'''simple docstring'''




from typing import TYPE_CHECKING

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


_a   :	Optional[Any] 							=   {
    "configuration_rembert": ["REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RemBertConfig", "RemBertOnnxConfig"]
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	Optional[Any] 							=   ["RemBertTokenizer"]

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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	int 							=   [
       "REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "RemBertForCausalLM",
       "RemBertForMaskedLM",
       "RemBertForMultipleChoice",
       "RemBertForQuestionAnswering",
       "RemBertForSequenceClassification",
       "RemBertForTokenClassification",
       "RemBertLayer",
       "RemBertModel",
       "RemBertPreTrainedModel",
       "load_tf_weights_in_rembert",
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	Any 							=   [
       "TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFRemBertForCausalLM",
       "TFRemBertForMaskedLM",
       "TFRemBertForMultipleChoice",
       "TFRemBertForQuestionAnswering",
       "TFRemBertForSequenceClassification",
       "TFRemBertForTokenClassification",
       "TFRemBertLayer",
       "TFRemBertModel",
       "TFRemBertPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_rembert import RemBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_rembert_fast import RemBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_rembert import (
          REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          RemBertForCausalLM,
          RemBertForMaskedLM,
          RemBertForMultipleChoice,
          RemBertForQuestionAnswering,
          RemBertForSequenceClassification,
          RemBertForTokenClassification,
          RemBertLayer,
          RemBertModel,
          RemBertPreTrainedModel,
          load_tf_weights_in_rembert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_rembert import (
          TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRemBertForCausalLM,
          TFRemBertForMaskedLM,
          TFRemBertForMultipleChoice,
          TFRemBertForQuestionAnswering,
          TFRemBertForSequenceClassification,
          TFRemBertForTokenClassification,
          TFRemBertLayer,
          TFRemBertModel,
          TFRemBertPreTrainedModel,
      )


else:
   import sys

   _a   :	Tuple 							=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







'''simple docstring'''




def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       bool:





       """simple docstring"""


       return not any(
           neighbour == 1 and colored_vertices[i] == color
           for i, neighbour in enumerate(lowerCamelCase__						)						)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       bool:





       """simple docstring"""


       if index == len(lowerCamelCase__						):
              return True

       # Recursive Step
       for i in range(lowerCamelCase__						):
              if valid_coloring(graph[index]							,    lowerCamelCase__							,    lowerCamelCase__						):
                     # Color current vertex
                     __UpperCAmelCase  :				List[str]       				=		i
                     # Validate coloring
                     if util_color(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    index + 1						):
                            return True
                     # Backtrack
                     __UpperCAmelCase  :				Any       				=		-1
       return False


def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       list[int]:





       """simple docstring"""


       __UpperCAmelCase  :				Optional[Any]       				=		[-1] * len(lowerCamelCase__						)

       if util_color(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    0						):
              return colored_vertices

       return []






