


from __future__ import annotations

from collections import Counter
from random import random

class 							lowercase  :







     def __init__(						self :						int      ):
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		{}







     def 							lowercase__       (						self :						List[Any]   ,   _lowercase :						str      ):
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		{}







     def 							lowercase__       (						self :						Any   ,   _lowercase :						str   ,   _lowercase :						str   ,   _lowercase :						float      ):


           if nodea not in self.connections:
                 self.add_node(snake_case__      )
           if nodea not in self.connections:
                 self.add_node(snake_case__      )
           SCREAMING_SNAKE_CASE__       :  str						     =		probability







     def 							lowercase__       (						self :						str      ):
           return list(self.connections      )







     def 							lowercase__       (						self :						Optional[int]   ,   _lowercase :						str      ):
           SCREAMING_SNAKE_CASE__       :  int						     =		0
           SCREAMING_SNAKE_CASE__       :  Dict						     =		random()

           for dest in self.connections[node]:
                 current_probability += self.connections[node][dest]
                 if current_probability > random_value:
                       return dest
           return ""




def    a   (							A__      ,    A__      ,    A__							)	-> List[str]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  int						     =		MarkovChainGraphUndirectedUnweighted()

      for nodea, nodea, probability in transitions:
            graph.add_transition_probability(__lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase							)

      SCREAMING_SNAKE_CASE__       :  Dict						     =		Counter(graph.get_nodes()							)
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		start

      for _ in range(__lowerCamelCase							):
            SCREAMING_SNAKE_CASE__       :  Dict						     =		graph.transition(__lowerCamelCase							)
            visited[node] += 1

      return visited


if __name__ == "__main__":
    import doctest

    doctest.testmod()








def      UpperCamelCase   (       __lowerCamelCase	:					str = "The quick brown fox jumps over the lazy dog"							,   ):
       snake_case			:  Dict     							= set()

       # Replace all the whitespace in our sentence
       snake_case			:  List[Any]     							= input_str.replace(" "							,   ""       )
       for alpha in input_str:
              if "a" <= alpha.lower() <= "z":
                     frequency.add(alpha.lower()       )
       return len(__lowerCamelCase       ) == 26


def      UpperCamelCase   (       __lowerCamelCase	:					str = "The quick brown fox jumps over the lazy dog"							,   ):
       snake_case			:  Optional[int]     							= [False] * 26
       for char in input_str:
              if char.islower():
                     snake_case			:  Dict     							= True
              elif char.isupper():
                     snake_case			:  Optional[Any]     							= True
       return all(__lowerCamelCase       )


def      UpperCamelCase   (       __lowerCamelCase	:					str = "The quick brown fox jumps over the lazy dog"							,   ):
       return len({char for char in input_str.lower() if char.isalpha()}       ) == 26


def      UpperCamelCase   (       ):
       from timeit import timeit

       snake_case			:  Tuple     							= "from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
       print(timeit("is_pangram()"							,   setup=__lowerCamelCase       )       )
       print(timeit("is_pangram_faster()"							,   setup=__lowerCamelCase       )       )
       print(timeit("is_pangram_fastest()"							,   setup=__lowerCamelCase       )       )
       # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
       # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   benchmark()





