

from __future__ import annotations

def 		UpperCamelCase   (lowercase_:					list[int]       , lowercase_:					int       , lowercase_:					int       , lowercase_:					int   )	-> None:
	if (direction == 1 and array[indexa] > array[indexa]) or (
	    direction == 0 and array[indexa] < array[indexa]
	):
		A__  ,  A__				:       Tuple	  =						array[indexa], array[indexa]

def 		UpperCamelCase   (lowercase_:					list[int]       , lowercase_:					int       , lowercase_:					int       , lowercase_:					int   )	-> None:
	if length > 1:
		A__				:       Tuple	  =						int(length / 2   )
		for i in range(lowercase_       , low + middle   ):
			comp_and_swap(lowercase_       , lowercase_       , i + middle       , lowercase_   )
		bitonic_merge(lowercase_       , lowercase_       , lowercase_       , lowercase_   )
		bitonic_merge(lowercase_       , low + middle       , lowercase_       , lowercase_   )

def 		UpperCamelCase   (lowercase_:					list[int]       , lowercase_:					int       , lowercase_:					int       , lowercase_:					int   )	-> None:
	if length > 1:
		A__				:       Tuple	  =						int(length / 2   )
		bitonic_sort(lowercase_       , lowercase_       , lowercase_       , 1   )
		bitonic_sort(lowercase_       , low + middle       , lowercase_       , 0   )
		bitonic_merge(lowercase_       , lowercase_       , lowercase_       , lowercase_   )


if __name__ == "__main__":
	A_			:					Optional[int]    			=     input('Enter numbers separated by a comma:\n').strip()
	A_			:					str    			=     [int(item.strip()) for item in user_input.split(',')]

	bitonic_sort(unsorted, 0, len(unsorted), 1)
	print('\nSorted array in ascending order is: ', end='')
	print(*unsorted, sep=', ')

	bitonic_merge(unsorted, 0, len(unsorted), 0)
	print('Sorted array in descending order is: ', end='')
	print(*unsorted, sep=', ')






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        _a		(unittest.TestCase						,  __magic_name__							):


					'''simple docstring'''






					def     __A     (					self     ):
						A__				:       List[str]	  =						load_tool("""text-to-speech"""     )
						self.tool.setup()






					def     __A     (					self     ):
						# SpeechT5 isn't deterministic
						torch.manual_seed(0     )
						A__				:       Tuple	  =						self.tool("""hey"""     )
						A__				:       Tuple	  =						result.to_raw()
						self.assertTrue(
						    torch.allclose(
						        resulting_tensor[:3]       ,			torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]     )       ,			)     )






					def     __A     (					self     ):
						# SpeechT5 isn't deterministic
						torch.manual_seed(0     )
						A__				:       Any	  =						self.tool("""hey"""     )
						A__				:       Optional[Any]	  =						result.to_raw()
						self.assertTrue(
						    torch.allclose(
						        resulting_tensor[:3]       ,			torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]     )       ,			)     )



