import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_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,
)


__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					OrderedDict(
    [
        ('''align''', '''EfficientNetImageProcessor'''),
        ('''beit''', '''BeitImageProcessor'''),
        ('''bit''', '''BitImageProcessor'''),
        ('''blip''', '''BlipImageProcessor'''),
        ('''blip-2''', '''BlipImageProcessor'''),
        ('''bridgetower''', '''BridgeTowerImageProcessor'''),
        ('''chinese_clip''', '''ChineseCLIPImageProcessor'''),
        ('''clip''', '''CLIPImageProcessor'''),
        ('''clipseg''', '''ViTImageProcessor'''),
        ('''conditional_detr''', '''ConditionalDetrImageProcessor'''),
        ('''convnext''', '''ConvNextImageProcessor'''),
        ('''convnextv2''', '''ConvNextImageProcessor'''),
        ('''cvt''', '''ConvNextImageProcessor'''),
        ('''data2vec-vision''', '''BeitImageProcessor'''),
        ('''deformable_detr''', '''DeformableDetrImageProcessor'''),
        ('''deit''', '''DeiTImageProcessor'''),
        ('''deta''', '''DetaImageProcessor'''),
        ('''detr''', '''DetrImageProcessor'''),
        ('''dinat''', '''ViTImageProcessor'''),
        ('''donut-swin''', '''DonutImageProcessor'''),
        ('''dpt''', '''DPTImageProcessor'''),
        ('''efficientformer''', '''EfficientFormerImageProcessor'''),
        ('''efficientnet''', '''EfficientNetImageProcessor'''),
        ('''flava''', '''FlavaImageProcessor'''),
        ('''focalnet''', '''BitImageProcessor'''),
        ('''git''', '''CLIPImageProcessor'''),
        ('''glpn''', '''GLPNImageProcessor'''),
        ('''groupvit''', '''CLIPImageProcessor'''),
        ('''imagegpt''', '''ImageGPTImageProcessor'''),
        ('''instructblip''', '''BlipImageProcessor'''),
        ('''layoutlmv2''', '''LayoutLMv2ImageProcessor'''),
        ('''layoutlmv3''', '''LayoutLMv3ImageProcessor'''),
        ('''levit''', '''LevitImageProcessor'''),
        ('''mask2former''', '''Mask2FormerImageProcessor'''),
        ('''maskformer''', '''MaskFormerImageProcessor'''),
        ('''mgp-str''', '''ViTImageProcessor'''),
        ('''mobilenet_v1''', '''MobileNetV1ImageProcessor'''),
        ('''mobilenet_v2''', '''MobileNetV2ImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevitv2''', '''MobileViTImageProcessor'''),
        ('''nat''', '''ViTImageProcessor'''),
        ('''oneformer''', '''OneFormerImageProcessor'''),
        ('''owlvit''', '''OwlViTImageProcessor'''),
        ('''perceiver''', '''PerceiverImageProcessor'''),
        ('''pix2struct''', '''Pix2StructImageProcessor'''),
        ('''poolformer''', '''PoolFormerImageProcessor'''),
        ('''regnet''', '''ConvNextImageProcessor'''),
        ('''resnet''', '''ConvNextImageProcessor'''),
        ('''sam''', '''SamImageProcessor'''),
        ('''segformer''', '''SegformerImageProcessor'''),
        ('''swiftformer''', '''ViTImageProcessor'''),
        ('''swin''', '''ViTImageProcessor'''),
        ('''swin2sr''', '''Swin2SRImageProcessor'''),
        ('''swinv2''', '''ViTImageProcessor'''),
        ('''table-transformer''', '''DetrImageProcessor'''),
        ('''timesformer''', '''VideoMAEImageProcessor'''),
        ('''tvlt''', '''TvltImageProcessor'''),
        ('''upernet''', '''SegformerImageProcessor'''),
        ('''van''', '''ConvNextImageProcessor'''),
        ('''videomae''', '''VideoMAEImageProcessor'''),
        ('''vilt''', '''ViltImageProcessor'''),
        ('''vit''', '''ViTImageProcessor'''),
        ('''vit_hybrid''', '''ViTHybridImageProcessor'''),
        ('''vit_mae''', '''ViTImageProcessor'''),
        ('''vit_msn''', '''ViTImageProcessor'''),
        ('''xclip''', '''CLIPImageProcessor'''),
        ('''yolos''', '''YolosImageProcessor'''),
    ]
)

__lowercase							 =					_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
        if class_name in extractors:
            __UpperCamelCase     :Tuple			  =		model_type_to_module_name(SCREAMING_SNAKE_CASE	)

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

    for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
        if getattr(SCREAMING_SNAKE_CASE  ,			'''__name__'''  ,			SCREAMING_SNAKE_CASE	) == class_name:
            return extractor

    # 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.
    __UpperCamelCase     :List[str]			  =		importlib.import_module('''transformers'''	)
    if hasattr(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
        return getattr(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    return None



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = None  ,			SCREAMING_SNAKE_CASE = False  ,			SCREAMING_SNAKE_CASE = False  ,			SCREAMING_SNAKE_CASE = None  ,			SCREAMING_SNAKE_CASE = None  ,			SCREAMING_SNAKE_CASE = None  ,			SCREAMING_SNAKE_CASE = False  ,			**SCREAMING_SNAKE_CASE  ,			):
    '''simple docstring'''







    __UpperCamelCase     :Dict			  =		get_file_from_repo(
        SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			cache_dir=SCREAMING_SNAKE_CASE  ,			force_download=SCREAMING_SNAKE_CASE  ,			resume_download=SCREAMING_SNAKE_CASE  ,			proxies=SCREAMING_SNAKE_CASE  ,			use_auth_token=SCREAMING_SNAKE_CASE  ,			revision=SCREAMING_SNAKE_CASE  ,			local_files_only=SCREAMING_SNAKE_CASE  ,			)
    if resolved_config_file is None:
        logger.info(
            '''Could not locate the image processor configuration file, will try to use the model config instead.'''	)
        return {}

    with open(SCREAMING_SNAKE_CASE  ,			encoding='''utf-8'''	) as reader:
        return json.load(SCREAMING_SNAKE_CASE	)


class 	lowerCamelCase_     :






    '''simple docstring'''






    def __init__(	self)       ->	Optional[Any]:
        raise EnvironmentError(
            '''AutoImageProcessor is designed to be instantiated '''
            '''using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.''')







    @classmethod
    @replace_list_option_in_docstrings(__lowercase)
    def 			UpperCamelCase__   (	cls , __lowercase , **__lowercase)       ->	Union[str, Any]:
        __UpperCamelCase     :Dict			  =		kwargs.pop('''config''' , __lowercase)
        __UpperCamelCase     :List[str]			  =		kwargs.pop('''trust_remote_code''' , __lowercase)
        __UpperCamelCase     :Optional[int]			  =		True

        __UpperCamelCase  ,       __UpperCamelCase     :List[str]			  =		ImageProcessingMixin.get_image_processor_dict(__lowercase , **__lowercase)
        __UpperCamelCase     :Optional[int]			  =		config_dict.get('''image_processor_type''' , __lowercase)
        __UpperCamelCase     :Any			  =		None
        if "AutoImageProcessor" in config_dict.get('''auto_map''' , {}):
            __UpperCamelCase     :int			  =		config_dict['''auto_map''']['''AutoImageProcessor''']

        # If we still don't have the image processor class, check if we're loading from a previous feature extractor config
        # and if so, infer the image processor class from there.
        if image_processor_class is None and image_processor_auto_map is None:
            __UpperCamelCase     :Dict			  =		config_dict.pop('''feature_extractor_type''' , __lowercase)
            if feature_extractor_class is not None:
                logger.warning(
                    '''Could not find image processor class in the image processor config or the model config. Loading'''
                    ''' based on pattern matching with the model\'s feature extractor configuration.''')
                __UpperCamelCase     :str			  =		feature_extractor_class.replace('''FeatureExtractor''' , '''ImageProcessor''')
            if "AutoFeatureExtractor" in config_dict.get('''auto_map''' , {}):
                __UpperCamelCase     :Optional[int]			  =		config_dict['''auto_map''']['''AutoFeatureExtractor''']
                __UpperCamelCase     :Union[str, Any]			  =		feature_extractor_auto_map.replace('''FeatureExtractor''' , '''ImageProcessor''')
                logger.warning(
                    '''Could not find image processor auto map in the image processor config or the model config.'''
                    ''' Loading based on pattern matching with the model\'s feature extractor configuration.''')

        # If we don't find the image processor class in the image processor config, let's try the model config.
        if image_processor_class is None and image_processor_auto_map is None:
            if not isinstance(__lowercase , __lowercase):
                __UpperCamelCase     :Union[str, Any]			  =		AutoConfig.from_pretrained(__lowercase , **__lowercase)
            # It could be in `config.image_processor_type``
            __UpperCamelCase     :List[str]			  =		getattr(__lowercase , '''image_processor_type''' , __lowercase)
            if hasattr(__lowercase , '''auto_map''') and "AutoImageProcessor" in config.auto_map:
                __UpperCamelCase     :Union[str, Any]			  =		config.auto_map['''AutoImageProcessor''']

        if image_processor_class is not None:
            __UpperCamelCase     :Tuple			  =		image_processor_class_from_name(__lowercase)

        __UpperCamelCase     :Union[str, Any]			  =		image_processor_auto_map is not None
        __UpperCamelCase     :str			  =		image_processor_class is not None or type(__lowercase) in IMAGE_PROCESSOR_MAPPING
        __UpperCamelCase     :Optional[Any]			  =		resolve_trust_remote_code(
            __lowercase , __lowercase , __lowercase , __lowercase)

        if has_remote_code and trust_remote_code:
            __UpperCamelCase     :int			  =		get_class_from_dynamic_module(
                __lowercase , __lowercase , **__lowercase)
            __UpperCamelCase     :Dict			  =		kwargs.pop('''code_revision''' , __lowercase)
            if os.path.isdir(__lowercase):
                image_processor_class.register_for_auto_class()
            return image_processor_class.from_dict(__lowercase , **__lowercase)
        elif image_processor_class is not None:
            return image_processor_class.from_dict(__lowercase , **__lowercase)
        # Last try: we use the IMAGE_PROCESSOR_MAPPING.
        elif type(__lowercase) in IMAGE_PROCESSOR_MAPPING:
            __UpperCamelCase     :int			  =		IMAGE_PROCESSOR_MAPPING[type(__lowercase)]
            return image_processor_class.from_dict(__lowercase , **__lowercase)

        raise ValueError(
            f"""Unrecognized image processor in {pretrained_model_name_or_path}. Should have a """
            f"""`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following """
            f"""`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys())}""")







    @staticmethod
    def 			UpperCamelCase__   (	__lowercase , __lowercase)       ->	Union[str, Any]:
        IMAGE_PROCESSOR_MAPPING.register(__lowercase , __lowercase)









"""simple docstring"""







def    UpperCAmelCase__       (lowerCAmelCase_ = 100_0000   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				set(range(3  ,						lowerCAmelCase_  ,						2   )   )
					primes.add(2   )
					for p in range(3  ,						lowerCAmelCase_  ,						2   ):
										if p not in primes:
															continue
										primes.difference_update(set(range(p * p  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )   )   )

					__SCREAMING_SNAKE_CASE						   =				[float(lowerCAmelCase_   ) for n in range(limit + 1   )]

					for p in primes:
										for n in range(lowerCAmelCase_  ,						limit + 1  ,						lowerCAmelCase_   ):
															phi[n] *= 1 - 1 / p

					return int(sum(phi[2:]   )   )


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