

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   a   (    A__			:     Tuple  )					->   int:





      """simple docstring"""



      for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
            if class_name in extractors:
                  _lowercase			 =model_type_to_module_name(A__  )

                  _lowercase			 =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.
      _lowercase			 =importlib.import_module('transformers'  )
      if hasattr(A__   ,     A__  ):
            return getattr(A__   ,     A__  )

      return None
def   a   (    A__			:     Any   ,     A__			:     Union[str, Any] = None   ,     A__			:     Union[str, Any] = False   ,     A__			:     Tuple = False   ,     A__			:     List[Any] = None   ,     A__			:     Optional[Any] = None   ,     A__			:     Any = None   ,     A__			:     int = False   ,     **A__			:     Any   ,     )					->   int:





      """simple docstring"""



      _lowercase			 =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 				__lowerCAmelCase  :




  def __init__(       self      )						->				List[Any]:
        '''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(lowerCAmelCase      )
  def 						A__  (       cls							,     lowerCAmelCase							,     **lowerCAmelCase      )						->				Tuple:
        '''simple docstring'''






        _lowercase			 =kwargs.pop('config'							,     lowerCAmelCase      )
        _lowercase			 =kwargs.pop('trust_remote_code'							,     lowerCAmelCase      )
        _lowercase			 =True

        _lowercase , _lowercase			 =ImageProcessingMixin.get_image_processor_dict(lowerCAmelCase							,     **lowerCAmelCase      )
        _lowercase			 =config_dict.get('image_processor_type'							,     lowerCAmelCase      )
        _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'							,     lowerCAmelCase      )
              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(lowerCAmelCase							,     lowerCAmelCase      ):
                    _lowercase			 =AutoConfig.from_pretrained(lowerCAmelCase							,     **lowerCAmelCase      )
              # It could be in `config.image_processor_type``
              _lowercase			 =getattr(lowerCAmelCase							,     'image_processor_type'							,     lowerCAmelCase      )
              if hasattr(lowerCAmelCase							,     '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(lowerCAmelCase      )

        _lowercase			 =image_processor_auto_map is not None
        _lowercase			 =image_processor_class is not None or type(lowerCAmelCase      ) in IMAGE_PROCESSOR_MAPPING
        _lowercase			 =resolve_trust_remote_code(
            lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )

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

        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__  (       lowerCAmelCase							,     lowerCAmelCase      )						->				int:
        '''simple docstring'''






        IMAGE_PROCESSOR_MAPPING.register(lowerCAmelCase							,     lowerCAmelCase      )





from __future__ import annotations

from typing import Any





class 				__lowerCAmelCase  :




  def __init__(       self							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        _lowercase			 =num_of_nodes
        _lowercase			 =[]
        _lowercase			 ={}




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        self.m_edges.append([u_node, v_node, weight]      )




  def 						A__  (       self							,     lowerCAmelCase      )						->				int:
        '''simple docstring'''






        if self.m_component[u_node] == u_node:
              return u_node
        return self.find_component(self.m_component[u_node]      )




  def 						A__  (       self							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        if self.m_component[u_node] != u_node:
              for k in self.m_component:
                    _lowercase			 =self.find_component(lowerCAmelCase      )




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        if component_size[u_node] <= component_size[v_node]:
              _lowercase			 =v_node
              component_size[v_node] += component_size[u_node]
              self.set_component(lowerCAmelCase      )

        elif component_size[u_node] >= component_size[v_node]:
              _lowercase			 =self.find_component(lowerCAmelCase      )
              component_size[u_node] += component_size[v_node]
              self.set_component(lowerCAmelCase      )




  def 						A__  (       self      )						->				None:
        '''simple docstring'''






        _lowercase			 =[]
        _lowercase			 =0

        _lowercase			 =[-1] * self.m_num_of_nodes

        # A list of components (initialized to all of the nodes)
        for node in range(self.m_num_of_nodes      ):
              self.m_component.update({node: node}      )
              component_size.append(1      )

        _lowercase			 =self.m_num_of_nodes

        while num_of_components > 1:
              for edge in self.m_edges:
                    _lowercase , _lowercase , _lowercase			 =edge

                    _lowercase			 =self.m_component[u]
                    _lowercase			 =self.m_component[v]

                    if u_component != v_component:

                          for component in (u_component, v_component):
                                if (
                                    minimum_weight_edge[component] == -1
                                    or minimum_weight_edge[component][2] > w
                                ):
                                      _lowercase			 =[u, v, w]

              for edge in minimum_weight_edge:
                    if isinstance(lowerCAmelCase							,     lowerCAmelCase      ):
                          _lowercase , _lowercase , _lowercase			 =edge

                          _lowercase			 =self.m_component[u]
                          _lowercase			 =self.m_component[v]

                          if u_component != v_component:
                                mst_weight += w
                                self.union(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )
                                print(F'''Added edge [{u} - {v}]\nAdded weight: {w}\n'''      )
                                num_of_components -= 1

              _lowercase			 =[-1] * self.m_num_of_nodes
        print(F'''The total weight of the minimal spanning tree is: {mst_weight}'''      )
def   a   (    )					->   None:





      """simple docstring"""





if __name__ == "__main__":
  import doctest

  doctest.testmod()


