


from typing import TYPE_CHECKING

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


if is_sentencepiece_available():
     from ..ta.tokenization_ta import TaTokenizer
else:
     from ...utils.dummy_sentencepiece_objects import TaTokenizer

snake_case				:    Dict       				=     TaTokenizer

if is_tokenizers_available():
     from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
     from ...utils.dummy_tokenizers_objects import TaTokenizerFast

snake_case				:    Dict       				=     TaTokenizerFast

snake_case				:    str       				=     {'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case				:    Dict       				=     [
         '''MT5EncoderModel''',
         '''MT5ForConditionalGeneration''',
         '''MT5ForQuestionAnswering''',
         '''MT5Model''',
         '''MT5PreTrainedModel''',
         '''MT5Stack''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case				:    List[str]       				=     ['''TFMT5EncoderModel''', '''TFMT5ForConditionalGeneration''', '''TFMT5Model''']

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case				:    Dict       				=     ['''FlaxMT5EncoderModel''', '''FlaxMT5ForConditionalGeneration''', '''FlaxMT5Model''']


if TYPE_CHECKING:
     from .configuration_mta import MTaConfig, MTaOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mta import (
              MTaEncoderModel,
              MTaForConditionalGeneration,
              MTaForQuestionAnswering,
              MTaModel,
              MTaPreTrainedModel,
              MTaStack,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
     import sys

     snake_case				:    Any       				=     _LazyModule(
         __name__,
         globals()['''__file__'''],
         _import_structure,
         extra_objects={'''MT5Tokenizer''': MTaTokenizer, '''MT5TokenizerFast''': MTaTokenizerFast},
         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'''))





