code: 
  
  
  
  
  
  
  """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
  
  
  snake_case   :   Optional[Any]    		=		logging.get_logger(__name__)
  
  snake_case   :   Dict    		=		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"""),
      ]
  )
  
  snake_case   :   Optional[int]    		=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)
  
  
  def        A	(							__snake_case:   str     )						->		Dict:
  
         """simple docstring"""
  
  
  
  
  
         for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
                if class_name in processors:
                       __magic_name__										=						model_type_to_module_name(__snake_case     )
  
                       __magic_name__										=						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.
         __magic_name__										=						importlib.import_module('transformers'     )
         if hasattr(__snake_case   ,    __snake_case     ):
                return getattr(__snake_case   ,    __snake_case     )
  
         return None
  
  
  
  
  
  
  class 					UpperCamelCase__   :
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def __init__(		self   :  Union[str, Any]   ):
  
  
  
  
  
             '''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(UpperCamelCase_   )
      def 							a__ (		cls   :  int							,						UpperCamelCase_   :  List[str]							,						**UpperCamelCase_   :  List[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						kwargs.pop('config'							,						UpperCamelCase_   )
             __magic_name__										=						kwargs.pop('trust_remote_code'							,						UpperCamelCase_   )
             __magic_name__										=						True
  
             __magic_name__										=						None
             __magic_name__										=						None
  
             # First, let's see if we have a preprocessor config.
             # Filter the kwargs for `get_file_from_repo`.
             __magic_name__										=						{
                 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
             __magic_name__										=						get_file_from_repo(
                 UpperCamelCase_							,						UpperCamelCase_							,						**UpperCamelCase_   )
             if preprocessor_config_file is not None:
                    __magic_name__							,     __magic_name__										=						ImageProcessingMixin.get_image_processor_dict(UpperCamelCase_							,						**UpperCamelCase_   )
                    __magic_name__										=						config_dict.get('processor_class'							,						UpperCamelCase_   )
                    if "AutoProcessor" in config_dict.get('auto_map'							,						{}   ):
                           __magic_name__										=						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:
                    __magic_name__							,     __magic_name__										=						FeatureExtractionMixin.get_feature_extractor_dict(UpperCamelCase_							,						**UpperCamelCase_   )
                    __magic_name__										=						config_dict.get('processor_class'							,						UpperCamelCase_   )
                    if "AutoProcessor" in config_dict.get('auto_map'							,						{}   ):
                           __magic_name__										=						config_dict['auto_map']['AutoProcessor']
  
             if processor_class is None:
                    # Next, let's check whether the processor class is saved in a tokenizer
                    __magic_name__										=						get_file_from_repo(
                        UpperCamelCase_							,						UpperCamelCase_							,						**UpperCamelCase_   )
                    if tokenizer_config_file is not None:
                           with open(UpperCamelCase_							,						encoding='utf-8'   ) as reader:
                                  __magic_name__										=						json.load(UpperCamelCase_   )
  
                           __magic_name__										=						config_dict.get('processor_class'							,						UpperCamelCase_   )
                           if "AutoProcessor" in config_dict.get('auto_map'							,						{}   ):
                                  __magic_name__										=						config_dict['auto_map']['AutoProcessor']
  
             if processor_class is None:
                    # Otherwise, load config, if it can be loaded.
                    if not isinstance(UpperCamelCase_							,						UpperCamelCase_   ):
                           __magic_name__										=						AutoConfig.from_pretrained(
                               UpperCamelCase_							,						trust_remote_code=UpperCamelCase_							,						**UpperCamelCase_   )
  
                    # And check if the config contains the processor class.
                    __magic_name__										=						getattr(UpperCamelCase_							,						'processor_class'							,						UpperCamelCase_   )
                    if hasattr(UpperCamelCase_							,						'auto_map'   ) and "AutoProcessor" in config.auto_map:
                           __magic_name__										=						config.auto_map['AutoProcessor']
  
             if processor_class is not None:
                    __magic_name__										=						processor_class_from_name(UpperCamelCase_   )
  
             __magic_name__										=						processor_auto_map is not None
             __magic_name__										=						processor_class is not None or type(UpperCamelCase_   ) in PROCESSOR_MAPPING
             __magic_name__										=						resolve_trust_remote_code(
                 UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_   )
  
             if has_remote_code and trust_remote_code:
                    __magic_name__										=						get_class_from_dynamic_module(
                        UpperCamelCase_							,						UpperCamelCase_							,						**UpperCamelCase_   )
                    __magic_name__										=						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_   :  Dict							,						UpperCamelCase_   :  List[str]   ):
  
  
  
  
  
             '''simple docstring'''
  
             PROCESSOR_MAPPING.register(UpperCamelCase_							,						UpperCamelCase_   )
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  import numpy as np
  
  
  def        A	(							__snake_case:   Any   ,    __snake_case:   List[str]   ,    __snake_case:   Tuple     )						->		Dict:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						n_draws * class_counts / class_counts.sum()
         # floored means we don't overshoot n_samples, but probably undershoot
         __magic_name__										=						np.floor(__snake_case     )
         # we add samples according to how much "left over" probability
         # they had, until we arrive at n_samples
         __magic_name__										=						int(n_draws - floored.sum()     )
         if need_to_add > 0:
                __magic_name__										=						continuous - floored
                __magic_name__										=						np.sort(np.unique(__snake_case     )     )[::-1]
                # add according to remainder, but break ties
                # randomly to avoid biases
                for value in values:
                       ((__magic_name__)							,     )										=						np.where(remainder == value     )
                       # if we need_to_add less than what's in inds
                       # we draw randomly from them.
                       # if we need to add more, we add them all and
                       # go to the next value
                       __magic_name__										=						min(len(__snake_case     )   ,    __snake_case     )
                       __magic_name__										=						rng.choice(__snake_case   ,    size=__snake_case   ,    replace=__snake_case     )
                       floored[inds] += 1
                       need_to_add -= add_now
                       if need_to_add == 0:
                              break
         return floored.astype(np.intaa     )
  
  
  
  
  
  
  def        A	(							__snake_case:   Any   ,    __snake_case:   str   ,    __snake_case:   int   ,    __snake_case:   Optional[Any]   ,    __snake_case:   Any=1_0     )						->		Any:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__							,     __magic_name__										=						np.unique(__snake_case   ,    return_inverse=__snake_case     )
         __magic_name__										=						classes.shape[0]
         __magic_name__										=						np.bincount(__snake_case     )
         if np.min(__snake_case     ) < 2:
                raise ValueError('Minimum class count error'     )
         if n_train < n_classes:
                raise ValueError(
                    'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)     )
         if n_test < n_classes:
                raise ValueError(
                    'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)     )
         __magic_name__										=						np.split(np.argsort(__snake_case   ,    kind='mergesort'     )   ,    np.cumsum(__snake_case     )[:-1]     )
         for _ in range(__snake_case     ):
                __magic_name__										=						approximate_mode(__snake_case   ,    __snake_case   ,    __snake_case     )
                __magic_name__										=						class_counts - n_i
                __magic_name__										=						approximate_mode(__snake_case   ,    __snake_case   ,    __snake_case     )
  
                __magic_name__										=						[]
                __magic_name__										=						[]
  
                for i in range(__snake_case     ):
                       __magic_name__										=						rng.permutation(class_counts[i]     )
                       __magic_name__										=						class_indices[i].take(__snake_case   ,    mode='clip'     )
                       train.extend(perm_indices_class_i[: n_i[i]]     )
                       test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]     )
                __magic_name__										=						rng.permutation(__snake_case     )
                __magic_name__										=						rng.permutation(__snake_case     )
  
                yield train, test