


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[int]							  =							logging.get_logger(__name__)

snake_case_  :     Union[str, 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'''),
    ]
)

snake_case_  :     str							  =							_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)







def   lowerCamelCase(				a__):

				for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
								if class_name in processors:
												_SCREAMING_SNAKE_CASE     					=model_type_to_module_name(a__)

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

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

				return None


class   A__							:




						def __init__(    self   : Optional[Any]					)							->       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(SCREAMING_SNAKE_CASE_					)
						def 							__UpperCamelCase			(    cls   : Optional[int]			,      _a   : List[str]			,      **_a   : Optional[Any]					)							->       Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=kwargs.pop('''config'''			,      SCREAMING_SNAKE_CASE_					)
										_SCREAMING_SNAKE_CASE     					=kwargs.pop('''trust_remote_code'''			,      SCREAMING_SNAKE_CASE_					)
										_SCREAMING_SNAKE_CASE     					=True

										_SCREAMING_SNAKE_CASE     					=None
										_SCREAMING_SNAKE_CASE     					=None

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

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

																		_SCREAMING_SNAKE_CASE     					=config_dict.get('''processor_class'''			,      SCREAMING_SNAKE_CASE_					)
																		if "AutoProcessor" in config_dict.get('''auto_map'''			,      {}					):
																						_SCREAMING_SNAKE_CASE     					=config_dict['''auto_map''']['''AutoProcessor''']

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

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

										if processor_class is not None:
														_SCREAMING_SNAKE_CASE     					=processor_class_from_name(SCREAMING_SNAKE_CASE_					)

										_SCREAMING_SNAKE_CASE     					=processor_auto_map is not None
										_SCREAMING_SNAKE_CASE     					=processor_class is not None or type(SCREAMING_SNAKE_CASE_					) in PROCESSOR_MAPPING
										_SCREAMING_SNAKE_CASE     					=resolve_trust_remote_code(
										    SCREAMING_SNAKE_CASE_			,      SCREAMING_SNAKE_CASE_			,      SCREAMING_SNAKE_CASE_			,      SCREAMING_SNAKE_CASE_					)

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

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

														try:
																		return AutoFeatureExtractor.from_pretrained(
																		    SCREAMING_SNAKE_CASE_			,      trust_remote_code=SCREAMING_SNAKE_CASE_			,      **SCREAMING_SNAKE_CASE_					)
														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			(    _a   : Optional[int]			,      _a   : Union[str, Any]					)							->       int:


										"""simple docstring"""





										PROCESSOR_MAPPING.register(SCREAMING_SNAKE_CASE_			,      SCREAMING_SNAKE_CASE_					)



import math







def   lowerCamelCase(				a__):
				_SCREAMING_SNAKE_CASE     					=math.log(2		,10) * number
				_SCREAMING_SNAKE_CASE     					=round((log_number - int(a__))		,15)

				return difference







def   lowerCamelCase(				a__ = 67_8910):
				_SCREAMING_SNAKE_CASE     					=90
				_SCREAMING_SNAKE_CASE     					=0

				_SCREAMING_SNAKE_CASE     					=math.log(1.23		,10)
				_SCREAMING_SNAKE_CASE     					=math.log(1.24		,10)
				_SCREAMING_SNAKE_CASE     					=0

				while position < number:
								_SCREAMING_SNAKE_CASE     					=log_difference(a__)

								if difference >= upper_limit:
												power_iterator += 93

								elif difference < lower_limit:
												power_iterator += 196

								else:
												_SCREAMING_SNAKE_CASE     					=power_iterator
												power_iterator += 196
												position += 1

				return previous_power


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							print(f"""{solution() = }""")