



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

_A					:     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'''),
    ]
)

_A					:     str      =_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)







def     __UpperCamelCase					(				_lowercase       )			->      List[Any]:
	for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
		if class_name in processors:
			_lowercase       :							int  	=		model_type_to_module_name(_lowercase       )

			_lowercase       :							Optional[int]  	=		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       :							List[Any]  	=		importlib.import_module('transformers'       )
	if hasattr(_lowercase,					_lowercase       ):
		return getattr(_lowercase,					_lowercase       )

	return None






class 		lowerCamelCase__   :




					'''simple docstring'''



					def __init__(	self					:						Dict       )	->							Optional[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     __UpperCAmelCase  (	cls					:						int  ,	UpperCamelCase_					:						Tuple  ,	**UpperCamelCase_					:						Dict       )	->							List[str]:



						'''simple docstring'''





						_lowercase       :							int  	=		kwargs.pop('config'  ,	UpperCamelCase_       )
						_lowercase       :							List[str]  	=		kwargs.pop('trust_remote_code'  ,	UpperCamelCase_       )
						_lowercase       :							Tuple  	=		True

						_lowercase       :							Any  	=		None
						_lowercase       :							Any  	=		None

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

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

								_lowercase       :							Union[str, Any]  	=		config_dict.get('processor_class'  ,	UpperCamelCase_       )
								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(UpperCamelCase_  ,	UpperCamelCase_       ):
								_lowercase       :							Union[str, Any]  	=		AutoConfig.from_pretrained(
								    UpperCamelCase_  ,	trust_remote_code=UpperCamelCase_  ,	**UpperCamelCase_       )

							# And check if the config contains the processor class.
							_lowercase       :							List[str]  	=		getattr(UpperCamelCase_  ,	'processor_class'  ,	UpperCamelCase_       )
							if hasattr(UpperCamelCase_  ,	'auto_map'       ) and "AutoProcessor" in config.auto_map:
								_lowercase       :							Tuple  	=		config.auto_map['AutoProcessor']

						if processor_class is not None:
							_lowercase       :							List[Any]  	=		processor_class_from_name(UpperCamelCase_       )

						_lowercase       :							Optional[int]  	=		processor_auto_map is not None
						_lowercase       :							Tuple  	=		processor_class is not None or type(UpperCamelCase_       ) in PROCESSOR_MAPPING
						_lowercase       :							int  	=		resolve_trust_remote_code(
						    UpperCamelCase_  ,	UpperCamelCase_  ,	UpperCamelCase_  ,	UpperCamelCase_       )

						if has_remote_code and trust_remote_code:
							_lowercase       :							Dict  	=		get_class_from_dynamic_module(
							    UpperCamelCase_  ,	UpperCamelCase_  ,	**UpperCamelCase_       )
							_lowercase       :							Tuple  	=		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     __UpperCAmelCase  (	UpperCamelCase_					:						Tuple  ,	UpperCamelCase_					:						Tuple       )	->							Optional[Any]:



						'''simple docstring'''





						PROCESSOR_MAPPING.register(UpperCamelCase_  ,	UpperCamelCase_       )





'''simple docstring'''

from __future__ import annotations

import sys







def     __UpperCamelCase					(				_lowercase       )			->      int:
	if n < 0:
		raise ValueError('Negative arguments are not supported'       )
	return _fib(_lowercase       )[0]







def     __UpperCamelCase					(				_lowercase       )			->      tuple[int, int]:
	if n == 0:  # (F(0), F(1))
		return (0, 1)

	# F(2n) = F(n)[2F(n+1) − F(n)]
	# F(2n+1) = F(n+1)^2+F(n)^2
	_lowercase	,     _lowercase       :							List[str]  	=		_fib(n // 2       )
	_lowercase       :							Dict  	=		a * (b * 2 - a)
	_lowercase       :							Union[str, Any]  	=		a * a + b * b
	return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
		_A					:     Optional[Any]      =int(sys.argv[1])
		print(F'''fibonacci({n}) is {fibonacci(n)}''')
