



"""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


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

SCREAMING_SNAKE_CASE				:     List[str]          =	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'''),
    ]
)

SCREAMING_SNAKE_CASE				:     int          =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)






def 	__UpperCAmelCase					(       snake_case_  :  str   )							->	List[str]:






   """simple docstring"""


   for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
      if class_name in processors:
         _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 processor in PROCESSOR_MAPPING._extra_content.values():
      if getattr(snake_case_   ,	"""__name__"""   ,	snake_case_   ) == 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.
   _lowerCAmelCase					     =		importlib.import_module("""transformers"""   )
   if hasattr(snake_case_   ,	snake_case_   ):
      return getattr(snake_case_   ,	snake_case_   )

   return None





class       __lowerCamelCase	:







   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'''




      _lowerCAmelCase					     =		kwargs.pop("""config"""       ,	lowerCamelCase				)
      _lowerCAmelCase					     =		kwargs.pop("""trust_remote_code"""       ,	lowerCamelCase				)
      _lowerCAmelCase					     =		True

      _lowerCAmelCase					     =		None
      _lowerCAmelCase					     =		None

      # First, let's see if we have a preprocessor config.
      # Filter the kwargs for `get_file_from_repo`.
      _lowerCAmelCase					     =		{
          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
      _lowerCAmelCase					     =		get_file_from_repo(
          lowerCamelCase       ,	lowerCamelCase       ,	**lowerCamelCase				)
      if preprocessor_config_file is not None:
         _lowerCAmelCase     ,						_lowerCAmelCase					     =		ImageProcessingMixin.get_image_processor_dict(lowerCamelCase       ,	**lowerCamelCase				)
         _lowerCAmelCase					     =		config_dict.get("""processor_class"""       ,	lowerCamelCase				)
         if "AutoProcessor" in config_dict.get("""auto_map"""       ,	{}				):
            _lowerCAmelCase					     =		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:
         _lowerCAmelCase     ,						_lowerCAmelCase					     =		FeatureExtractionMixin.get_feature_extractor_dict(lowerCamelCase       ,	**lowerCamelCase				)
         _lowerCAmelCase					     =		config_dict.get("""processor_class"""       ,	lowerCamelCase				)
         if "AutoProcessor" in config_dict.get("""auto_map"""       ,	{}				):
            _lowerCAmelCase					     =		config_dict["""auto_map"""]["""AutoProcessor"""]

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

            _lowerCAmelCase					     =		config_dict.get("""processor_class"""       ,	lowerCamelCase				)
            if "AutoProcessor" in config_dict.get("""auto_map"""       ,	{}				):
               _lowerCAmelCase					     =		config_dict["""auto_map"""]["""AutoProcessor"""]

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

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

      if processor_class is not None:
         _lowerCAmelCase					     =		processor_class_from_name(lowerCamelCase				)

      _lowerCAmelCase					     =		processor_auto_map is not None
      _lowerCAmelCase					     =		processor_class is not None or type(lowerCamelCase				) in PROCESSOR_MAPPING
      _lowerCAmelCase					     =		resolve_trust_remote_code(
          lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase				)

      if has_remote_code and trust_remote_code:
         _lowerCAmelCase					     =		get_class_from_dynamic_module(
             lowerCamelCase       ,	lowerCamelCase       ,	**lowerCamelCase				)
         _lowerCAmelCase					     =		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"""





def 	__UpperCAmelCase					(       snake_case_  :  Optional[int]   ,	snake_case_  :  List[str]   )							->	Optional[int]:






   """simple docstring"""


   if n < 0:
      return 0
   # table[i] represents the number of ways to get to amount i
   _lowerCAmelCase					     =		[0] * (n + 1)

   # There is exactly 1 way to get to zero(You pick no coins).
   _lowerCAmelCase					     =		1

   # Pick all coins one by one and update table[] values
   # after the index greater than or equal to the value of the
   # picked coin
   for coin_val in s:
      for j in range(snake_case_   ,	n + 1   ):
         table[j] += table[j - coin_val]

   return table[n]


if __name__ == "__main__":
  import doctest

  doctest.testmod()