




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_       (     SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  ,    unittest.TestCase     ):
	A    							= AutoencoderKL
	A    							= '''sample'''
	A    							= 1E-2





	@property
	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						4
				SCREAMING_SNAKE_CASE_								=						3
				SCREAMING_SNAKE_CASE_								=						(32, 32)

				SCREAMING_SNAKE_CASE_								=						floats_tensor((batch_size, num_channels) + sizes      ).to(SCREAMING_SNAKE_CASE      )

				return {"sample": image}





	@property
	def 						A_(							self      )  ->					List[Any]:




				"""simple docstring"""



				return (3, 32, 32)





	@property
	def 						A_(							self      )  ->					List[str]:




				"""simple docstring"""



				return (3, 32, 32)





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						{
				    'block_out_channels': [32, 64],
				    'in_channels': 3,
				    'out_channels': 3,
				    'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
				    'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
				    'latent_channels': 4,
				}
				SCREAMING_SNAKE_CASE_								=						self.dummy_input
				return init_dict, inputs_dict





	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				pass





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				pass





	@unittest.skipIf(torch_device == 'mps'      ,     'Gradient checkpointing skipped on MPS'      )
	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						self.prepare_init_args_and_inputs_for_common()
				SCREAMING_SNAKE_CASE_								=						self.model_class(**SCREAMING_SNAKE_CASE      )
				model.to(SCREAMING_SNAKE_CASE      )

				assert not model.is_gradient_checkpointing and model.training

				SCREAMING_SNAKE_CASE_								=						model(**SCREAMING_SNAKE_CASE      ).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()

				SCREAMING_SNAKE_CASE_								=						torch.randn_like(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						(out - labels).mean()
				loss.backward()

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

				assert model_a.is_gradient_checkpointing and model_a.training

				SCREAMING_SNAKE_CASE_								=						model_a(**SCREAMING_SNAKE_CASE      ).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()
				SCREAMING_SNAKE_CASE_								=						(out_a - labels).mean()
				loss_a.backward()

				# compare the output and parameters gradients
				self.assertTrue((loss - loss_a).abs() < 1e-5      )
				SCREAMING_SNAKE_CASE_								=						dict(model.named_parameters()      )
				SCREAMING_SNAKE_CASE_								=						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 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'      ,     output_loading_info=SCREAMING_SNAKE_CASE      )
				self.assertIsNotNone(SCREAMING_SNAKE_CASE      )
				self.assertEqual(len(loading_info['missing_keys']      )      ,     0      )

				model.to(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						model(**self.dummy_input      )

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





	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'      )
				SCREAMING_SNAKE_CASE_								=						model.to(SCREAMING_SNAKE_CASE      )
				model.eval()

				if torch_device == "mps":
							SCREAMING_SNAKE_CASE_								=						torch.manual_seed(0      )
				else:
							SCREAMING_SNAKE_CASE_								=						torch.Generator(device=SCREAMING_SNAKE_CASE      ).manual_seed(0      )

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

				SCREAMING_SNAKE_CASE_								=						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":
							SCREAMING_SNAKE_CASE_								=						torch.tensor(
							    [
							        -4.0_078e-01,
							        -3.8_323e-04,
							        -1.2_681e-01,
							        -1.1_462e-01,
							        2.0_095e-01,
							        1.0_893e-01,
							        -8.8_247e-02,
							        -3.0_361e-01,
							        -9.8_644e-03,
							    ]      )
				elif torch_device == "cpu":
							SCREAMING_SNAKE_CASE_								=						torch.tensor(
							    [-0.1_3_5_2, 0.0_8_7_8, 0.0_4_1_9, -0.0_8_1_8, -0.1_0_6_9, 0.0_6_8_8, -0.1_4_5_8, -0.4_4_4_6, -0.0_0_2_6]      )
				else:
							SCREAMING_SNAKE_CASE_								=						torch.tensor(
							    [-0.2_4_2_1, 0.4_6_4_2, 0.2_5_0_7, -0.0_4_3_8, 0.0_6_8_2, 0.3_1_6_0, -0.2_0_1_8, -0.0_7_2_7, 0.2_4_8_5]      )

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




@slow
class       a_       (     unittest.TestCase     ):





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					Optional[Any]:




				"""simple docstring"""



				return f'gaussian_noise_s={seed}_shape={"_".join([str(SCREAMING_SNAKE_CASE      ) for s in shape]      )}.npy'





	def 						A_(							self      )  ->					List[Any]:




				"""simple docstring"""



				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE=0      ,     SCREAMING_SNAKE_CASE=(4, 3, 512, 512)      ,     SCREAMING_SNAKE_CASE=False      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						torch.floataa if fpaa else torch.floataa
				SCREAMING_SNAKE_CASE_								=						torch.from_numpy(load_hf_numpy(self.get_file_format(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )      )      ).to(SCREAMING_SNAKE_CASE      ).to(SCREAMING_SNAKE_CASE      )
				return image





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE="CompVis/stable-diffusion-v1-4"      ,     SCREAMING_SNAKE_CASE=False      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						'fp16' if fpaa else None
				SCREAMING_SNAKE_CASE_								=						torch.floataa if fpaa else torch.floataa

				SCREAMING_SNAKE_CASE_								=						AutoencoderKL.from_pretrained(
				    SCREAMING_SNAKE_CASE      ,     subfolder='vae'      ,     torch_dtype=SCREAMING_SNAKE_CASE      ,     revision=SCREAMING_SNAKE_CASE      ,     )
				model.to(SCREAMING_SNAKE_CASE      ).eval()

				return model





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE=0      )  ->					Any:




				"""simple docstring"""



				if torch_device == "mps":
							return torch.manual_seed(SCREAMING_SNAKE_CASE      )
				return torch.Generator(device=SCREAMING_SNAKE_CASE      ).manual_seed(SCREAMING_SNAKE_CASE      )





	@parameterized.expand(
	    [
	        # fmt: off
	        [33, [-0.1_6_0_3, 0.9_8_7_8, -0.0_4_9_5, -0.0_7_9_0, -0.2_7_0_9, 0.8_3_7_5, -0.2_0_6_0, -0.0_8_2_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
	        [47, [-0.2_3_7_6, 0.1_1_6_8, 0.1_3_3_2, -0.4_8_4_0, -0.2_5_0_8, -0.0_7_9_1, -0.0_4_9_3, -0.4_0_8_9], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
	        # fmt: on
	    ]      )
	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					List[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.get_sd_vae_model()
				SCREAMING_SNAKE_CASE_								=						self.get_sd_image(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						self.get_generator(SCREAMING_SNAKE_CASE      )

				with torch.no_grad():
							SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     generator=SCREAMING_SNAKE_CASE      ,     sample_posterior=SCREAMING_SNAKE_CASE      ).sample

				assert sample.shape == image.shape

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

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





	@parameterized.expand(
	    [
	        # fmt: off
	        [33, [-0.0_5_1_3, 0.0_2_8_9, 1.3_7_9_9, 0.2_1_6_6, -0.2_5_7_3, -0.0_8_7_1, 0.5_1_0_3, -0.0_9_9_9]],
	        [47, [-0.4_1_2_8, -0.1_3_2_0, -0.3_7_0_4, 0.1_9_6_5, -0.4_1_1_6, -0.2_3_3_2, -0.3_3_4_0, 0.2_2_4_7]],
	        # fmt: on
	    ]      )
	@require_torch_gpu
	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					Union[str, Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.get_sd_vae_model(fpaa=SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						self.get_sd_image(SCREAMING_SNAKE_CASE      ,     fpaa=SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						self.get_generator(SCREAMING_SNAKE_CASE      )

				with torch.no_grad():
							SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     generator=SCREAMING_SNAKE_CASE      ,     sample_posterior=SCREAMING_SNAKE_CASE      ).sample

				assert sample.shape == image.shape

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

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





	@parameterized.expand(
	    [
	        # fmt: off
	        [33, [-0.1_6_0_9, 0.9_8_6_6, -0.0_4_8_7, -0.0_7_7_7, -0.2_7_1_6, 0.8_3_6_8, -0.2_0_5_5, -0.0_8_1_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
	        [47, [-0.2_3_7_7, 0.1_1_4_7, 0.1_3_3_3, -0.4_8_4_1, -0.2_5_0_6, -0.0_8_0_5, -0.0_4_9_1, -0.4_0_8_5], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
	        # fmt: on
	    ]      )
	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.get_sd_vae_model()
				SCREAMING_SNAKE_CASE_								=						self.get_sd_image(SCREAMING_SNAKE_CASE      )

				with torch.no_grad():
							SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ).sample

				assert sample.shape == image.shape

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

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





	@parameterized.expand(
	    [
	        # fmt: off
	        [13, [-0.2_0_5_1, -0.1_8_0_3, -0.2_3_1_1, -0.2_1_1_4, -0.3_2_9_2, -0.3_5_7_4, -0.2_9_5_3, -0.3_3_2_3]],
	        [37, [-0.2_6_3_2, -0.2_6_2_5, -0.2_1_9_9, -0.2_7_4_1, -0.4_5_3_9, -0.4_9_9_0, -0.3_7_2_0, -0.4_9_2_5]],
	        # fmt: on
	    ]      )
	@require_torch_gpu
	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					Optional[int]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.get_sd_vae_model()
				SCREAMING_SNAKE_CASE_								=						self.get_sd_image(SCREAMING_SNAKE_CASE      ,     shape=(3, 4, 64, 64)      )

				with torch.no_grad():
							SCREAMING_SNAKE_CASE_								=						model.decode(SCREAMING_SNAKE_CASE      ).sample

				assert list(sample.shape      ) == [3, 3, 512, 512]

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

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





	@parameterized.expand(
	    [
	        # fmt: off
	        [27, [-0.0_3_6_9, 0.0_2_0_7, -0.0_7_7_6, -0.0_6_8_2, -0.1_7_4_7, -0.1_9_3_0, -0.1_4_6_5, -0.2_0_3_9]],
	        [16, [-0.1_6_2_8, -0.2_1_3_4, -0.2_7_4_7, -0.2_6_4_2, -0.3_7_7_4, -0.4_4_0_4, -0.3_6_8_7, -0.4_2_7_7]],
	        # fmt: on
	    ]      )
	@require_torch_gpu
	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.get_sd_vae_model(fpaa=SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						self.get_sd_image(SCREAMING_SNAKE_CASE      ,     shape=(3, 4, 64, 64)      ,     fpaa=SCREAMING_SNAKE_CASE      )

				with torch.no_grad():
							SCREAMING_SNAKE_CASE_								=						model.decode(SCREAMING_SNAKE_CASE      ).sample

				assert list(sample.shape      ) == [3, 3, 512, 512]

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

				assert torch_all_close(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     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 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Any:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.get_sd_vae_model(fpaa=SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						self.get_sd_image(SCREAMING_SNAKE_CASE      ,     shape=(3, 4, 64, 64)      ,     fpaa=SCREAMING_SNAKE_CASE      )

				with torch.no_grad():
							SCREAMING_SNAKE_CASE_								=						model.decode(SCREAMING_SNAKE_CASE      ).sample

				model.enable_xformers_memory_efficient_attention()
				with torch.no_grad():
							SCREAMING_SNAKE_CASE_								=						model.decode(SCREAMING_SNAKE_CASE      ).sample

				assert list(sample.shape      ) == [3, 3, 512, 512]

				assert torch_all_close(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     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 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Optional[int]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.get_sd_vae_model()
				SCREAMING_SNAKE_CASE_								=						self.get_sd_image(SCREAMING_SNAKE_CASE      ,     shape=(3, 4, 64, 64)      )

				with torch.no_grad():
							SCREAMING_SNAKE_CASE_								=						model.decode(SCREAMING_SNAKE_CASE      ).sample

				model.enable_xformers_memory_efficient_attention()
				with torch.no_grad():
							SCREAMING_SNAKE_CASE_								=						model.decode(SCREAMING_SNAKE_CASE      ).sample

				assert list(sample.shape      ) == [3, 3, 512, 512]

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





	@parameterized.expand(
	    [
	        # fmt: off
	        [33, [-0.3_0_0_1, 0.0_9_1_8, -2.6_9_8_4, -3.9_7_2_0, -3.2_0_9_9, -5.0_3_5_3, 1.7_3_3_8, -0.2_0_6_5, 3.4_2_6_7]],
	        [47, [-1.5_0_3_0, -4.3_8_7_1, -6.0_3_5_5, -9.1_1_5_7, -1.6_6_6_1, -2.7_8_5_3, 2.1_6_0_7, -5.0_8_2_3, 2.5_6_3_3]],
	        # fmt: on
	    ]      )
	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					Optional[int]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.get_sd_vae_model()
				SCREAMING_SNAKE_CASE_								=						self.get_sd_image(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						self.get_generator(SCREAMING_SNAKE_CASE      )

				with torch.no_grad():
							SCREAMING_SNAKE_CASE_								=						model.encode(SCREAMING_SNAKE_CASE      ).latent_dist
							SCREAMING_SNAKE_CASE_								=						dist.sample(generator=SCREAMING_SNAKE_CASE      )

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

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

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






from __future__ import annotations

from typing import Any

def lowercase    (  SCREAMING_SNAKE_CASE )			->							None:


			'''simple docstring'''







			create_state_space_tree(SCREAMING_SNAKE_CASE       ,    []       ,    0 )

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							None:


			'''simple docstring'''







			if index == len(SCREAMING_SNAKE_CASE ):
						print(SCREAMING_SNAKE_CASE )
						return

			create_state_space_tree(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    index + 1 )
			current_subsequence.append(sequence[index] )
			create_state_space_tree(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    index + 1 )
			current_subsequence.pop()


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     list[Any]		    =       [3, 1, 2, 4]
				generate_all_subsequences(seq)

				seq.clear()
				seq.extend(["A", "B", "C"])
				generate_all_subsequences(seq)
