






'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCamelCase							={
    "configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
    "tokenization_ctrl": ["CTRLTokenizer"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase							=[
          "CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
          "CTRLForSequenceClassification",
          "CTRLLMHeadModel",
          "CTRLModel",
          "CTRLPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase							=[
          "TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFCTRLForSequenceClassification",
          "TFCTRLLMHeadModel",
          "TFCTRLModel",
          "TFCTRLPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
      from .tokenization_ctrl import CTRLTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_ctrl import (
                CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
                CTRLForSequenceClassification,
                CTRLLMHeadModel,
                CTRLModel,
                CTRLPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_ctrl import (
                TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFCTRLForSequenceClassification,
                TFCTRLLMHeadModel,
                TFCTRLModel,
                TFCTRLPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase							=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)














'''simple docstring'''

import random



def 							snake_case					(     a_							: int    ,   a_							: float    ,   a_							: bool = False	)       ->				dict:





      """simple docstring"""

      UpperCamelCase_				:   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 							snake_case					(     a_							: int	)       ->				dict:





      """simple docstring"""

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


if __name__ == "__main__":
      import doctest

      doctest.testmod()






