



'''simple docstring'''





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,
)


SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      					=     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"),
    ]
)

SCREAMING_SNAKE_CASE__      					=     _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)




def        lowerCamelCase  (				_snake_case  :						str       ):





    '''simple docstring'''


    for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
        if class_name in extractors:
            lowercase__		       =						model_type_to_module_name(_snake_case       )

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

    for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
        if getattr(_snake_case     ,"__name__"     ,_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.
    lowercase__		       =						importlib.import_module("transformers"       )
    if hasattr(_snake_case     ,_snake_case       ):
        return getattr(_snake_case     ,_snake_case       )

    return None




def        lowerCamelCase  (				_snake_case  :						Union[str, os.PathLike]     ,_snake_case  :						Optional[Union[str, os.PathLike]] = None     ,_snake_case  :						bool = False     ,_snake_case  :						bool = False     ,_snake_case  :						Optional[Dict[str, str]] = None     ,_snake_case  :						Optional[Union[bool, str]] = None     ,_snake_case  :						Optional[str] = None     ,_snake_case  :						bool = False     ,**_snake_case  :						Any     ,):





    '''simple docstring'''


    lowercase__		       =						get_file_from_repo(
        _snake_case     ,_snake_case     ,cache_dir=_snake_case     ,force_download=_snake_case     ,resume_download=_snake_case     ,proxies=_snake_case     ,use_auth_token=_snake_case     ,revision=_snake_case     ,local_files_only=_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(_snake_case     ,encoding="utf-8"       ) as reader:
        return json.load(_snake_case       )



class 				snake_case :



       def __init__(				self      )  ->							int:
           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(UpperCAmelCase_      )
       def 							_a      (				cls     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							str:
           lowercase__		       =						kwargs.pop("config"     ,UpperCAmelCase_      )
           lowercase__		       =						kwargs.pop("trust_remote_code"     ,UpperCAmelCase_      )
           lowercase__		       =						True

           lowercase__       ,       lowercase__		       =						ImageProcessingMixin.get_image_processor_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )
           lowercase__		       =						config_dict.get("image_processor_type"     ,UpperCAmelCase_      )
           lowercase__		       =						None
           if "AutoImageProcessor" in config_dict.get("auto_map"     ,{}      ):
               lowercase__		       =						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:
               lowercase__		       =						config_dict.pop("feature_extractor_type"     ,UpperCAmelCase_      )
               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."      )
                   lowercase__		       =						feature_extractor_class.replace("FeatureExtractor"     ,"ImageProcessor"      )
               if "AutoFeatureExtractor" in config_dict.get("auto_map"     ,{}      ):
                   lowercase__		       =						config_dict["auto_map"]["AutoFeatureExtractor"]
                   lowercase__		       =						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(UpperCAmelCase_     ,UpperCAmelCase_      ):
                   lowercase__		       =						AutoConfig.from_pretrained(UpperCAmelCase_     ,**UpperCAmelCase_      )
               # It could be in `config.image_processor_type``
               lowercase__		       =						getattr(UpperCAmelCase_     ,"image_processor_type"     ,UpperCAmelCase_      )
               if hasattr(UpperCAmelCase_     ,"auto_map"      ) and "AutoImageProcessor" in config.auto_map:
                   lowercase__		       =						config.auto_map["AutoImageProcessor"]

           if image_processor_class is not None:
               lowercase__		       =						image_processor_class_from_name(UpperCAmelCase_      )

           lowercase__		       =						image_processor_auto_map is not None
           lowercase__		       =						image_processor_class is not None or type(UpperCAmelCase_      ) in IMAGE_PROCESSOR_MAPPING
           lowercase__		       =						resolve_trust_remote_code(
               UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )

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

           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 							_a      (				UpperCAmelCase_     ,UpperCAmelCase_      )  ->							int:
           IMAGE_PROCESSOR_MAPPING.register(UpperCAmelCase_     ,UpperCAmelCase_      )









'''simple docstring'''





def        lowerCamelCase  (				_snake_case  :						List[str]       ):  # noqa: E741





    '''simple docstring'''


    lowercase__		       =						len(_snake_case       )
    lowercase__		       =						0
    lowercase__		       =						[0] * n
    lowercase__		       =						[False] * n
    lowercase__		       =						[False] * n

    def dfs(_snake_case  :						Tuple     ,_snake_case  :						int     ,_snake_case  :						List[str]     ,_snake_case  :						str       ):
        if parent == root:
            out_edge_count += 1
        lowercase__		       =						True
        lowercase__		       =						at

        for to in l[at]:
            if to == parent:
                pass
            elif not visited[to]:
                lowercase__		       =						dfs(_snake_case     ,_snake_case     ,_snake_case     ,_snake_case       )
                lowercase__		       =						min(low[at]     ,low[to]       )

                # AP found via bridge
                if at < low[to]:
                    lowercase__		       =						True
                # AP found via cycle
                if at == low[to]:
                    lowercase__		       =						True
            else:
                lowercase__		       =						min(low[at]     ,_snake_case       )
        return out_edge_count

    for i in range(_snake_case       ):
        if not visited[i]:
            lowercase__		       =						0
            lowercase__		       =						dfs(_snake_case     ,_snake_case     ,-1     ,_snake_case       )
            lowercase__		       =						out_edge_count > 1

    for x in range(len(_snake_case       )       ):
        if is_art[x] is True:
            print(_snake_case       )


# Adjacency list of graph
SCREAMING_SNAKE_CASE__      					=     {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}



compute_ap(data)




