





"""simple docstring"""



from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass






@dataclass
class 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     42
 _lowerCamelCase        =     42






class 		__snake_case					:





 """simple docstring"""


 def __init__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						list[list[Edge]]						 =     [[] for _ in range(__lowerCamelCase  )]
     __A    :						Optional[int]						 =     size

 def __getitem__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     return iter(self._graph[vertex]  )

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return self._size

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     if weight not in (0, 1):
         raise ValueError('''Edge weight must be either 0 or 1.'''  )

     if to_vertex < 0 or to_vertex >= self.size:
         raise ValueError('''Vertex indexes must be in [0; size).'''  )

     self._graph[from_vertex].append(Edge(__lowerCamelCase					,						__lowerCamelCase  )  )







 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						str						 =     deque([start_vertex]  )
     __A    :						list[int | None]						 =     [None] * self.size
     __A    :						List[Any]						 =     0

     while queue:
         __A    :						Union[str, Any]						 =     queue.popleft()
         __A    :						Union[str, Any]						 =     distances[current_vertex]
         if current_distance is None:
             continue

         for edge in self[current_vertex]:
             __A    :						int						 =     current_distance + edge.weight
             __A    :						Any						 =     distances[edge.destination_vertex]
             if (
                 isinstance(__lowerCamelCase					,						__lowerCamelCase  )
                 and new_distance >= dest_vertex_distance
             ):
                 continue
             __A    :						List[str]						 =     new_distance
             if edge.weight == 0:
                 queue.appendleft(edge.destination_vertex  )
             else:
                 queue.append(edge.destination_vertex  )

     if distances[finish_vertex] is None:
         raise ValueError('''No path from start_vertex to finish_vertex.'''  )

     return distances[finish_vertex]


if __name__ == "__main__":
       import doctest

       doctest.testmod()











"""simple docstring"""



import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


a_ =			50000
a_ =			5000

a_ ,			a_ =			os.path.split(__file__)
a_ =			os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))







@get_duration
def 		__lowercase     (   snake_case_  :  datasets.Dataset				,snake_case_  :  Tuple						)   ->Tuple:



    '''simple docstring'''







    for i in range(snake_case_						):
        __A    :						int						 =     dataset[i]







@get_duration
def 		__lowercase     (   snake_case_  :  datasets.Dataset				,snake_case_  :  Optional[Any]				,snake_case_  :  int						)   ->Tuple:



    '''simple docstring'''







    for i in range(0				,len(snake_case_						)				,snake_case_						):
        __A    :						List[str]						 =     dataset[i : i + batch_size]







@get_duration
def 		__lowercase     (   snake_case_  :  datasets.Dataset				,snake_case_  :  List[Any]				,snake_case_  :  Any						)   ->int:



    '''simple docstring'''







    with dataset.formatted_as(type=snake_case_						):
        for i in range(snake_case_						):
            __A    :						Union[str, Any]						 =     dataset[i]







@get_duration
def 		__lowercase     (   snake_case_  :  datasets.Dataset				,snake_case_  :  Any				,snake_case_  :  Union[str, Any]				,snake_case_  :  Optional[int]						)   ->Union[str, Any]:



    '''simple docstring'''







    with dataset.formatted_as(type=snake_case_						):
        for i in range(0				,snake_case_				,snake_case_						):
            __A    :						Dict						 =     dataset[i : i + batch_size]







def 		__lowercase     (   )   ->Optional[int]:



    '''simple docstring'''







    __A    :						int						 =     {'''num examples''': SPEED_TEST_N_EXAMPLES}
    __A    :						Optional[int]						 =     [
        (read, {'''length''': SMALL_TEST}),
        (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 100}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1000}),
        (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
        (read_formatted, {'''type''': '''pandas''', '''length''': SMALL_TEST}),
        (read_formatted, {'''type''': '''torch''', '''length''': SMALL_TEST}),
        (read_formatted, {'''type''': '''tensorflow''', '''length''': SMALL_TEST}),
        (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}),
        (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1000}),
    ]

    __A    :						int						 =     [
        (read, {'''length''': SMALL_TEST}),
        (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 100}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1000}),
        (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
        (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}),
        (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1000}),
    ]
    with tempfile.TemporaryDirectory() as tmp_dir:
        print('''generating dataset'''						)
        __A    :						Any						 =     datasets.Features(
            {'''list''': datasets.Sequence(datasets.Value('''float32'''						)						), '''numbers''': datasets.Value('''float32'''						)}						)
        __A    :						List[Any]						 =     generate_example_dataset(
            os.path.join(snake_case_				,'''dataset.arrow'''						)				,snake_case_				,num_examples=snake_case_				,seq_shapes={'''list''': (100,)}				,)
        print('''first set of iterations'''						)
        for func, kwargs in functions:
            print(func.__name__				,str(snake_case_						)						)
            __A    :						Dict						 =     func(snake_case_				,**snake_case_						)

        print('''shuffling dataset'''						)
        __A    :						int						 =     dataset.shuffle()
        print('''Second set of iterations (after shuffling'''						)
        for func, kwargs in functions_shuffled:
            print('''shuffled '''				,func.__name__				,str(snake_case_						)						)
            __A    :						Optional[Any]						 =     func(
                snake_case_				,**snake_case_						)

    with open(snake_case_				,'''wb'''						) as f:
        f.write(json.dumps(snake_case_						).encode('''utf-8'''						)						)


if __name__ == "__main__":  # useful to run the profiler
       benchmark_iterating()




