



"""simple docstring"""






class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	pass






class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	pass






class 			A__   :




	'''simple docstring'''





	def __init__(     self:		Tuple)				->       Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Union[str, Any]												=       [
				    [],
				    [],
				    [],
				]

	def 							_SCREAMING_SNAKE_CASE   (     self:		str       ,			_SCREAMING_SNAKE_CASE:		int       ,			_SCREAMING_SNAKE_CASE:		int)				->       None:

				"""simple docstring"""

				try:
							if len(self.queues[priority]) >= 100:
										raise OverflowError("Maximum queue size is 100")
							self.queues[priority].append(_SCREAMING_SNAKE_CASE)
				except IndexError:
							raise ValueError("Valid priorities are 0, 1, and 2")

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       int:

				"""simple docstring"""

				for queue in self.queues:
							if queue:
										return queue.pop(0)
				raise UnderFlowError("All queues are empty")







	def __str__(     self:		str)				->       str:

				"""simple docstring"""

				return "\n".join(F"""Priority {i}: {q}""" for i, q in enumerate(self.queues))






class 			A__   :




	'''simple docstring'''





	def __init__(     self:		List[str])				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       []

	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict       ,			_SCREAMING_SNAKE_CASE:		int)				->       None:

				"""simple docstring"""

				if len(self.queue) == 100:
							raise OverFlowError("Maximum queue size is 100")
				self.queue.append(_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       int:

				"""simple docstring"""

				if not self.queue:
							raise UnderFlowError("The queue is empty")
				else:
							__lowerCAmelCase      :  Union[str, Any]												=       min(self.queue)
							self.queue.remove(_SCREAMING_SNAKE_CASE)
							return data







	def __str__(     self:		Any)				->       str:

				"""simple docstring"""

				return str(self.queue)






def 			_lowercase				(  )						->   Optional[Any]:
			__lowerCAmelCase      :  Any												=       FixedPriorityQueue()
			fpq.enqueue(0					,10	)
			fpq.enqueue(1					,70	)
			fpq.enqueue(0					,100	)
			fpq.enqueue(2					,1	)
			fpq.enqueue(2					,5	)
			fpq.enqueue(1					,7	)
			fpq.enqueue(2					,4	)
			fpq.enqueue(1					,64	)
			fpq.enqueue(0					,128	)
			print(__snake_case	)
			print(fpq.dequeue()	)
			print(fpq.dequeue()	)
			print(fpq.dequeue()	)
			print(fpq.dequeue()	)
			print(fpq.dequeue()	)
			print(__snake_case	)
			print(fpq.dequeue()	)
			print(fpq.dequeue()	)
			print(fpq.dequeue()	)
			print(fpq.dequeue()	)
			print(fpq.dequeue()	)


def 			_lowercase				(  )						->   Dict:
			__lowerCAmelCase      :  str												=       ElementPriorityQueue()
			epq.enqueue(10	)
			epq.enqueue(70	)
			epq.enqueue(100	)
			epq.enqueue(1	)
			epq.enqueue(5	)
			epq.enqueue(7	)
			epq.enqueue(4	)
			epq.enqueue(64	)
			epq.enqueue(128	)
			print(__snake_case	)
			print(epq.dequeue()	)
			print(epq.dequeue()	)
			print(epq.dequeue()	)
			print(epq.dequeue()	)
			print(epq.dequeue()	)
			print(__snake_case	)
			print(epq.dequeue()	)
			print(epq.dequeue()	)
			print(epq.dequeue()	)
			print(epq.dequeue()	)
			print(epq.dequeue()	)


if __name__ == "__main__":
	fixed_priority_queue()
	element_priority_queue()




"""simple docstring"""






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__snake_case							:   Optional[int]      =    50_000
__snake_case							:   Dict      =    5_000

__snake_case      ,		__snake_case							:   Union[str, Any]      =    os.path.split(__file__)
__snake_case							:   Any      =    os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case	)						->   Dict:
			for i in range(__snake_case	):
						__lowerCAmelCase      :  Union[str, Any]												=       dataset[i]


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case	)						->   Dict:
			for i in range(0					,len(__snake_case	)					,__snake_case	):
						__lowerCAmelCase      :  List[str]												=       dataset[i : i + batch_size]


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case	)						->   Dict:
			with dataset.formatted_as(type=__snake_case	):
						for i in range(__snake_case	):
									__lowerCAmelCase      :  Union[str, Any]												=       dataset[i]


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case					,__snake_case	)						->   str:
			with dataset.formatted_as(type=__snake_case	):
						for i in range(0					,__snake_case					,__snake_case	):
									__lowerCAmelCase      :  Optional[int]												=       dataset[i : i + batch_size]


def 			_lowercase				(  )						->   Union[str, Any]:
			__lowerCAmelCase      :  Optional[int]												=       {"num examples": SPEED_TEST_N_EXAMPLES}
			__lowerCAmelCase      :  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": 1_000}),
			    (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": 1_000}),
			]

			__lowerCAmelCase      :  Any												=       [
			    (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": 1_000}),
			    (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": 1_000}),
			]
			with tempfile.TemporaryDirectory() as tmp_dir:
						print("generating dataset"	)
						__lowerCAmelCase      :  int												=       datasets.Features(
						    {"list": datasets.Sequence(datasets.Value("float32"	)	), "numbers": datasets.Value("float32"	)}	)
						__lowerCAmelCase      :  str												=       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	)	)
									__lowerCAmelCase      :  str												=       func(__snake_case					,**__snake_case	)

						print("shuffling dataset"	)
						__lowerCAmelCase      :  Optional[int]												=       dataset.shuffle()
						print("Second set of iterations (after shuffling"	)
						for func, kwargs in functions_shuffled:
									print("shuffled "					,func.__name__					,str(__snake_case	)	)
									__lowerCAmelCase      :  List[str]												=       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()