


from typing import TYPE_CHECKING

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


snake_case				:    Union[str, Any]       				=     {
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case				:    Any       				=     ['''DebertaTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case				:    Optional[Any]       				=     [
         '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''DebertaForMaskedLM''',
         '''DebertaForQuestionAnswering''',
         '''DebertaForSequenceClassification''',
         '''DebertaForTokenClassification''',
         '''DebertaModel''',
         '''DebertaPreTrainedModel''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case				:    Any       				=     [
         '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFDebertaForMaskedLM''',
         '''TFDebertaForQuestionAnswering''',
         '''TFDebertaForSequenceClassification''',
         '''TFDebertaForTokenClassification''',
         '''TFDebertaModel''',
         '''TFDebertaPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
     from .tokenization_deberta import DebertaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_deberta_fast import DebertaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_deberta import (
              DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              DebertaForMaskedLM,
              DebertaForQuestionAnswering,
              DebertaForSequenceClassification,
              DebertaForTokenClassification,
              DebertaModel,
              DebertaPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_deberta import (
              TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFDebertaForMaskedLM,
              TFDebertaForQuestionAnswering,
              TFDebertaForSequenceClassification,
              TFDebertaForTokenClassification,
              TFDebertaModel,
              TFDebertaPreTrainedModel,
          )


else:
     import sys

     snake_case				:    int       				=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









from __future__ import annotations
def 				__lowerCamelCase						( UpperCAmelCase_						:		dict ,  UpperCAmelCase_						:		str						):




      """simple docstring"""





      a							,				a						:Optional[Any] 							=       set(UpperCAmelCase_						), [start]

      while stack:
            a						:Optional[int] 							=       stack.pop()
            explored.add(UpperCAmelCase_						)
            # Differences from BFS:
            # 1) pop last element instead of first one
            # 2) add adjacent elements to stack without exploring them
            for adj in reversed(graph[v]						):
                  if adj not in explored:
                        stack.append(UpperCAmelCase_						)
      return explored


snake_case				:    Optional[int]       				=     {
    '''A''': ['''B''', '''C''', '''D'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F'''],
    '''D''': ['''B''', '''D'''],
    '''E''': ['''B''', '''F'''],
    '''F''': ['''C''', '''E''', '''G'''],
    '''G''': ['''F'''],
}

if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(depth_first_search(G, '''A'''))





