





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


__UpperCamelCase:  Any   =   logging.get_logger(__name__)

__UpperCamelCase:  List[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"""),
    ]
)

__UpperCamelCase:  List[str]   =   _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   str )      ->       Dict:




		'''simple docstring'''
		for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
				if class_name in processors:
						lowercase__				:			int            =		model_type_to_module_name(_lowercase )

						lowercase__				:			str            =		importlib.import_module(f""".{module_name}"""      ,						'transformers.models' )
						try:
								return getattr(_lowercase      ,						_lowercase )
						except AttributeError:
								continue

		for processor in PROCESSOR_MAPPING._extra_content.values():
				if getattr(_lowercase      ,						'__name__'      ,						_lowercase ) == 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.
		lowercase__				:			Optional[Any]            =		importlib.import_module('transformers' )
		if hasattr(_lowercase      ,						_lowercase ):
				return getattr(_lowercase      ,						_lowercase )

		return None





class 			__lowerCAmelCase				:


	'''simple docstring'''





	def __init__(	self:	Any ):
			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       snake_case__(	cls:	Optional[Any],				lowerCamelCase_:	str,				**lowerCamelCase_:	int ):
			lowercase__				:			Any            =		kwargs.pop('config',				lowerCamelCase_ )
			lowercase__				:			Union[str, Any]            =		kwargs.pop('trust_remote_code',				lowerCamelCase_ )
			lowercase__				:			Optional[Any]            =		True

			lowercase__				:			List[Any]            =		None
			lowercase__				:			Union[str, Any]            =		None

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

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

							lowercase__				:			Union[str, Any]            =		config_dict.get('processor_class',				lowerCamelCase_ )
							if "AutoProcessor" in config_dict.get('auto_map',				{} ):
									lowercase__				:			Union[str, Any]            =		config_dict['auto_map']['AutoProcessor']

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

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

			if processor_class is not None:
					lowercase__				:			List[str]            =		processor_class_from_name(lowerCamelCase_ )

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

			if has_remote_code and trust_remote_code:
					lowercase__				:			str            =		get_class_from_dynamic_module(
					    lowerCamelCase_,				lowerCamelCase_,				**lowerCamelCase_ )
					lowercase__				:			Any            =		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       snake_case__(	lowerCamelCase_:	List[Any],				lowerCamelCase_:	Optional[Any] ):
			PROCESSOR_MAPPING.register(lowerCamelCase_,				lowerCamelCase_ )









def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   int )      ->       bool:




		'''simple docstring'''
		lowercase__				:			Optional[int]            =		9

		while num > 0:
				if num % 10 != digit:
						return False
				num //= 100
				digit -= 1

		return True


def 			SCREAMING_SNAKE_CASE__     (					)      ->       int:




		'''simple docstring'''
		lowercase__				:			List[str]            =		138_902_663

		while not is_square_form(num * num ):
				if num % 10 == 3:
						num -= 6  # (3 - 6) % 10 = 7
				else:
						num -= 4  # (7 - 4) % 10 = 3

		return num * 10


if __name__ == "__main__":
			print(F'{solution() = }')


