import sys

       lowerCamelCase	:	Optional[int]				=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





