



'''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,
)


_lowerCamelCase	:       str      	=	logging.get_logger(__name__)

_lowerCamelCase	:       Optional[Any]      	=	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"),
    ]
)

_lowerCamelCase	:       List[Any]      	=	_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)







def 				__lowerCamelCase				(   A__    )       ->	Optional[int]:






   """simple docstring"""
   for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
      if class_name in extractors:
         UpperCamelCase					=  model_type_to_module_name(A__    )

         UpperCamelCase					=  importlib.import_module(F""".{module_name}"""    ,						'transformers.models'    )
         try:
            return getattr(A__    ,						A__    )
         except AttributeError:
            continue

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

   return None







def 				__lowerCamelCase				(   A__    ,						A__ = None    ,						A__ = False    ,						A__ = False    ,						A__ = None    ,						A__ = None    ,						A__ = None    ,						A__ = False    ,						**A__    ,						)       ->	int:






   """simple docstring"""
   UpperCamelCase					=  get_file_from_repo(
       A__    ,						A__    ,						cache_dir=A__    ,						force_download=A__    ,						resume_download=A__    ,						proxies=A__    ,						use_auth_token=A__    ,						revision=A__    ,						local_files_only=A__    ,						)
   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(A__    ,						encoding='utf-8'    ) as reader:
      return json.load(A__    )

class  SCREAMING_SNAKE_CASE :






    """simple docstring"""





    def __init__(  self      :					str ):


       """simple docstring"""







       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      :					Optional[int]  ,						UpperCamelCase__      :					str  ,						**UpperCamelCase__      :					int ):


       """simple docstring"""







       UpperCamelCase					=  kwargs.pop('config'  ,						UpperCamelCase__ )
       UpperCamelCase					=  kwargs.pop('trust_remote_code'  ,						UpperCamelCase__ )
       UpperCamelCase					=  True

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

       if image_processor_class is not None:
          UpperCamelCase					=  image_processor_class_from_name(UpperCamelCase__ )

       UpperCamelCase					=  image_processor_auto_map is not None
       UpperCamelCase					=  image_processor_class is not None or type(UpperCamelCase__ ) in IMAGE_PROCESSOR_MAPPING
       UpperCamelCase					=  resolve_trust_remote_code(
           UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )

       if has_remote_code and trust_remote_code:
          UpperCamelCase					=  get_class_from_dynamic_module(
              UpperCamelCase__  ,						UpperCamelCase__  ,						**UpperCamelCase__ )
          UpperCamelCase					=  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:
          UpperCamelCase					=  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__      :					Dict  ,						UpperCamelCase__      :					int ):


       """simple docstring"""







       IMAGE_PROCESSOR_MAPPING.register(UpperCamelCase__  ,						UpperCamelCase__ )






'''simple docstring'''


import math

class  SCREAMING_SNAKE_CASE :






    """simple docstring"""





    def __init__(  self      :					Union[str, Any]  ,						UpperCamelCase__      :					Optional[Any]=0 ):  # a graph with Node 0,1,...,N-1


       """simple docstring"""







       UpperCamelCase					=  n
       UpperCamelCase					=  [
           [math.inf for j in range(0  ,						UpperCamelCase__ )] for i in range(0  ,						UpperCamelCase__ )
       ]  # adjacency matrix for weight
       UpperCamelCase					=  [
           [math.inf for j in range(0  ,						UpperCamelCase__ )] for i in range(0  ,						UpperCamelCase__ )
       ]  # dp[i][j] stores minimum distance from i to j






    def  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  w






    def  A       (  self      :					str ):


       """simple docstring"""







       for k in range(0  ,						self.n ):
          for i in range(0  ,						self.n ):
             for j in range(0  ,						self.n ):
                UpperCamelCase					=  min(self.dp[i][j]  ,						self.dp[i][k] + self.dp[k][j] )






    def  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					List[Any] ):


       """simple docstring"""







       return self.dp[u][v]


if __name__ == "__main__":
   _lowerCamelCase	:       List[str]      	=	Graph(5)
   graph.add_edge(0, 2, 9)
   graph.add_edge(0, 4, 10)
   graph.add_edge(1, 3, 5)
   graph.add_edge(2, 3, 7)
   graph.add_edge(3, 0, 10)
   graph.add_edge(3, 1, 2)
   graph.add_edge(3, 2, 1)
   graph.add_edge(3, 4, 6)
   graph.add_edge(4, 1, 3)
   graph.add_edge(4, 2, 4)
   graph.add_edge(4, 3, 9)
   graph.floyd_warshall()
   graph.show_min(1, 4)
   graph.show_min(0, 3)

