



"""simple docstring"""

import random




def __lowercase			(							_a		,			_a		,			_a = False    ):
    snake_case_ :     dict				=	{i: [] for i in range(_a    )}

    # if probability is greater or equal than 1, then generate a complete graph
    if probability >= 1:
        return complete_graph(_a    )
    # if probability is lower or equal than 0, then return a graph without edges
    if probability <= 0:
        return graph

    # for each couple of nodes, add an edge from u to v
    # if the number randomly generated is greater than probability probability
    for i in range(_a    ):
        for j in range(i + 1		,			_a    ):
            if random.random() < probability:
                graph[i].append(_a    )
                if not directed:
                    # if the graph is undirected, add an edge in from j to i, either
                    graph[j].append(_a    )
    return graph




def __lowercase			(							_a    ):
    return {
        i: [j for j in range(_a    ) if i != j] for i in range(_a    )
    }


if __name__ == "__main__":
     import doctest

     doctest.testmod()










"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__    :  str							    =	{
    '''configuration_x_clip''': [
        '''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XCLIPConfig''',
        '''XCLIPTextConfig''',
        '''XCLIPVisionConfig''',
    ],
    '''processing_x_clip''': ['''XCLIPProcessor'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__    :  Tuple							    =	[
         '''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''XCLIPModel''',
         '''XCLIPPreTrainedModel''',
         '''XCLIPTextModel''',
         '''XCLIPVisionModel''',
     ]

if TYPE_CHECKING:
     from .configuration_x_clip import (
         XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
         XCLIPConfig,
         XCLIPTextConfig,
         XCLIPVisionConfig,
     )
     from .processing_x_clip import XCLIPProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_x_clip import (
              XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
              XCLIPModel,
              XCLIPPreTrainedModel,
              XCLIPTextModel,
              XCLIPVisionModel,
          )

else:
     import sys

     lowercase__    :  Tuple							    =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





