


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


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




      """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     __lowerCamelCase						(			UpperCAmelCase_						:		Any			,  UpperCAmelCase_						:		Optional[int]						):




      """simple docstring"""





      if args.student_type == "roberta":
            a						:Tuple										=	False
      elif args.student_type == "gpt2":
            a						:List[Any]										=	False
def     __lowerCamelCase						(			UpperCAmelCase_						:		Tuple			,  UpperCAmelCase_						:		int						):




      """simple docstring"""





      if args.student_type == "roberta":
            a						:Optional[int]										=	False
def     __lowerCamelCase						(			):




      """simple docstring"""





      a						: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=500			,  help='''Tensorboard logging interval.'''						)
      parser.add_argument('''--checkpoint_interval'''			,  type=UpperCAmelCase_			,  default=4000			,  help='''Checkpoint interval.'''						)
      a						:Union[str, Any]										=	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						)

      a							,    a							,    a						:int										=	MODEL_CLASSES[args.student_type]
      a							,    a							,    a						:int										=	MODEL_CLASSES[args.teacher_type]

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

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

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

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

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

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

      # TEACHER #
      a						:Optional[Any]										=	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()
      a						:str										=	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()









# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    '''pipelines_utils''',
    '''0.22.0''',
    '''Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.''',
    standard_warn=False,
    stacklevel=3,
)





