





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class   a_ (			lowerCAmelCase__      ):
			A__      :			Any       	=  42
			A__      :			Any       	=  42

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






							"""simple docstring"""
							super().__init__()
							self.register_modules(unet=snake_case_     , scheduler=snake_case_						)



			@torch.no_grad()
			def __call__(   self   :  Union[str, Any]     , UpperCAmelCase__   :  Tuple = 1     , UpperCAmelCase__   :  Tuple = 2_000     , UpperCAmelCase__   :  Tuple = None     , UpperCAmelCase__   :  List[Any] = "pil"     , UpperCAmelCase__   :  Any = True     , **UpperCAmelCase__   :  Tuple     , ):






							"""simple docstring"""
							snake_case   :					str    						=				self.unet.config.sample_size
							snake_case   :					List[Any]    						=				(batch_size, 3, img_size, img_size)

							snake_case   :					str    						=				self.unet

							snake_case   :					Optional[int]    						=				randn_tensor(snake_case_     , generator=snake_case_						) * self.scheduler.init_noise_sigma
							snake_case   :					str    						=				sample.to(self.device						)

							self.scheduler.set_timesteps(snake_case_						)
							self.scheduler.set_sigmas(snake_case_						)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
											snake_case   :					Tuple    						=				self.scheduler.sigmas[i] * torch.ones(shape[0]     , device=self.device						)

											# correction step
											for _ in range(self.scheduler.config.correct_steps						):
															snake_case   :					Optional[int]    						=				self.unet(snake_case_     , snake_case_						).sample
															snake_case   :					Optional[int]    						=				self.scheduler.step_correct(snake_case_     , snake_case_     , generator=snake_case_						).prev_sample

											# prediction step
											snake_case   :					int    						=				model(snake_case_     , snake_case_						).sample
											snake_case   :					Optional[Any]    						=				self.scheduler.step_pred(snake_case_     , snake_case_     , snake_case_     , generator=snake_case_						)

											snake_case						,						snake_case   :					int    						=				output.prev_sample, output.prev_sample_mean

							snake_case   :					Any    						=				sample_mean.clamp(0     , 1						)
							snake_case   :					Dict    						=				sample.cpu().permute(0     , 2     , 3     , 1						).numpy()
							if output_type == "pil":
											snake_case   :					Dict    						=				self.numpy_to_pil(snake_case_						)

							if not return_dict:
											return (sample,)

							return ImagePipelineOutput(images=snake_case_						)




from collections import Counter
from timeit import timeit


def 		lowercase_	(  _UpperCamelCase = ""							,      ):


			'''simple docstring'''







			return sum(c % 2 for c in Counter(input_str.replace(''' '''							,      ''''''	).lower()	).values()	) < 2


def 		lowercase_	(  _UpperCamelCase = ""	):


			'''simple docstring'''







			if len(_UpperCamelCase	) == 0:
						return True
			__lowercase			       = input_str.replace(''' '''							,      ''''''	).lower()
			# character_freq_dict: Stores the frequency of every character in the input string
			__lowercase			       = {}

			for character in lower_case_input_str:
						__lowercase			       = character_freq_dict.get(_UpperCamelCase							,      0	) + 1
			__lowercase			       = 0

			for character_count in character_freq_dict.values():
						if character_count % 2:
									odd_char += 1
			if odd_char > 1:
						return False
			return True


def 		lowercase_	(  _UpperCamelCase = ""	):


			'''simple docstring'''







			print('''\nFor string = '''							,      _UpperCamelCase							,      ''':'''	)
			print(
			    '''> can_string_be_rearranged_as_palindrome_counter()'''							,      '''\tans ='''							,      can_string_be_rearranged_as_palindrome_counter(_UpperCamelCase	)							,      '''\ttime ='''							,      timeit(
			        '''z.can_string_be_rearranged_as_palindrome_counter(z.check_str)'''							,      setup='''import __main__ as z'''							,      )							,      '''seconds'''							,      )
			print(
			    '''> can_string_be_rearranged_as_palindrome()'''							,      '''\tans ='''							,      can_string_be_rearranged_as_palindrome(_UpperCamelCase	)							,      '''\ttime ='''							,      timeit(
			        '''z.can_string_be_rearranged_as_palindrome(z.check_str)'''							,      setup='''import __main__ as z'''							,      )							,      '''seconds'''							,      )


if __name__ == "__main__":
	a			:			int												=					input(
	    '''Enter string to determine if it can be rearranged as a palindrome or not: '''
	).strip()
	benchmark(check_str)
	a			:			Tuple												=					can_string_be_rearranged_as_palindrome_counter(check_str)
	print(f'''{check_str} can {'' if status else 'not '}be rearranged as a palindrome''')


