






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      :       Any    =						{
    '''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":
          __lowerCAmelCase             =       False
     elif args.student_type == "gpt2":
          __lowerCAmelCase             =       False





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     if args.student_type == "roberta":
          __lowerCAmelCase             =       False





def    __lowerCAmelCase   (    ):
     __lowerCAmelCase             =       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."							)
     __lowerCAmelCase             =       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							)

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

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

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

     __lowerCAmelCase             =       LmSeqsDataset(params=__snake_case       ,			data=__snake_case							)
     logger.info("Data loader created."							)

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

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

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

     # TEACHER #
     __lowerCAmelCase             =       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()
     __lowerCAmelCase             =       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,
)


lowerCamelCase      :       Optional[Any]    =						logging.get_logger(__name__)

lowerCamelCase      :       Any    =						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'''),
    ]
)

lowerCamelCase      :       Optional[int]    =						_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:
               __lowerCAmelCase             =       model_type_to_module_name(__snake_case							)

               __lowerCAmelCase             =       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.
     __lowerCAmelCase             =       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       ,			):
     __lowerCAmelCase             =       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 				_UpperCamelCase      :


      def __init__(						self						)->						Union[str, 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(__UpperCamelCase						)
      def 			__UpperCAmelCase		(						cls       ,    __UpperCamelCase       ,    **__UpperCamelCase						)->						Optional[int]:
           __lowerCAmelCase             =       kwargs.pop("config"       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       kwargs.pop("trust_remote_code"       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       True

           __lowerCAmelCase      ,			__lowerCAmelCase             =       FeatureExtractionMixin.get_feature_extractor_dict(__UpperCamelCase       ,    **__UpperCamelCase						)
           __lowerCAmelCase             =       config_dict.get("feature_extractor_type"       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       None
           if "AutoFeatureExtractor" in config_dict.get("auto_map"       ,    {}						):
                __lowerCAmelCase             =       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(__UpperCamelCase       ,    __UpperCamelCase						):
                     __lowerCAmelCase             =       AutoConfig.from_pretrained(__UpperCamelCase       ,    **__UpperCamelCase						)
                # It could be in `config.feature_extractor_type``
                __lowerCAmelCase             =       getattr(__UpperCamelCase       ,    "feature_extractor_type"       ,    __UpperCamelCase						)
                if hasattr(__UpperCamelCase       ,    "auto_map"						) and "AutoFeatureExtractor" in config.auto_map:
                     __lowerCAmelCase             =       config.auto_map["AutoFeatureExtractor"]

           if feature_extractor_class is not None:
                __lowerCAmelCase             =       feature_extractor_class_from_name(__UpperCamelCase						)

           __lowerCAmelCase             =       feature_extractor_auto_map is not None
           __lowerCAmelCase             =       feature_extractor_class is not None or type(__UpperCamelCase						) in FEATURE_EXTRACTOR_MAPPING
           __lowerCAmelCase             =       resolve_trust_remote_code(
               __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)

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

           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 			__UpperCAmelCase		(						__UpperCamelCase       ,    __UpperCamelCase						)->						Optional[int]:
           FEATURE_EXTRACTOR_MAPPING.register(__UpperCamelCase       ,    __UpperCamelCase						)






