
from __future__ import annotations

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Union[str, Any]:


  '''simple docstring'''






  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_ )		->						Tuple:


  '''simple docstring'''






  for j in range(lowercase_ ):
    lowercase__		,     lowercase__		,     lowercase__	: str     		=   (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_ )		->						list[float]:


  '''simple docstring'''






  lowercase__	: Tuple     		=   [float("""inf""" )] * vertex_count
  lowercase__	: Optional[int]     		=   0.0

  for _ in range(vertex_count - 1 ):
    for j in range(lowercase_ ):
      lowercase__		,     lowercase__		,     lowercase__	: int     		=   (graph[j][k] for k in ["""src""", """dst""", """weight"""])

      if distance[u] != float("""inf""" ) and distance[u] + w < distance[v]:
        lowercase__	: Optional[Any]     		=   distance[u] + w

  lowercase__	: Union[str, Any]     		=   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__     :		Dict        = int(input("""Enter number of vertices: """).strip())
     lowerCamelCase__     :		Tuple        = int(input("""Enter number of edges: """).strip())

     lowerCamelCase__     :		list[dict[str, int]]        = [{} for _ in range(E)]

     for i in range(E):
          print("""Edge """, i + 1)
          lowerCamelCase__   ,    lowerCamelCase__   ,    lowerCamelCase__     :		Optional[Any]        = (
              int(x)
              for x in input("""Enter source, destination, weight: """).strip().split(""" """)
          )
          lowerCamelCase__     :		Union[str, Any]        = {"""src""": src, """dst""": dest, """weight""": weight}

     lowerCamelCase__     :		Union[str, Any]        = int(input("""\nEnter shortest path source:""").strip())
     lowerCamelCase__     :		Any        = bellman_ford(graph, V, E, source)
     print_distance(shortest_distance, 0)








# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__     :		Tuple        = {
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__     :		Optional[int]        = [
         """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """MgpstrModel""",
         """MgpstrPreTrainedModel""",
         """MgpstrForSceneTextRecognition""",
     ]

if TYPE_CHECKING:
     from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
     from .processing_mgp_str import MgpstrProcessor
     from .tokenization_mgp_str import MgpstrTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mgp_str import (
              MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
              MgpstrForSceneTextRecognition,
              MgpstrModel,
              MgpstrPreTrainedModel,
          )
else:
     import sys

     lowerCamelCase__     :		List[Any]        = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






