




'''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		:	Any       =      logging.get_logger(__name__)

a		:	Any       =      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		:	str       =      _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)





def UpperCAmelCase    (       UpperCAmelCase_						):
    for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
        if class_name in processors:
            _a				:							Union[str, Any]							       =      model_type_to_module_name(UpperCAmelCase_						)

            _a				:							Dict							       =      importlib.import_module(F'''.{module_name}'''   ,	"""transformers.models"""						)
            try:
                return getattr(UpperCAmelCase_   ,	UpperCAmelCase_						)
            except AttributeError:
                continue

    for processor in PROCESSOR_MAPPING._extra_content.values():
        if getattr(UpperCAmelCase_   ,	"""__name__"""   ,	UpperCAmelCase_						) == 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.
    _a				:							Union[str, Any]							       =      importlib.import_module("""transformers"""						)
    if hasattr(UpperCAmelCase_   ,	UpperCAmelCase_						):
        return getattr(UpperCAmelCase_   ,	UpperCAmelCase_						)

    return None





class      UpperCamelCase       :



  def __init__(			self ):







      '''simple docstring'''


      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(__lowerCAmelCase )
  def     a_							(			cls		,     __lowerCAmelCase		,     **__lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      kwargs.pop("""config"""		,     __lowerCAmelCase )
      _a				:							Any							       =      kwargs.pop("""trust_remote_code"""		,     __lowerCAmelCase )
      _a				:							List[Any]							       =      True

      _a				:							Dict							       =      None
      _a				:							str							       =      None

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

      if processor_class is None:
          # Next, let's check whether the processor class is saved in a tokenizer
          _a				:							Dict							       =      get_file_from_repo(
              __lowerCAmelCase		,     __lowerCAmelCase		,     **__lowerCAmelCase )
          if tokenizer_config_file is not None:
              with open(__lowerCAmelCase		,     encoding="""utf-8""" ) as reader:
                  _a				:							Optional[int]							       =      json.load(__lowerCAmelCase )

              _a				:							str							       =      config_dict.get("""processor_class"""		,     __lowerCAmelCase )
              if "AutoProcessor" in config_dict.get("""auto_map"""		,     {} ):
                  _a				:							Dict							       =      config_dict["""auto_map"""]["""AutoProcessor"""]

      if processor_class is None:
          # Otherwise, load config, if it can be loaded.
          if not isinstance(__lowerCAmelCase		,     __lowerCAmelCase ):
              _a				:							Any							       =      AutoConfig.from_pretrained(
                  __lowerCAmelCase		,     trust_remote_code=__lowerCAmelCase		,     **__lowerCAmelCase )

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

      if processor_class is not None:
          _a				:							List[str]							       =      processor_class_from_name(__lowerCAmelCase )

      _a				:							Optional[int]							       =      processor_auto_map is not None
      _a				:							List[Any]							       =      processor_class is not None or type(__lowerCAmelCase ) in PROCESSOR_MAPPING
      _a				:							List[Any]							       =      resolve_trust_remote_code(
          __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase )

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

      # 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(
              __lowerCAmelCase		,     trust_remote_code=__lowerCAmelCase		,     **__lowerCAmelCase )
      except Exception:
          try:
              return AutoImageProcessor.from_pretrained(
                  __lowerCAmelCase		,     trust_remote_code=__lowerCAmelCase		,     **__lowerCAmelCase )
          except Exception:
              pass

          try:
              return AutoFeatureExtractor.from_pretrained(
                  __lowerCAmelCase		,     trust_remote_code=__lowerCAmelCase		,     **__lowerCAmelCase )
          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     a_							(			__lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      PROCESSOR_MAPPING.register(__lowerCAmelCase		,     __lowerCAmelCase )



'''simple docstring'''

from __future__ import annotations

_lowerCamelCase :				Optional[int]														= """Alexander Joslin"""



def  _lowerCAmelCase				(				__a      )  ->      int:
     '''simple docstring'''







     if number <= 0:
          _UpperCamelCase  :Optional[Any]       =F'''n must be greater than 0. Got n = {number}'''
          raise ValueError(__a      )

     _UpperCamelCase  :Optional[Any]       =[number + 1] * (number + 1)

     # starting position
     _UpperCamelCase  :int       =0
     for i in range(1			,      __a      ):
          _UpperCamelCase  :List[str]       =min(table[i + 1]			,      table[i] + 1      )
          # check if out of bounds
          if i * 2 <= number:
               _UpperCamelCase  :int       =min(table[i * 2]			,      table[i] + 1      )
          # check if out of bounds
          if i * 3 <= number:
               _UpperCamelCase  :Tuple       =min(table[i * 3]			,      table[i] + 1      )
     return table[number]


if __name__ == "__main__":
      import doctest

      doctest.testmod()