




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







def 				lowerCAmelCase_	(     snake_case_     ):
      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_	(     snake_case_,snake_case_     ):
      if args.student_type == "roberta":
            _A							:     List[str]			=						False
      elif args.student_type == "gpt2":
            _A							:     Optional[int]			=						False







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      if args.student_type == "roberta":
            _A							:     str			=						False







def 				lowerCAmelCase_	(     ):
      _A							:     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=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=500,help="""Tensorboard logging interval."""     )
      parser.add_argument("""--checkpoint_interval""",type=snake_case_,default=4000,help="""Checkpoint interval."""     )
      _A							:     List[str]			=						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							:     List[Any]			=						MODEL_CLASSES[args.student_type]
      _A			,   _A			,   _A							:     Union[str, Any]			=						MODEL_CLASSES[args.teacher_type]

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

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

      _A							:     int			=						LmSeqsDataset(params=snake_case_,data=snake_case_     )
      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							:     Dict			=						True

      if args.student_pretrained_weights is not None:
            logger.info(f'''Loading pretrained weights from {args.student_pretrained_weights}'''     )
            _A							:     List[Any]			=						student_model_class.from_pretrained(args.student_pretrained_weights,config=snake_case_     )
      else:
            _A							:     Optional[int]			=						student_model_class(snake_case_     )

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

      # TEACHER #
      _A							:     Any			=						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							:     Optional[int]			=						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()







import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


_snake_case										=       logging.get_logger(__name__)

_snake_case										=       OrderedDict(
    [
        ("audio-spectrogram-transformer", "ASTFeatureExtractor"),
        ("beit", "BeitFeatureExtractor"),
        ("chinese_clip", "ChineseCLIPFeatureExtractor"),
        ("clap", "ClapFeatureExtractor"),
        ("clip", "CLIPFeatureExtractor"),
        ("clipseg", "ViTFeatureExtractor"),
        ("conditional_detr", "ConditionalDetrFeatureExtractor"),
        ("convnext", "ConvNextFeatureExtractor"),
        ("cvt", "ConvNextFeatureExtractor"),
        ("data2vec-audio", "Wav2Vec2FeatureExtractor"),
        ("data2vec-vision", "BeitFeatureExtractor"),
        ("deformable_detr", "DeformableDetrFeatureExtractor"),
        ("deit", "DeiTFeatureExtractor"),
        ("detr", "DetrFeatureExtractor"),
        ("dinat", "ViTFeatureExtractor"),
        ("donut-swin", "DonutFeatureExtractor"),
        ("dpt", "DPTFeatureExtractor"),
        ("encodec", "EncodecFeatureExtractor"),
        ("flava", "FlavaFeatureExtractor"),
        ("glpn", "GLPNFeatureExtractor"),
        ("groupvit", "CLIPFeatureExtractor"),
        ("hubert", "Wav2Vec2FeatureExtractor"),
        ("imagegpt", "ImageGPTFeatureExtractor"),
        ("layoutlmv2", "LayoutLMv2FeatureExtractor"),
        ("layoutlmv3", "LayoutLMv3FeatureExtractor"),
        ("levit", "LevitFeatureExtractor"),
        ("maskformer", "MaskFormerFeatureExtractor"),
        ("mctct", "MCTCTFeatureExtractor"),
        ("mobilenet_v1", "MobileNetV1FeatureExtractor"),
        ("mobilenet_v2", "MobileNetV2FeatureExtractor"),
        ("mobilevit", "MobileViTFeatureExtractor"),
        ("nat", "ViTFeatureExtractor"),
        ("owlvit", "OwlViTFeatureExtractor"),
        ("perceiver", "PerceiverFeatureExtractor"),
        ("poolformer", "PoolFormerFeatureExtractor"),
        ("regnet", "ConvNextFeatureExtractor"),
        ("resnet", "ConvNextFeatureExtractor"),
        ("segformer", "SegformerFeatureExtractor"),
        ("sew", "Wav2Vec2FeatureExtractor"),
        ("sew-d", "Wav2Vec2FeatureExtractor"),
        ("speech_to_text", "Speech2TextFeatureExtractor"),
        ("speecht5", "SpeechT5FeatureExtractor"),
        ("swiftformer", "ViTFeatureExtractor"),
        ("swin", "ViTFeatureExtractor"),
        ("swinv2", "ViTFeatureExtractor"),
        ("table-transformer", "DetrFeatureExtractor"),
        ("timesformer", "VideoMAEFeatureExtractor"),
        ("tvlt", "TvltFeatureExtractor"),
        ("unispeech", "Wav2Vec2FeatureExtractor"),
        ("unispeech-sat", "Wav2Vec2FeatureExtractor"),
        ("van", "ConvNextFeatureExtractor"),
        ("videomae", "VideoMAEFeatureExtractor"),
        ("vilt", "ViltFeatureExtractor"),
        ("vit", "ViTFeatureExtractor"),
        ("vit_mae", "ViTFeatureExtractor"),
        ("vit_msn", "ViTFeatureExtractor"),
        ("wav2vec2", "Wav2Vec2FeatureExtractor"),
        ("wav2vec2-conformer", "Wav2Vec2FeatureExtractor"),
        ("wavlm", "Wav2Vec2FeatureExtractor"),
        ("whisper", "WhisperFeatureExtractor"),
        ("xclip", "CLIPFeatureExtractor"),
        ("yolos", "YolosFeatureExtractor"),
    ]
)

_snake_case										=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)







def 				lowerCAmelCase_	(     snake_case_     ):
      for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
            if class_name in extractors:
                  _A							:     List[str]			=						model_type_to_module_name(snake_case_     )

                  _A							:     List[Any]			=						importlib.import_module(f'''.{module_name}''',"""transformers.models"""     )
                  try:
                        return getattr(snake_case_,snake_case_     )
                  except AttributeError:
                        continue

      for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
            if getattr(snake_case_,"""__name__""",snake_case_     ) == class_name:
                  return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
      _A							:     List[Any]			=						importlib.import_module("""transformers"""     )
      if hasattr(snake_case_,snake_case_     ):
            return getattr(snake_case_,snake_case_     )

      return None







def 				lowerCAmelCase_	(     snake_case_,snake_case_ = None,snake_case_ = False,snake_case_ = False,snake_case_ = None,snake_case_ = None,snake_case_ = None,snake_case_ = False,**snake_case_,):
      _A							:     Optional[int]			=						get_file_from_repo(
          snake_case_,snake_case_,cache_dir=snake_case_,force_download=snake_case_,resume_download=snake_case_,proxies=snake_case_,use_auth_token=snake_case_,revision=snake_case_,local_files_only=snake_case_,)
      if resolved_config_file is None:
            logger.info(
                """Could not locate the feature extractor configuration file, will try to use the model config instead."""     )
            return {}

      with open(snake_case_,encoding="""utf-8"""     ) as reader:
            return json.load(snake_case_     )







class 						lowercase							:

     def __init__(  self				)						->							List[Any]:
           raise EnvironmentError(
               """AutoFeatureExtractor is designed to be instantiated """
               """using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."""				)

     @classmethod
     @replace_list_option_in_docstrings(_a				)
     def 					a__	(  cls		,     _a		,     **_a				)						->							Any:
           _A							:     Tuple			=						kwargs.pop("""config"""		,     _a				)
           _A							:     Tuple			=						kwargs.pop("""trust_remote_code"""		,     _a				)
           _A							:     List[Any]			=						True

           _A			,   _A							:     Tuple			=						FeatureExtractionMixin.get_feature_extractor_dict(_a		,     **_a				)
           _A							:     Tuple			=						config_dict.get("""feature_extractor_type"""		,     _a				)
           _A							:     int			=						None
           if "AutoFeatureExtractor" in config_dict.get("""auto_map"""		,     {}				):
                 _A							:     Optional[int]			=						config_dict["""auto_map"""]["""AutoFeatureExtractor"""]

           # If we don't find the feature extractor class in the feature extractor config, let's try the model config.
           if feature_extractor_class is None and feature_extractor_auto_map is None:
                 if not isinstance(_a		,     _a				):
                       _A							:     int			=						AutoConfig.from_pretrained(_a		,     **_a				)
                 # It could be in `config.feature_extractor_type``
                 _A							:     Optional[int]			=						getattr(_a		,     """feature_extractor_type"""		,     _a				)
                 if hasattr(_a		,     """auto_map"""				) and "AutoFeatureExtractor" in config.auto_map:
                       _A							:     Tuple			=						config.auto_map["""AutoFeatureExtractor"""]

           if feature_extractor_class is not None:
                 _A							:     Optional[Any]			=						feature_extractor_class_from_name(_a				)

           _A							:     List[Any]			=						feature_extractor_auto_map is not None
           _A							:     Union[str, Any]			=						feature_extractor_class is not None or type(_a				) in FEATURE_EXTRACTOR_MAPPING
           _A							:     Optional[int]			=						resolve_trust_remote_code(
               _a		,     _a		,     _a		,     _a				)

           if has_remote_code and trust_remote_code:
                 _A							:     Dict			=						get_class_from_dynamic_module(
                     _a		,     _a		,     **_a				)
                 _A							:     str			=						kwargs.pop("""code_revision"""		,     _a				)
                 if os.path.isdir(_a				):
                       feature_extractor_class.register_for_auto_class()
                 return feature_extractor_class.from_dict(_a		,     **_a				)
           elif feature_extractor_class is not None:
                 return feature_extractor_class.from_dict(_a		,     **_a				)
           # Last try: we use the FEATURE_EXTRACTOR_MAPPING.
           elif type(_a				) in FEATURE_EXTRACTOR_MAPPING:
                 _A							:     Dict			=						FEATURE_EXTRACTOR_MAPPING[type(_a				)]
                 return feature_extractor_class.from_dict(_a		,     **_a				)

           raise ValueError(
               F'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
               F'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
               F'''`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()				)}'''				)



     @staticmethod
     def 					a__	(  _a		,     _a				)						->							Optional[int]:
           FEATURE_EXTRACTOR_MAPPING.register(_a		,     _a				)

