
import math





class 							UpperCAmelCase      :




	'''simple docstring'''





	def __init__(       self					: Optional[int]							,A					: Dict=0 ):  # a graph with Node 0,1,...,N-1
					__A   			= n
					__A   			= [
					    [math.inf for j in range(0							,A )] for i in range(0							,A )
					]  # adjacency matrix for weight
					__A   			= [
					    [math.inf for j in range(0							,A )] for i in range(0							,A )
					]  # dp[i][j] stores minimum distance from i to j






	def 				UpperCamelCase_  (       self					: List[str]							,A					: Tuple							,A					: Union[str, Any]							,A					: Optional[int] ):
					__A   			= w






	def 				UpperCamelCase_  (       self					: int ):
					for k in range(0							,self.n ):
									for i in range(0							,self.n ):
													for j in range(0							,self.n ):
																	__A   			= min(self.dp[i][j]							,self.dp[i][k] + self.dp[k][j] )







	def 				UpperCamelCase_  (       self					: Optional[int]							,A					: str							,A					: Union[str, Any] ):
					return self.dp[u][v]


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				Graph(5)
					graph.add_edge(0, 2, 9)
					graph.add_edge(0, 4, 10)
					graph.add_edge(1, 3, 5)
					graph.add_edge(2, 3, 7)
					graph.add_edge(3, 0, 10)
					graph.add_edge(3, 1, 2)
					graph.add_edge(3, 2, 1)
					graph.add_edge(3, 4, 6)
					graph.add_edge(4, 1, 3)
					graph.add_edge(4, 2, 4)
					graph.add_edge(4, 3, 9)
					graph.floyd_warshall()
					graph.show_min(1, 4)
					graph.show_min(0, 3)

"""simple docstring"""






from typing import TYPE_CHECKING

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


__A			   = {
    "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:
       __A			   = ["SpeechT5Tokenizer"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = [
           "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

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