





from __future__ import annotations




def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
       print(F"""Vertex\tShortest Distance from vertex {src}"""      )
       for i, d in enumerate(lowercase_      ):
              print(F"""{i}\t\t{d}"""      )




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       for j in range(lowercase_      ):
              _lowerCamelCase							:     Optional[Any]	=		(graph[j][k] for k in ['''src''', '''dst''', '''weight'''])
              if distance[u] != float('''inf'''      ) and distance[u] + w < distance[v]:
                     return True
       return False




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     List[Any]	=		[float('''inf'''      )] * vertex_count
       _lowerCamelCase							:     List[Any]	=		0.0

       for _ in range(vertex_count - 1      ):
              for j in range(lowercase_      ):
                     _lowerCamelCase							:     str	=		(graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

                     if distance[u] != float('''inf'''      ) and distance[u] + w < distance[v]:
                            _lowerCamelCase							:     str	=		distance[u] + w

       _lowerCamelCase							:     Dict	=		check_negative_cycle(lowercase_ ,    lowercase_ ,    lowercase_      )
       if negative_cycle_exists:
              raise Exception('''Negative cycle found'''      )

       return distance


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       _lowerCamelCase			       =      int(input('Enter number of vertices: ').strip())
       _lowerCamelCase			       =      int(input('Enter number of edges: ').strip())

       _lowerCamelCase			       =      [{} for _ in range(E)]

       for i in range(E):
              print('Edge ', i + 1)
              _lowerCamelCase       ,	_lowerCamelCase       ,	_lowerCamelCase			       =      (
                  int(x)
                  for x in input('Enter source, destination, weight: ').strip().split(' ')
              )
              _lowerCamelCase			       =      {'src': src, 'dst': dest, 'weight': weight}

       _lowerCamelCase			       =      int(input('\nEnter shortest path source:').strip())
       _lowerCamelCase			       =      bellman_ford(graph, V, E, source)
       print_distance(shortest_distance, 0)






from typing import TYPE_CHECKING

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


_lowerCamelCase			       =      {'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'OPT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'OPTForCausalLM',
           'OPTModel',
           'OPTPreTrainedModel',
           'OPTForSequenceClassification',
           'OPTForQuestionAnswering',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      ['TFOPTForCausalLM', 'TFOPTModel', 'TFOPTPreTrainedModel']

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'FlaxOPTForCausalLM',
           'FlaxOPTModel',
           'FlaxOPTPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_opt import (
                  OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  OPTForCausalLM,
                  OPTForQuestionAnswering,
                  OPTForSequenceClassification,
                  OPTModel,
                  OPTPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
       import sys

       _lowerCamelCase			       =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

