


'''simple docstring'''






import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()




class 	a__       (							a__       ,       a__       ,       unittest.TestCase						):





       '''simple docstring'''






       lowercase__    :     int					=   AutoencoderKL
       lowercase__    :     List[Any]					=   "sample"
       lowercase__    :     str					=   1e-2







       @property
       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
              lowerCAmelCase__      =			4
              lowerCAmelCase__      =			3
              lowerCAmelCase__      =			(32, 32)

              lowerCAmelCase__      =			floats_tensor((batch_size, num_channels) + sizes     ).to(lowerCamelCase_     )

              return {"sample": image}







       @property
       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[str]:
              return (3, 32, 32)







       @property
       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[Any]:
              return (3, 32, 32)







       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
              lowerCAmelCase__      =			{
                  '''block_out_channels''': [32, 64],
                  '''in_channels''': 3,
                  '''out_channels''': 3,
                  '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
                  '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
                  '''latent_channels''': 4,
              }
              lowerCAmelCase__      =			self.dummy_input
              return init_dict, inputs_dict







       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
              pass







       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[Any]:
              pass







       @unittest.skipIf(torch_device == '''mps'''      ,					'''Gradient checkpointing skipped on MPS'''     )
       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Any:
              # enable deterministic behavior for gradient checkpointing
              lowerCAmelCase__     ,      lowerCAmelCase__      =			self.prepare_init_args_and_inputs_for_common()
              lowerCAmelCase__      =			self.model_class(**lowerCamelCase_     )
              model.to(lowerCamelCase_     )

              assert not model.is_gradient_checkpointing and model.training

              lowerCAmelCase__      =			model(**lowerCamelCase_     ).sample
              # run the backwards pass on the model. For backwards pass, for simplicity purpose,
              # we won't calculate the loss and rather backprop on out.sum()
              model.zero_grad()

              lowerCAmelCase__      =			torch.randn_like(lowerCamelCase_     )
              lowerCAmelCase__      =			(out - labels).mean()
              loss.backward()

              # re-instantiate the model now enabling gradient checkpointing
              lowerCAmelCase__      =			self.model_class(**lowerCamelCase_     )
              # clone model
              model_a.load_state_dict(model.state_dict()     )
              model_a.to(lowerCamelCase_     )
              model_a.enable_gradient_checkpointing()

              assert model_a.is_gradient_checkpointing and model_a.training

              lowerCAmelCase__      =			model_a(**lowerCamelCase_     ).sample
              # run the backwards pass on the model. For backwards pass, for simplicity purpose,
              # we won't calculate the loss and rather backprop on out.sum()
              model_a.zero_grad()
              lowerCAmelCase__      =			(out_a - labels).mean()
              loss_a.backward()

              # compare the output and parameters gradients
              self.assertTrue((loss - loss_a).abs() < 1e-5     )
              lowerCAmelCase__      =			dict(model.named_parameters()     )
              lowerCAmelCase__      =			dict(model_a.named_parameters()     )
              for name, param in named_params.items():
                     self.assertTrue(torch_all_close(param.grad.data      ,					named_params_a[name].grad.data      ,					atol=5e-5     )     )







       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[int]:
              lowerCAmelCase__     ,      lowerCAmelCase__      =			AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''      ,					output_loading_info=lowerCamelCase_     )
              self.assertIsNotNone(lowerCamelCase_     )
              self.assertEqual(len(loading_info['''missing_keys''']     )      ,					0     )

              model.to(lowerCamelCase_     )
              lowerCAmelCase__      =			model(**self.dummy_input     )

              assert image is not None, "Make sure output is not None"







       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							int:
              lowerCAmelCase__      =			AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''     )
              lowerCAmelCase__      =			model.to(lowerCamelCase_     )
              model.eval()

              if torch_device == "mps":
                     lowerCAmelCase__      =			torch.manual_seed(0     )
              else:
                     lowerCAmelCase__      =			torch.Generator(device=lowerCamelCase_     ).manual_seed(0     )

              lowerCAmelCase__      =			torch.randn(
                  1      ,					model.config.in_channels      ,					model.config.sample_size      ,					model.config.sample_size      ,					generator=torch.manual_seed(0     )      ,					)
              lowerCAmelCase__      =			image.to(lowerCamelCase_     )
              with torch.no_grad():
                     lowerCAmelCase__      =			model(lowerCamelCase_      ,					sample_posterior=lowerCamelCase_      ,					generator=lowerCamelCase_     ).sample

              lowerCAmelCase__      =			output[0, -1, -3:, -3:].flatten().cpu()

              # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
              # the expected output slices are not the same for CPU and GPU.
              if torch_device == "mps":
                     lowerCAmelCase__      =			torch.tensor(
                         [
                             -4.00_78e-01,
                             -3.83_23e-04,
                             -1.26_81e-01,
                             -1.14_62e-01,
                             2.00_95e-01,
                             1.08_93e-01,
                             -8.82_47e-02,
                             -3.03_61e-01,
                             -9.86_44e-03,
                         ]     )
              elif torch_device == "cpu":
                     lowerCAmelCase__      =			torch.tensor(
                         [-0.1_352, 0.0_878, 0.0_419, -0.0_818, -0.1_069, 0.0_688, -0.1_458, -0.4_446, -0.0_026]     )
              else:
                     lowerCAmelCase__      =			torch.tensor(
                         [-0.2_421, 0.4_642, 0.2_507, -0.0_438, 0.0_682, 0.3_160, -0.2_018, -0.0_727, 0.2_485]     )

              self.assertTrue(torch_all_close(lowerCamelCase_      ,					lowerCamelCase_      ,					rtol=1e-2     )     )




@slow
class 	a__       (							unittest.TestCase						):





       '''simple docstring'''






       def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							int:
              return F"""gaussian_noise_s={seed}_shape={"_".join([str(lowerCamelCase_     ) for s in shape]     )}.npy"""







       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Dict:
              # clean up the VRAM after each test
              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()







       def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_=0      ,					lowerCamelCase_=(4, 3, 5_12, 5_12)      ,					lowerCamelCase_=False     ) ->							List[Any]:
              lowerCAmelCase__      =			torch.floataa if fpaa else torch.floataa
              lowerCAmelCase__      =			torch.from_numpy(load_hf_numpy(self.get_file_format(lowerCamelCase_      ,					lowerCamelCase_     )     )     ).to(lowerCamelCase_     ).to(lowerCamelCase_     )
              return image







       def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_="CompVis/stable-diffusion-v1-4"      ,					lowerCamelCase_=False     ) ->							str:
              lowerCAmelCase__      =			'''fp16''' if fpaa else None
              lowerCAmelCase__      =			torch.floataa if fpaa else torch.floataa

              lowerCAmelCase__      =			AutoencoderKL.from_pretrained(
                  lowerCamelCase_      ,					subfolder='''vae'''      ,					torch_dtype=lowerCamelCase_      ,					revision=lowerCamelCase_      ,					)
              model.to(lowerCamelCase_     ).eval()

              return model







       def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_=0     ) ->							List[Any]:
              if torch_device == "mps":
                     return torch.manual_seed(lowerCamelCase_     )
              return torch.Generator(device=lowerCamelCase_     ).manual_seed(lowerCamelCase_     )







       @parameterized.expand(
           [
               # fmt: off
               [33, [-0.1_603, 0.9_878, -0.0_495, -0.0_790, -0.2_709, 0.8_375, -0.2_060, -0.0_824], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
               [47, [-0.2_376, 0.1_168, 0.1_332, -0.4_840, -0.2_508, -0.0_791, -0.0_493, -0.4_089], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
               # fmt: on
           ]     )
       def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Tuple:
              lowerCAmelCase__      =			self.get_sd_vae_model()
              lowerCAmelCase__      =			self.get_sd_image(lowerCamelCase_     )
              lowerCAmelCase__      =			self.get_generator(lowerCamelCase_     )

              with torch.no_grad():
                     lowerCAmelCase__      =			model(lowerCamelCase_      ,					generator=lowerCamelCase_      ,					sample_posterior=lowerCamelCase_     ).sample

              assert sample.shape == image.shape

              lowerCAmelCase__      =			sample[-1, -2:, -2:, :2].flatten().float().cpu()
              lowerCAmelCase__      =			torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice     )

              assert torch_all_close(lowerCamelCase_      ,					lowerCamelCase_      ,					atol=3e-3     )







       @parameterized.expand(
           [
               # fmt: off
               [33, [-0.0_513, 0.0_289, 1.3_799, 0.2_166, -0.2_573, -0.0_871, 0.5_103, -0.0_999]],
               [47, [-0.4_128, -0.1_320, -0.3_704, 0.1_965, -0.4_116, -0.2_332, -0.3_340, 0.2_247]],
               # fmt: on
           ]     )
       @require_torch_gpu
       def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Union[str, Any]:
              lowerCAmelCase__      =			self.get_sd_vae_model(fpaa=lowerCamelCase_     )
              lowerCAmelCase__      =			self.get_sd_image(lowerCamelCase_      ,					fpaa=lowerCamelCase_     )
              lowerCAmelCase__      =			self.get_generator(lowerCamelCase_     )

              with torch.no_grad():
                     lowerCAmelCase__      =			model(lowerCamelCase_      ,					generator=lowerCamelCase_      ,					sample_posterior=lowerCamelCase_     ).sample

              assert sample.shape == image.shape

              lowerCAmelCase__      =			sample[-1, -2:, :2, -2:].flatten().float().cpu()
              lowerCAmelCase__      =			torch.tensor(lowerCamelCase_     )

              assert torch_all_close(lowerCamelCase_      ,					lowerCamelCase_      ,					atol=1e-2     )







       @parameterized.expand(
           [
               # fmt: off
               [33, [-0.1_609, 0.9_866, -0.0_487, -0.0_777, -0.2_716, 0.8_368, -0.2_055, -0.0_814], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
               [47, [-0.2_377, 0.1_147, 0.1_333, -0.4_841, -0.2_506, -0.0_805, -0.0_491, -0.4_085], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
               # fmt: on
           ]     )
       def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Dict:
              lowerCAmelCase__      =			self.get_sd_vae_model()
              lowerCAmelCase__      =			self.get_sd_image(lowerCamelCase_     )

              with torch.no_grad():
                     lowerCAmelCase__      =			model(lowerCamelCase_     ).sample

              assert sample.shape == image.shape

              lowerCAmelCase__      =			sample[-1, -2:, -2:, :2].flatten().float().cpu()
              lowerCAmelCase__      =			torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice     )

              assert torch_all_close(lowerCamelCase_      ,					lowerCamelCase_      ,					atol=3e-3     )







       @parameterized.expand(
           [
               # fmt: off
               [13, [-0.2_051, -0.1_803, -0.2_311, -0.2_114, -0.3_292, -0.3_574, -0.2_953, -0.3_323]],
               [37, [-0.2_632, -0.2_625, -0.2_199, -0.2_741, -0.4_539, -0.4_990, -0.3_720, -0.4_925]],
               # fmt: on
           ]     )
       @require_torch_gpu
       def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Optional[int]:
              lowerCAmelCase__      =			self.get_sd_vae_model()
              lowerCAmelCase__      =			self.get_sd_image(lowerCamelCase_      ,					shape=(3, 4, 64, 64)     )

              with torch.no_grad():
                     lowerCAmelCase__      =			model.decode(lowerCamelCase_     ).sample

              assert list(sample.shape     ) == [3, 3, 5_12, 5_12]

              lowerCAmelCase__      =			sample[-1, -2:, :2, -2:].flatten().cpu()
              lowerCAmelCase__      =			torch.tensor(lowerCamelCase_     )

              assert torch_all_close(lowerCamelCase_      ,					lowerCamelCase_      ,					atol=1e-3     )







       @parameterized.expand(
           [
               # fmt: off
               [27, [-0.0_369, 0.0_207, -0.0_776, -0.0_682, -0.1_747, -0.1_930, -0.1_465, -0.2_039]],
               [16, [-0.1_628, -0.2_134, -0.2_747, -0.2_642, -0.3_774, -0.4_404, -0.3_687, -0.4_277]],
               # fmt: on
           ]     )
       @require_torch_gpu
       def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Dict:
              lowerCAmelCase__      =			self.get_sd_vae_model(fpaa=lowerCamelCase_     )
              lowerCAmelCase__      =			self.get_sd_image(lowerCamelCase_      ,					shape=(3, 4, 64, 64)      ,					fpaa=lowerCamelCase_     )

              with torch.no_grad():
                     lowerCAmelCase__      =			model.decode(lowerCamelCase_     ).sample

              assert list(sample.shape     ) == [3, 3, 5_12, 5_12]

              lowerCAmelCase__      =			sample[-1, -2:, :2, -2:].flatten().float().cpu()
              lowerCAmelCase__      =			torch.tensor(lowerCamelCase_     )

              assert torch_all_close(lowerCamelCase_      ,					lowerCamelCase_      ,					atol=5e-3     )







       @parameterized.expand([(13,), (16,), (27,)]     )
       @require_torch_gpu
       @unittest.skipIf(not is_xformers_available()      ,					reason='''xformers is not required when using PyTorch 2.0.'''     )
       def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							int:
              lowerCAmelCase__      =			self.get_sd_vae_model(fpaa=lowerCamelCase_     )
              lowerCAmelCase__      =			self.get_sd_image(lowerCamelCase_      ,					shape=(3, 4, 64, 64)      ,					fpaa=lowerCamelCase_     )

              with torch.no_grad():
                     lowerCAmelCase__      =			model.decode(lowerCamelCase_     ).sample

              model.enable_xformers_memory_efficient_attention()
              with torch.no_grad():
                     lowerCAmelCase__      =			model.decode(lowerCamelCase_     ).sample

              assert list(sample.shape     ) == [3, 3, 5_12, 5_12]

              assert torch_all_close(lowerCamelCase_      ,					lowerCamelCase_      ,					atol=1e-1     )







       @parameterized.expand([(13,), (16,), (37,)]     )
       @require_torch_gpu
       @unittest.skipIf(not is_xformers_available()      ,					reason='''xformers is not required when using PyTorch 2.0.'''     )
       def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							Any:
              lowerCAmelCase__      =			self.get_sd_vae_model()
              lowerCAmelCase__      =			self.get_sd_image(lowerCamelCase_      ,					shape=(3, 4, 64, 64)     )

              with torch.no_grad():
                     lowerCAmelCase__      =			model.decode(lowerCamelCase_     ).sample

              model.enable_xformers_memory_efficient_attention()
              with torch.no_grad():
                     lowerCAmelCase__      =			model.decode(lowerCamelCase_     ).sample

              assert list(sample.shape     ) == [3, 3, 5_12, 5_12]

              assert torch_all_close(lowerCamelCase_      ,					lowerCamelCase_      ,					atol=1e-2     )







       @parameterized.expand(
           [
               # fmt: off
               [33, [-0.3_001, 0.0_918, -2.6_984, -3.9_720, -3.2_099, -5.0_353, 1.7_338, -0.2_065, 3.4_267]],
               [47, [-1.5_030, -4.3_871, -6.0_355, -9.1_157, -1.6_661, -2.7_853, 2.1_607, -5.0_823, 2.5_633]],
               # fmt: on
           ]     )
       def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							List[Any]:
              lowerCAmelCase__      =			self.get_sd_vae_model()
              lowerCAmelCase__      =			self.get_sd_image(lowerCamelCase_     )
              lowerCAmelCase__      =			self.get_generator(lowerCamelCase_     )

              with torch.no_grad():
                     lowerCAmelCase__      =			model.encode(lowerCamelCase_     ).latent_dist
                     lowerCAmelCase__      =			dist.sample(generator=lowerCamelCase_     )

              assert list(sample.shape     ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

              lowerCAmelCase__      =			sample[0, -1, -3:, -3:].flatten().cpu()
              lowerCAmelCase__      =			torch.tensor(lowerCamelCase_     )

              lowerCAmelCase__      =			3e-3 if torch_device != '''mps''' else 1e-2
              assert torch_all_close(lowerCamelCase_      ,					lowerCamelCase_      ,					atol=lowerCamelCase_     )



'''simple docstring'''






from collections import Counter
from timeit import timeit


def       _snake_case     (  A = ""				,					)		->					bool:
							return sum(c % 2 for c in Counter(input_str.replace(''' '''				,					'''''' ).lower() ).values() ) < 2


def       _snake_case     (  A = "" )		->					bool:
							if len(A ) == 0:
														return True
							lowerCAmelCase__      =			input_str.replace(''' '''				,					'''''' ).lower()
							# character_freq_dict: Stores the frequency of every character in the input string
							lowerCAmelCase__      =			{}

							for character in lower_case_input_str:
														lowerCAmelCase__      =			character_freq_dict.get(A				,					0 ) + 1
							lowerCAmelCase__      =			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       _snake_case     (  A = "" )		->					None:
							print('''\nFor string = '''				,					A				,					''':''' )
							print(
							    '''> can_string_be_rearranged_as_palindrome_counter()'''				,					'''\tans ='''				,					can_string_be_rearranged_as_palindrome_counter(A )				,					'''\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(A )				,					'''\ttime ='''				,					timeit(
							        '''z.can_string_be_rearranged_as_palindrome(z.check_str)'''				,					setup='''import __main__ as z'''				,					)				,					'''seconds'''				,					)


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