
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       =					logging.get_logger(__name__)

_A       =					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       =					_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)






def        lowerCamelCase__    (    __lowerCAmelCase  :  Dict    ):




			"""simple docstring"""


			for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
						if class_name in processors:
									lowerCAmelCase_      =     model_type_to_module_name(__lowerCAmelCase    )

									lowerCAmelCase_      =     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.
			lowerCAmelCase_      =     importlib.import_module("transformers"    )
			if hasattr(__lowerCAmelCase   ,							__lowerCAmelCase    ):
						return getattr(__lowerCAmelCase   ,							__lowerCAmelCase    )

			return None





class      _lowerCAmelCase :




				def __init__( self					)    ->				List[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(_UpperCamelCase					)
				def 					__a						( cls    ,    _UpperCamelCase    ,    **_UpperCamelCase					)    ->				Union[str, Any]:
							lowerCAmelCase_      =     kwargs.pop("config"    ,    _UpperCamelCase					)
							lowerCAmelCase_      =     kwargs.pop("trust_remote_code"    ,    _UpperCamelCase					)
							lowerCAmelCase_      =     True

							lowerCAmelCase_      =     None
							lowerCAmelCase_      =     None

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

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

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

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

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

							if processor_class is not None:
										lowerCAmelCase_      =     processor_class_from_name(_UpperCamelCase					)

							lowerCAmelCase_      =     processor_auto_map is not None
							lowerCAmelCase_      =     processor_class is not None or type(_UpperCamelCase					) in PROCESSOR_MAPPING
							lowerCAmelCase_      =     resolve_trust_remote_code(
							    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)

							if has_remote_code and trust_remote_code:
										lowerCAmelCase_      =     get_class_from_dynamic_module(
										    _UpperCamelCase    ,    _UpperCamelCase    ,    **_UpperCamelCase					)
										lowerCAmelCase_      =     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					)    ->				int:
							PROCESSOR_MAPPING.register(_UpperCamelCase    ,    _UpperCamelCase					)





import math






def        lowerCamelCase__    (    __lowerCAmelCase  :  int    ):




			"""simple docstring"""


			if 1 < number < 4:
						# 2 and 3 are primes
						return True
			elif number < 2 or number % 2 == 0 or number % 3 == 0:
						# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
						return False

			# All primes number are in format of 6k +/- 1
			for i in range(5   ,							int(math.sqrt(__lowerCAmelCase    ) + 1    )   ,							6    ):
						if number % i == 0 or number % (i + 2) == 0:
									return False
			return True






def        lowerCamelCase__    (    __lowerCAmelCase  :  int = 600851475143    ):




			"""simple docstring"""


			try:
						lowerCAmelCase_      =     int(__lowerCAmelCase    )
			except (TypeError, ValueError):
						raise TypeError("Parameter n must be int or castable to int."    )
			if n <= 0:
						raise ValueError("Parameter n must be greater than or equal to one."    )
			lowerCAmelCase_      =     0
			if is_prime(__lowerCAmelCase    ):
						return n
			while n % 2 == 0:
						n //= 2
			if is_prime(__lowerCAmelCase    ):
						return n
			for i in range(3   ,							int(math.sqrt(__lowerCAmelCase    )    ) + 1   ,							2    ):
						if n % i == 0:
									if is_prime(n // i    ):
												lowerCAmelCase_      =     n // i
												break
									elif is_prime(__lowerCAmelCase    ):
												lowerCAmelCase_      =     i
			return max_number


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



