




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_UpperCAmelCase	:						Tuple			=     {
    "configuration_clip": [
        "CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPConfig",
        "CLIPOnnxConfig",
        "CLIPTextConfig",
        "CLIPVisionConfig",
    ],
    "processing_clip": ["CLIPProcessor"],
    "tokenization_clip": ["CLIPTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase	:						List[str]			=     ["CLIPTokenizerFast"]

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase	:						List[Any]			=     ["CLIPFeatureExtractor"]
     _UpperCAmelCase	:						str			=     ["CLIPImageProcessor"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase	:						Dict			=     [
         "CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
         "CLIPModel",
         "CLIPPreTrainedModel",
         "CLIPTextModel",
         "CLIPTextModelWithProjection",
         "CLIPVisionModel",
         "CLIPVisionModelWithProjection",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase	:						List[str]			=     [
         "TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFCLIPModel",
         "TFCLIPPreTrainedModel",
         "TFCLIPTextModel",
         "TFCLIPVisionModel",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase	:						Optional[int]			=     [
         "FlaxCLIPModel",
         "FlaxCLIPPreTrainedModel",
         "FlaxCLIPTextModel",
         "FlaxCLIPTextPreTrainedModel",
         "FlaxCLIPVisionModel",
         "FlaxCLIPVisionPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_clip import (
         CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
         CLIPConfig,
         CLIPOnnxConfig,
         CLIPTextConfig,
         CLIPVisionConfig,
     )
     from .processing_clip import CLIPProcessor
     from .tokenization_clip import CLIPTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_clip_fast import CLIPTokenizerFast

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_clip import CLIPFeatureExtractor
          from .image_processing_clip import CLIPImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_clip import (
              CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
              CLIPModel,
              CLIPPreTrainedModel,
              CLIPTextModel,
              CLIPTextModelWithProjection,
              CLIPVisionModel,
              CLIPVisionModelWithProjection,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_clip import (
              TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFCLIPModel,
              TFCLIPPreTrainedModel,
              TFCLIPTextModel,
              TFCLIPVisionModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_clip import (
              FlaxCLIPModel,
              FlaxCLIPPreTrainedModel,
              FlaxCLIPTextModel,
              FlaxCLIPTextPreTrainedModel,
              FlaxCLIPVisionModel,
              FlaxCLIPVisionPreTrainedModel,
          )


else:
     import sys

     _UpperCAmelCase	:						Union[str, Any]			=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst



def       UpperCAmelCase__  (     ):
  lowercase	,    lowercase    :Union[str, Any]      	=						9, 14  # noqa: F841
  lowercase    :Optional[Any]      	=						[
      [0, 1, 4],
      [0, 7, 8],
      [1, 2, 8],
      [7, 8, 7],
      [7, 6, 1],
      [2, 8, 2],
      [8, 6, 6],
      [2, 3, 7],
      [2, 5, 4],
      [6, 5, 2],
      [3, 5, 14],
      [3, 4, 9],
      [5, 4, 10],
      [1, 7, 11],
  ]

  lowercase    :Optional[Any]      	=						defaultdict(lowerCamelCase   )
  for nodea, nodea, cost in edges:
    adjancency[nodea].append([nodea, cost]   )
    adjancency[nodea].append([nodea, cost]   )

  lowercase    :Optional[int]      	=						mst(lowerCamelCase   )

  lowercase    :Any      	=						[
      [7, 6, 1],
      [2, 8, 2],
      [6, 5, 2],
      [0, 1, 4],
      [2, 5, 4],
      [2, 3, 7],
      [0, 7, 8],
      [3, 4, 9],
  ]

  for answer in expected:
    lowercase    :Any      	=						tuple(answer[:2]   )
    lowercase    :str      	=						tuple(edge[::-1]   )
    assert edge in result or reverse in result



