


'''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


lowercase__					  =		logging.get_logger(__name__)

lowercase__					  =		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"),
    ]
)

lowercase__					  =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)






def __UpperCamelCase (      __lowerCamelCase		:			str					)   ->  int:







     '''simple docstring'''






     for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
          if class_name in processors:
               _a            =		model_type_to_module_name(__lowerCamelCase					)

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

     for processor in PROCESSOR_MAPPING._extra_content.values():
          if getattr(__lowerCamelCase   ,	"__name__"   ,	__lowerCamelCase					) == 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            =		importlib.import_module("transformers"					)
     if hasattr(__lowerCamelCase   ,	__lowerCamelCase					):
          return getattr(__lowerCamelCase   ,	__lowerCamelCase					)

     return None






class     __SCREAMING_SNAKE_CASE       :






     def __init__(      self		)							->     int:
          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(__UpperCamelCase		)
     def 			a_       (      cls  ,				__UpperCamelCase  ,				**__UpperCamelCase		)							->     Dict:
          _a            =		kwargs.pop("config"  ,				__UpperCamelCase		)
          _a            =		kwargs.pop("trust_remote_code"  ,				__UpperCamelCase		)
          _a            =		True

          _a            =		None
          _a            =		None

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

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

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

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

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

          if processor_class is not None:
               _a            =		processor_class_from_name(__UpperCamelCase		)

          _a            =		processor_auto_map is not None
          _a            =		processor_class is not None or type(__UpperCamelCase		) in PROCESSOR_MAPPING
          _a            =		resolve_trust_remote_code(
              __UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase		)

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

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

               try:
                    return AutoFeatureExtractor.from_pretrained(
                        __UpperCamelCase  ,				trust_remote_code=__UpperCamelCase  ,				**__UpperCamelCase		)
               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_       (      __UpperCamelCase  ,				__UpperCamelCase		)							->     str:
          PROCESSOR_MAPPING.register(__UpperCamelCase  ,				__UpperCamelCase		)





'''simple docstring'''

def __UpperCamelCase (      __lowerCamelCase		:			int					)   ->  int:







     '''simple docstring'''






     if not isinstance(__lowerCamelCase   ,	__lowerCamelCase					) or number < 0:
          raise ValueError("Input must be a non-negative integer"					)

     _a            =		number & ~(number - 1)
     _a            =		0
     while intermediate:
          intermediate >>= 1
          index += 1
     return index - 1


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)

