






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


A__      :     Optional[int]       =					5_00_00
A__      :     Tuple       =					50_00

A__  ,  A__      :     Dict       =					os.path.split(__file__)
A__      :     List[str]       =					os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))






@get_duration
def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				for i in range(lowerCamelCase_							):
								lowercase__  					=     dataset[i]






@get_duration
def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				for i in range(0						,  len(lowerCamelCase_							)						,  lowerCamelCase_							):
								lowercase__  					=     dataset[i : i + batch_size]






@get_duration
def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				with dataset.formatted_as(type=lowerCamelCase_							):
								for i in range(lowerCamelCase_							):
												lowercase__  					=     dataset[i]






@get_duration
def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				with dataset.formatted_as(type=lowerCamelCase_							):
								for i in range(0						,  lowerCamelCase_						,  lowerCamelCase_							):
												lowercase__  					=     dataset[i : i + batch_size]






def     a     (		):






				'''simple docstring'''
				lowercase__  					=     {'''num examples''': SPEED_TEST_N_EXAMPLES}
				lowercase__  					=     [
				    (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}),
				]

				lowercase__  					=     [
				    (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'''							)
								lowercase__  					=     datasets.Features(
								    {'''list''': datasets.Sequence(datasets.Value('''float32'''							)							), '''numbers''': datasets.Value('''float32'''							)}							)
								lowercase__  					=     generate_example_dataset(
								    os.path.join(lowerCamelCase_						,  '''dataset.arrow'''							)						,  lowerCamelCase_						,  num_examples=lowerCamelCase_						,  seq_shapes={'''list''': (100,)}						,  )
								print('''first set of iterations'''							)
								for func, kwargs in functions:
												print(func.__name__						,  str(lowerCamelCase_							)							)
												lowercase__  					=     func(lowerCamelCase_						,  **lowerCamelCase_							)

								print('''shuffling dataset'''							)
								lowercase__  					=     dataset.shuffle()
								print('''Second set of iterations (after shuffling'''							)
								for func, kwargs in functions_shuffled:
												print('''shuffled '''						,  func.__name__						,  str(lowerCamelCase_							)							)
												lowercase__  					=     func(
												    lowerCamelCase_						,  **lowerCamelCase_							)

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


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












import heapq as hq
import math
from collections.abc import Iterator
class  _UpperCAmelCase   :

				"""simple docstring"""




				def __init__(	self		:       str,    lowerCamelCase		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     str(id_    )
								lowercase__  					=     None
								lowercase__  					=     None
								lowercase__  					=     []
								lowercase__  					=     {}  # {vertex:distance}




				def __lt__(	self		:       Dict,    lowerCamelCase		:       Tuple    ):

								'''simple docstring'''
								return self.key < other.key




				def __repr__(	self		:       Dict    ):

								'''simple docstring'''
								return self.id




				def 							lowercase__  (	self		:       str,    lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								self.neighbors.append(lowerCamelCase    )




				def 							lowercase__  (	self		:       Any,    lowerCamelCase		:       Dict,    lowerCamelCase		:       Optional[Any]    ):

								'''simple docstring'''
								lowercase__  					=     weight






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				# add the neighbors:
				graph[a - 1].add_neighbor(graph[b - 1]							)
				graph[b - 1].add_neighbor(graph[a - 1]							)
				# add the edges:
				graph[a - 1].add_edge(graph[b - 1]						,  lowerCamelCase_							)
				graph[b - 1].add_edge(graph[a - 1]						,  lowerCamelCase_							)






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     []
				for u in graph:
								lowercase__  					=     math.inf
								lowercase__  					=     None
				lowercase__  					=     0
				lowercase__  					=     graph[:]
				while q:
								lowercase__  					=     min(lowerCamelCase_							)
								q.remove(lowerCamelCase_							)
								for v in u.neighbors:
												if (v in q) and (u.edges[v.id] < v.key):
																lowercase__  					=     u
																lowercase__  					=     u.edges[v.id]
				for i in range(1						,  len(lowerCamelCase_							)							):
								a.append((int(graph[i].id							) + 1, int(graph[i].pi.id							) + 1)							)
				return a






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				for u in graph:
								lowercase__  					=     math.inf
								lowercase__  					=     None
				lowercase__  					=     0

				lowercase__  					=     list(lowerCamelCase_							)
				hq.heapify(lowerCamelCase_							)

				while h:
								lowercase__  					=     hq.heappop(lowerCamelCase_							)
								for v in u.neighbors:
												if (v in h) and (u.edges[v.id] < v.key):
																lowercase__  					=     u
																lowercase__  					=     u.edges[v.id]
																hq.heapify(lowerCamelCase_							)

				for i in range(1						,  len(lowerCamelCase_							)							):
								yield (int(graph[i].id							) + 1, int(graph[i].pi.id							) + 1)






def     a     (		):






				'''simple docstring'''


if __name__ == "__main__":
							import doctest

							doctest.testmod()




