import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
     import torch

__snake_case         =     logging.get_logger(__name__)





def 	lowerCAmelCase_			(					)->	Union[str, Any]:






      '''simple docstring'''





      return importlib.util.find_spec('''deepspeed'''    ) is not None


if is_accelerate_available() and is_deepspeed_available():
     from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
     # Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
     # Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
     from builtins import object as DeepSpeedConfig







class       __snake_case (  _snake_case ):
 def __init__(	self		, snake_case__    )						->				Optional[Any]:
       '''simple docstring'''





       set_hf_deepspeed_config(self    )
       dep_version_check('''accelerate'''    )
       dep_version_check('''deepspeed'''    )
       super().__init__(snake_case_    )







class       __snake_case (  _snake_case ):
 def __init__(	self		, snake_case__    )						->				Dict:
       '''simple docstring'''





       super().__init__(snake_case_    )
       UpperCAmelCase     :			Optional[Any]     	=None
       UpperCAmelCase     :			Optional[int]     	=[]
 def 						UpperCAmelCase__							(	self    )						->				Union[str, Any]:
       '''simple docstring'''





       if self._dtype is None:
             raise ValueError('''trainer_config_process() wasn\'t called yet to tell dtype'''    )
       return self._dtype
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=self.get_value(snake_case_    )
       if val is None:
             return False
       else:
             return val == "auto"
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__=None		, snake_case__=True    )						->				Optional[int]:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=self.find_config_node(snake_case_    )
       if config is None:
             return

       if config.get(snake_case_    ) == "auto":
             UpperCAmelCase     :			str     	=hf_val
             return

       if not must_match:
             return

       UpperCAmelCase     :			Tuple     	=config.get(snake_case_    )
       if ds_val is not None and ds_val != hf_val:
             self.mismatches.append(f'''- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}'''    )

 __lowerCamelCase     : Any 	=   partialmethod(_snake_case       , must_match=_snake_case )
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				Optional[int]:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
       self.fill_match(
           '''train_micro_batch_size_per_gpu'''		, args.per_device_train_batch_size		, '''per_device_train_batch_size'''    )
       self.fill_match('''gradient_accumulation_steps'''		, args.gradient_accumulation_steps		, '''gradient_accumulation_steps'''    )
       self.fill_match('''train_batch_size'''		, snake_case_		, '''train_batch_size (calculated)'''    )
       self.fill_match('''gradient_clipping'''		, args.max_grad_norm		, '''max_grad_norm'''    )

       self.fill_match('''optimizer.params.lr'''		, args.learning_rate		, '''learning_rate'''    )
       self.fill_match('''optimizer.params.betas'''		, [args.adam_betaa, args.adam_betaa]		, '''adam_beta1+adam_beta2'''    )
       self.fill_match('''optimizer.params.eps'''		, args.adam_epsilon		, '''adam_epsilon'''    )
       self.fill_match('''optimizer.params.weight_decay'''		, args.weight_decay		, '''weight_decay'''    )

       self.fill_only('''scheduler.params.warmup_min_lr'''		, 0    )  # not a trainer arg
       self.fill_match('''scheduler.params.warmup_max_lr'''		, args.learning_rate		, '''learning_rate'''    )
       # total_num_steps - will get set in trainer_config_finalize

       # fp16
       if args.fpaa or args.fpaa_full_eval:
             UpperCAmelCase     :			List[Any]     	="apex" if args.fpaa_backend == "apex" else "amp"
       else:
             UpperCAmelCase     :			Optional[int]     	=None

       if args.save_on_each_node:
             # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
             UpperCAmelCase     :			List[Any]     	=self.config.get('''checkpoint'''		, {}    )
             UpperCAmelCase     :			Dict     	=args.save_on_each_node

       # amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
       # any here unless the user did the work
       self.fill_match(
           '''fp16.enabled'''		, ((args.fpaa or args.fpaa_full_eval) and fpaa_backend == '''amp''')		, '''fp16|fp16_full_eval+fp16_backend(amp)'''		, )

       # apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
       # ZeRO features
       self.fill_match('''amp.enabled'''		, fpaa_backend == '''apex'''		, '''fp16+fp16_backend(apex)'''    )
       self.fill_match('''amp.opt_level'''		, args.fpaa_opt_level		, '''fp16_opt_level'''    )

       self.fill_match('''bf16.enabled'''		, (args.bfaa or args.bfaa_full_eval)		, '''bf16|bf16_full_eval'''    )

       # deepspeed's default mode is fp16 unless there is a config that says differently
       if self.is_true('''bf16.enabled'''    ):
             UpperCAmelCase     :			str     	=torch.bfloataa
       elif self.is_false('''fp16.enabled'''    ):
             UpperCAmelCase     :			Union[str, Any]     	=torch.floataa
       else:
             UpperCAmelCase     :			Tuple     	=torch.floataa






 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__    )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[Any]     	=[
           "zero_optimization.reduce_bucket_size",
           "zero_optimization.stage3_prefetch_bucket_size",
           "zero_optimization.stage3_param_persistence_threshold",
       ]
       UpperCAmelCase     :			Dict     	=[x for x in hidden_size_based_keys if self.is_auto(snake_case_    )]

       if len(snake_case_    ) > 0:
             if hasattr(model.config		, '''hidden_size'''    ):
                   UpperCAmelCase     :			int     	=model.config.hidden_size
             elif hasattr(model.config		, '''hidden_sizes'''    ):
                   # if there are many hidden sizes pick the largest one
                   UpperCAmelCase     :			List[Any]     	=max(model.config.hidden_sizes    )
             else:
                   raise ValueError(
                       '''The model\'s config file has neither `hidden_size` nor `hidden_sizes` entry, '''
                       '''therefore it\'s not possible to automatically fill out the following `auto` entries '''
                       f'''in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing '''
                       '''`auto` values for these keys with an integer value of your choice.'''    )

             self.fill_only('''zero_optimization.reduce_bucket_size'''		, hidden_size * hidden_size    )
             if self.is_zeroa():
                   # automatically assign the optimal config values based on model config
                   self.fill_only('''zero_optimization.stage3_prefetch_bucket_size'''		, 0.9 * hidden_size * hidden_size    )
                   self.fill_only('''zero_optimization.stage3_param_persistence_threshold'''		, 10 * hidden_size    )

             # scheduler
       self.fill_match('''scheduler.params.total_num_steps'''		, snake_case_		, '''num_training_steps (calculated)'''    )
       self.fill_match('''scheduler.params.warmup_num_steps'''		, args.get_warmup_steps(snake_case_    )		, '''warmup_steps'''    )

       if len(self.mismatches    ) > 0:
             UpperCAmelCase     :			int     	="\n".join(self.mismatches    )
             raise ValueError(
                 '''Please correct the following DeepSpeed config values that mismatch TrainingArguments'''
                 f''' values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to \'auto\'.'''    )



__snake_case         =     None





def 	lowerCAmelCase_			(					__lowerCAmelCase    )->	List[str]:






      '''simple docstring'''





      global _hf_deepspeed_config_weak_ref
      # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
      UpperCAmelCase     :			Any     	=weakref.ref(__lowerCAmelCase    )





def 	lowerCAmelCase_			(					)->	Union[str, Any]:






      '''simple docstring'''





      global _hf_deepspeed_config_weak_ref
      UpperCAmelCase     :			int     	=None





def 	lowerCAmelCase_			(					)->	Tuple:






      '''simple docstring'''





      if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
            return _hf_deepspeed_config_weak_ref().is_zeroa()
      else:
            return False





def 	lowerCAmelCase_			(					)->	List[Any]:






      '''simple docstring'''





      if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
            return _hf_deepspeed_config_weak_ref().config
      else:
            return None





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	Dict:






      '''simple docstring'''





      from accelerate.utils import DummyOptim, DummyScheduler

      UpperCAmelCase     :			Dict     	=hf_deepspeed_config.config

      # Optimizer + Scheduler
      # Currently supported combos:
      # 1. DS scheduler + DS optimizer: Yes
      # 2. HF scheduler + HF optimizer: Yes
      # 3. DS scheduler + HF optimizer: Yes
      # 4. HF scheduler + DS optimizer: No
      #
      # Unless Offload is enabled in which case it's:
      # 1. DS scheduler + DS optimizer: Yes
      # 2. HF scheduler + HF optimizer: Mostly*
      # 3. DS scheduler + HF optimizer: Mostly*
      # 4. HF scheduler + DS optimizer: No
      #
      # Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

      UpperCAmelCase     :			List[str]     	=None
      if "optimizer" in config:
            if args.adafactor:
                  raise ValueError(
                      '''--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. '''
                      '''Only one optimizer can be configured.'''    )
            UpperCAmelCase     :			str     	=DummyOptim(params=__lowerCAmelCase    )
      else:
            if hf_deepspeed_config.is_offload():
                  logger.info(
                      '''Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the'''
                      ''' custom optimizer has both CPU and GPU implementation (except LAMB)'''    )

            # ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
            # But trainer uses AdamW by default.
            UpperCAmelCase     :			Dict     	=trainer.create_optimizer()
            # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
            UpperCAmelCase     :			Any     	=True

      UpperCAmelCase     :			str     	=None
      if "scheduler" in config:
            UpperCAmelCase     :			int     	=DummyScheduler(__lowerCAmelCase    )
      else:
            if isinstance(__lowerCAmelCase		,       __lowerCAmelCase    ):
                  raise ValueError(
                      '''Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '''
                      '''Please configure a scheduler in the DeepSpeed config.'''    )
            UpperCAmelCase     :			Optional[int]     	=trainer.create_scheduler(num_training_steps=__lowerCAmelCase		,       optimizer=__lowerCAmelCase    )

      return optimizer, lr_scheduler





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=False    )->	Dict:






      '''simple docstring'''





      from deepspeed.utils import logger as ds_logger

      UpperCAmelCase     :			List[Any]     	=trainer.model
      UpperCAmelCase     :			Tuple     	=trainer.args

      UpperCAmelCase     :			List[str]     	=trainer.accelerator.state.deepspeed_plugin.hf_ds_config

      # resume config update - some bits like `model` and `num_training_steps` only become available during train
      hf_deepspeed_config.trainer_config_finalize(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )

      # set the Deepspeed log level consistent with the Trainer
      ds_logger.setLevel(args.get_process_log_level()    )

      if inference:
            # only Z3 makes sense for the inference
            if not hf_deepspeed_config.is_zeroa():
                  raise ValueError('''ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config'''    )

            # in case the training config is re-used for inference
            hf_deepspeed_config.del_config_sub_tree('''optimizer'''    )
            hf_deepspeed_config.del_config_sub_tree('''lr_scheduler'''    )
            UpperCAmelCase     :			int     	=None, None
            UpperCAmelCase     :			Optional[int]     	=None
      else:
            UpperCAmelCase     :			Any     	=None  # important for when deepspeed_init is used as re-init
            UpperCAmelCase     :			Union[str, Any]     	=list(filter(lambda __lowerCAmelCase    : p.requires_grad		,       model.parameters()    )    )
            UpperCAmelCase     :			Tuple     	=deepspeed_optim_sched(
                __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )

      # keep for quick debug:
      # from pprint import pprint; pprint(config)

      return optimizer, lr_scheduler





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase    )->	Optional[int]:






      '''simple docstring'''





      import glob

      UpperCAmelCase     :			Dict     	=sorted(glob.glob(f'''{checkpoint_path}/global_step*'''    )    )

      if len(__lowerCAmelCase    ) > 0:
            logger.info(f'''Attempting to resume from {checkpoint_path}'''    )
            # this magically updates self.optimizer and self.lr_scheduler
            UpperCAmelCase     :			Any     	=deepspeed_engine.load_checkpoint(
                __lowerCAmelCase		,       load_optimizer_states=__lowerCAmelCase		,       load_lr_scheduler_states=__lowerCAmelCase    )
            if load_path is None:
                  raise ValueError(f'''[deepspeed] failed to resume from checkpoint {checkpoint_path}'''    )
      else:
            raise ValueError(f'''Can\'t find a valid checkpoint at {checkpoint_path}'''    )


"""simple docstring"""
import importlib
import inspect
import json
import os
from collections import OrderedDict

# 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 ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import 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,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


_a   :      Optional[int]       =				logging.get_logger(__name__)

_a   :      Tuple       =				OrderedDict(
    [
        ("""align""", """AlignProcessor"""),
        ("""altclip""", """AltCLIPProcessor"""),
        ("""bark""", """BarkProcessor"""),
        ("""blip""", """BlipProcessor"""),
        ("""blip-2""", """Blip2Processor"""),
        ("""bridgetower""", """BridgeTowerProcessor"""),
        ("""chinese_clip""", """ChineseCLIPProcessor"""),
        ("""clap""", """ClapProcessor"""),
        ("""clip""", """CLIPProcessor"""),
        ("""clipseg""", """CLIPSegProcessor"""),
        ("""flava""", """FlavaProcessor"""),
        ("""git""", """GitProcessor"""),
        ("""groupvit""", """CLIPProcessor"""),
        ("""hubert""", """Wav2Vec2Processor"""),
        ("""instructblip""", """InstructBlipProcessor"""),
        ("""layoutlmv2""", """LayoutLMv2Processor"""),
        ("""layoutlmv3""", """LayoutLMv3Processor"""),
        ("""markuplm""", """MarkupLMProcessor"""),
        ("""mctct""", """MCTCTProcessor"""),
        ("""mgp-str""", """MgpstrProcessor"""),
        ("""oneformer""", """OneFormerProcessor"""),
        ("""owlvit""", """OwlViTProcessor"""),
        ("""pix2struct""", """Pix2StructProcessor"""),
        ("""sam""", """SamProcessor"""),
        ("""sew""", """Wav2Vec2Processor"""),
        ("""sew-d""", """Wav2Vec2Processor"""),
        ("""speech_to_text""", """Speech2TextProcessor"""),
        ("""speech_to_text_2""", """Speech2Text2Processor"""),
        ("""speecht5""", """SpeechT5Processor"""),
        ("""trocr""", """TrOCRProcessor"""),
        ("""tvlt""", """TvltProcessor"""),
        ("""unispeech""", """Wav2Vec2Processor"""),
        ("""unispeech-sat""", """Wav2Vec2Processor"""),
        ("""vilt""", """ViltProcessor"""),
        ("""vision-text-dual-encoder""", """VisionTextDualEncoderProcessor"""),
        ("""wav2vec2""", """Wav2Vec2Processor"""),
        ("""wav2vec2-conformer""", """Wav2Vec2Processor"""),
        ("""wavlm""", """Wav2Vec2Processor"""),
        ("""whisper""", """WhisperProcessor"""),
        ("""xclip""", """XCLIPProcessor"""),
    ]
)

_a   :      Any       =				_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)
def 					a__ ( a :					str		):

  """simple docstring"""







  for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
    if class_name in processors:
      _snake_case  :    Dict            = model_type_to_module_name(a		)

      _snake_case  :    Optional[int]            = importlib.import_module(f'.{module_name}'							,  "transformers.models"		)
      try:
        return getattr(a							,  a		)
      except AttributeError:
        continue

  for processor in PROCESSOR_MAPPING._extra_content.values():
    if getattr(a							,  "__name__"							,  a		) == class_name:
      return processor

    # 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.
  _snake_case  :    int            = importlib.import_module("transformers"		)
  if hasattr(a							,  a		):
    return getattr(a							,  a		)

  return None






class   _UpperCAmelCase					:
    def __init__(     self  ):
      raise EnvironmentError(
          "AutoProcessor is designed to be instantiated "
          "using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method."  )
    @classmethod
    @replace_list_option_in_docstrings(snake_case_  )
    def        lowerCamelCase__						(     cls    ,     snake_case_    ,     **snake_case_  ):
      _snake_case  :    str            = kwargs.pop("config"    ,     snake_case_  )
      _snake_case  :    Dict            = kwargs.pop("trust_remote_code"    ,     snake_case_  )
      _snake_case  :    Tuple            = True

      _snake_case  :    List[str]            = None
      _snake_case  :    Optional[Any]            = None

      # First, let's see if we have a preprocessor config.
      # Filter the kwargs for `get_file_from_repo`.
      _snake_case  :    Optional[int]            = {
          key: kwargs[key] for key in inspect.signature(snake_case_  ).parameters.keys() if key in kwargs
      }
      # Let's start by checking whether the processor class is saved in an image processor
      _snake_case  :    Dict            = get_file_from_repo(
          snake_case_    ,     snake_case_    ,     **snake_case_  )
      if preprocessor_config_file is not None:
        _snake_case      ,       _snake_case  :    List[str]            = ImageProcessingMixin.get_image_processor_dict(snake_case_    ,     **snake_case_  )
        _snake_case  :    Union[str, Any]            = config_dict.get("processor_class"    ,     snake_case_  )
        if "AutoProcessor" in config_dict.get("auto_map"    ,     {}  ):
          _snake_case  :    Optional[int]            = config_dict["auto_map"]["AutoProcessor"]

        # If not found, let's check whether the processor class is saved in a feature extractor config
      if preprocessor_config_file is not None and processor_class is None:
        _snake_case      ,       _snake_case  :    Any            = FeatureExtractionMixin.get_feature_extractor_dict(snake_case_    ,     **snake_case_  )
        _snake_case  :    List[Any]            = config_dict.get("processor_class"    ,     snake_case_  )
        if "AutoProcessor" in config_dict.get("auto_map"    ,     {}  ):
          _snake_case  :    int            = config_dict["auto_map"]["AutoProcessor"]

      if processor_class is None:
        # Next, let's check whether the processor class is saved in a tokenizer
        _snake_case  :    Union[str, Any]            = get_file_from_repo(
            snake_case_    ,     snake_case_    ,     **snake_case_  )
        if tokenizer_config_file is not None:
          with open(snake_case_    ,     encoding="utf-8"  ) as reader:
            _snake_case  :    Tuple            = json.load(snake_case_  )

          _snake_case  :    List[Any]            = config_dict.get("processor_class"    ,     snake_case_  )
          if "AutoProcessor" in config_dict.get("auto_map"    ,     {}  ):
            _snake_case  :    Tuple            = config_dict["auto_map"]["AutoProcessor"]

      if processor_class is None:
        # Otherwise, load config, if it can be loaded.
        if not isinstance(snake_case_    ,     snake_case_  ):
          _snake_case  :    Union[str, Any]            = AutoConfig.from_pretrained(
              snake_case_    ,     trust_remote_code=snake_case_    ,     **snake_case_  )

        # And check if the config contains the processor class.
        _snake_case  :    List[Any]            = getattr(snake_case_    ,     "processor_class"    ,     snake_case_  )
        if hasattr(snake_case_    ,     "auto_map"  ) and "AutoProcessor" in config.auto_map:
          _snake_case  :    str            = config.auto_map["AutoProcessor"]

      if processor_class is not None:
        _snake_case  :    Optional[int]            = processor_class_from_name(snake_case_  )

      _snake_case  :    Optional[int]            = processor_auto_map is not None
      _snake_case  :    Any            = processor_class is not None or type(snake_case_  ) in PROCESSOR_MAPPING
      _snake_case  :    Union[str, Any]            = resolve_trust_remote_code(
          snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  )

      if has_remote_code and trust_remote_code:
        _snake_case  :    Dict            = get_class_from_dynamic_module(
            snake_case_    ,     snake_case_    ,     **snake_case_  )
        _snake_case  :    Union[str, Any]            = kwargs.pop("code_revision"    ,     snake_case_  )
        if os.path.isdir(snake_case_  ):
          processor_class.register_for_auto_class()
        return processor_class.from_pretrained(
            snake_case_    ,     trust_remote_code=snake_case_    ,     **snake_case_  )
      elif processor_class is not None:
        return processor_class.from_pretrained(
            snake_case_    ,     trust_remote_code=snake_case_    ,     **snake_case_  )
      # Last try: we use the PROCESSOR_MAPPING.
      elif type(snake_case_  ) in PROCESSOR_MAPPING:
        return PROCESSOR_MAPPING[type(snake_case_  )].from_pretrained(snake_case_    ,     **snake_case_  )

      # At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
      # tokenizer.
      try:
        return AutoTokenizer.from_pretrained(
            snake_case_    ,     trust_remote_code=snake_case_    ,     **snake_case_  )
      except Exception:
        try:
          return AutoImageProcessor.from_pretrained(
              snake_case_    ,     trust_remote_code=snake_case_    ,     **snake_case_  )
        except Exception:
          pass

        try:
          return AutoFeatureExtractor.from_pretrained(
              snake_case_    ,     trust_remote_code=snake_case_    ,     **snake_case_  )
        except Exception:
          pass

      raise ValueError(
          F'Unrecognized processing class in {pretrained_model_name_or_path}. Can\'t instantiate a processor, a '
          "tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains"
          "the files of at least one of those processing classes."  )


    @staticmethod
    def        lowerCamelCase__						(     snake_case_    ,     snake_case_  ):
      PROCESSOR_MAPPING.register(snake_case_    ,     snake_case_  )

