class        UpperCamelCase						(				__a				):
						pass


class        UpperCamelCase						(				__a				):
						pass


class        UpperCamelCase						:





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





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase						)				->				None:
												try:
																		if len(self.queues[priority]						) >= 100:
																								raise OverflowError("""Maximum queue size is 100"""						)
																		self.queues[priority].append(__UpperCamelCase						)
												except IndexError:
																		raise ValueError("""Valid priorities are 0, 1, and 2"""						)





						def        A_							(self						)				->				int:
												for queue in self.queues:
																		if queue:
																								return queue.pop(0						)
												raise UnderFlowError("""All queues are empty"""						)





						def __str__(self						)				->				str:
												return "\n".join(f'''Priority {i}: {q}''' for i, q in enumerate(self.queues						)						)


class        UpperCamelCase						:





						def __init__(self						)				->				Optional[Any]:
												UpperCamelCase_		:							Optional[int]  =		[]





						def        A_							(self			,     __UpperCamelCase						)				->				None:
												if len(self.queue						) == 100:
																		raise OverFlowError("""Maximum queue size is 100"""						)
												self.queue.append(__UpperCamelCase						)





						def        A_							(self						)				->				int:
												if not self.queue:
																		raise UnderFlowError("""The queue is empty"""						)
												else:
																		UpperCamelCase_		:							Union[str, Any]  =		min(self.queue						)
																		self.queue.remove(__UpperCamelCase						)
																		return data





						def __str__(self						)				->				str:
												return str(self.queue						)






def     lowerCAmelCase_			(   ):
						UpperCamelCase_		:							str  =		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(_SCREAMING_SNAKE_CASE			)
						print(fpq.dequeue()			)
						print(fpq.dequeue()			)
						print(fpq.dequeue()			)
						print(fpq.dequeue()			)
						print(fpq.dequeue()			)
						print(_SCREAMING_SNAKE_CASE			)
						print(fpq.dequeue()			)
						print(fpq.dequeue()			)
						print(fpq.dequeue()			)
						print(fpq.dequeue()			)
						print(fpq.dequeue()			)






def     lowerCAmelCase_			(   ):
						UpperCamelCase_		:							Any  =		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(_SCREAMING_SNAKE_CASE			)
						print(epq.dequeue()			)
						print(epq.dequeue()			)
						print(epq.dequeue()			)
						print(epq.dequeue()			)
						print(epq.dequeue()			)
						print(_SCREAMING_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()







import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
	import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin

@require_torch
class        UpperCamelCase						(				unittest.TestCase	,	__a				):





						def        A_							(self						)				->				Tuple:
												UpperCamelCase_		:							Any  =		load_tool("""text-to-speech"""						)
												self.tool.setup()





						def        A_							(self						)				->				Dict:
												# SpeechT5 isn't deterministic
												torch.manual_seed(0						)
												UpperCamelCase_		:							Optional[Any]  =		self.tool("""hey"""						)
												UpperCamelCase_		:							Optional[Any]  =		result.to_raw()
												self.assertTrue(
												    torch.allclose(
												        resulting_tensor[:3]			,     torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]						)			,     )						)





						def        A_							(self						)				->				Dict:
												# SpeechT5 isn't deterministic
												torch.manual_seed(0						)
												UpperCamelCase_		:							Any  =		self.tool("""hey"""						)
												UpperCamelCase_		:							Tuple  =		result.to_raw()
												self.assertTrue(
												    torch.allclose(
												        resulting_tensor[:3]			,     torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]						)			,     )						)






