





'''simple docstring'''




from typing import TYPE_CHECKING

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


lowercase__   :				Tuple      =   {
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__   :				Dict      =   ['SpeechT5Tokenizer']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__   :				Tuple      =   [
      'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
      'SpeechT5ForSpeechToText',
      'SpeechT5ForSpeechToSpeech',
      'SpeechT5ForTextToSpeech',
      'SpeechT5Model',
      'SpeechT5PreTrainedModel',
      'SpeechT5HifiGan',
  ]

if TYPE_CHECKING:
  from .configuration_speechta import (
      SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
      SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
      SpeechTaConfig,
      SpeechTaHifiGanConfig,
  )
  from .feature_extraction_speechta import SpeechTaFeatureExtractor
  from .processing_speechta import SpeechTaProcessor

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_speechta import SpeechTaTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_speechta import (
        SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
        SpeechTaForSpeechToSpeech,
        SpeechTaForSpeechToText,
        SpeechTaForTextToSpeech,
        SpeechTaHifiGan,
        SpeechTaModel,
        SpeechTaPreTrainedModel,
    )

else:
  import sys

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






'''simple docstring'''




def 	_lowerCAmelCase      (  __snake_case  :			str      )					->					str:
     __A						:			Optional[Any]								=	[0] * len(__snake_case      )
     __A						:			Dict								=	[]
     __A						:			Optional[int]								=	[1] * len(__snake_case      )

     for values in graph.values():
          for i in values:
               indegree[i] += 1

     for i in range(len(__snake_case      )      ):
          if indegree[i] == 0:
               queue.append(__snake_case      )

     while queue:
          __A						:			int								=	queue.pop(0      )
          for x in graph[vertex]:
               indegree[x] -= 1

               if long_dist[vertex] + 1 > long_dist[x]:
                    __A						:			str								=	long_dist[vertex] + 1

               if indegree[x] == 0:
                    queue.append(__snake_case      )

     print(max(__snake_case      )      )


# Adjacency list of Graph
lowercase__   :				Dict      =   {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}







longest_distance(graph)