


from random import randint
from tempfile import TemporaryFile

import numpy as np







def        SCREAMING_SNAKE_CASE_						(			__A :			Any  ,    __A :			List[str]  ,    __A :			List[str]				)	->		Optional[int]:

       """simple docstring"""

       a_					:		int							=  0
       if start < end:
              a_					:		int							=  randint(__A  ,    __A				)
              a_					:		List[Any]							=  a[end]
              a_					:		Optional[int]							=  a[pivot]
              a_					:		Dict							=  temp

              a_							,		a_					:		List[str]							=  _in_place_partition(__A  ,    __A  ,    __A				)
              count += _in_place_quick_sort(__A  ,    __A  ,    p - 1				)
              count += _in_place_quick_sort(__A  ,    p + 1  ,    __A				)
       return count







def        SCREAMING_SNAKE_CASE_						(			__A :			List[str]  ,    __A :			Dict  ,    __A :			Optional[Any]				)	->		Optional[int]:

       """simple docstring"""

       a_					:		List[str]							=  0
       a_					:		int							=  randint(__A  ,    __A				)
       a_					:		Optional[int]							=  a[end]
       a_					:		List[str]							=  a[pivot]
       a_					:		Tuple							=  temp
       a_					:		Optional[int]							=  start - 1
       for index in range(__A  ,    __A				):
              count += 1
              if a[index] < a[end]:  # check if current val is less than pivot value
                     a_					:		List[Any]							=  new_pivot_index + 1
                     a_					:		Dict							=  a[new_pivot_index]
                     a_					:		Optional[Any]							=  a[index]
                     a_					:		Any							=  temp

       a_					:		str							=  a[new_pivot_index + 1]
       a_					:		List[str]							=  a[end]
       a_					:		Dict							=  temp
       return new_pivot_index + 1, count


UpperCAmelCase_      : int  					=       TemporaryFile()
UpperCAmelCase_      : Dict  					=       100  # 1000 elements are to be sorted


UpperCAmelCase_					,   UpperCAmelCase_      : Optional[Any]  					=       0, 1  # mean and standard deviation
UpperCAmelCase_      : List[Any]  					=       np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
UpperCAmelCase_      : Any  					=       np.load(outfile)
UpperCAmelCase_      : Optional[int]  					=       len(M) - 1
UpperCAmelCase_      : Tuple  					=       _in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)
print(z)






__lowerCAmelCase		       =							{
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}




def    snake_case_     (  snake_case			,							snake_case			,							snake_case     )  ->				list[str]:
       lowercase__:   int  			=   set()
       # keep track of all the paths to be checked
       lowercase__:   Optional[int]  			=   [[start]]

       # return path if start is goal
       if start == goal:
              return [start]

       # keeps looping until all possible paths have been checked
       while queue:
              # pop the first path from the queue
              lowercase__:   List[Any]  			=   queue.pop(0     )
              # get the last node from the path
              lowercase__:   Optional[int]  			=   path[-1]
              if node not in explored:
                     lowercase__:   Optional[Any]  			=   graph[node]
                     # go through all neighbour nodes, construct a new path and
                     # push it into the queue
                     for neighbour in neighbours:
                            lowercase__:   Tuple  			=   list(snake_case     )
                            new_path.append(snake_case     )
                            queue.append(snake_case     )
                            # return path if neighbour is goal
                            if neighbour == goal:
                                   return new_path

            # mark node as explored
                     explored.add(snake_case     )

    # in case there's no path between the 2 nodes
       return []




def    snake_case_     (  snake_case			,							snake_case			,							snake_case     )  ->				int:
       if not graph or start not in graph or target not in graph:
              return -1
       if start == target:
              return 0
       lowercase__:   Tuple  			=   [start]
       lowercase__:   List[Any]  			=   set(snake_case     )
       # Keep tab on distances from `start` node.
       lowercase__:   Tuple  			=   {start: 0, target: -1}
       while queue:
              lowercase__:   Dict  			=   queue.pop(0     )
              if node == target:
                     lowercase__:   str  			=   (
                         dist[node] if dist[target] == -1 else min(dist[target]			,							dist[node]     )
                     )
              for adjacent in graph[node]:
                     if adjacent not in visited:
                            visited.add(snake_case     )
                            queue.append(snake_case     )
                            lowercase__:   List[str]  			=   dist[node] + 1
       return dist[target]


if __name__ == "__main__":
 print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
 print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4


