

"""simple docstring"""


from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__A		  =     200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
__A		  =     50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
__A		  =     0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))






def        a__       (       __SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE					)      ->      Optional[Any]:
		__lowerCAmelCase:			int        =     len([g for position, g in enumerate(SCREAMING_SNAKE_CASE_					) if g == main_target[position]]					)
		return (item, float(SCREAMING_SNAKE_CASE_					))






def        a__       (       __SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE					)      ->      Optional[int]:
		__lowerCAmelCase:			Optional[Any]        =     random.randint(0   ,					len(SCREAMING_SNAKE_CASE_					) - 1					)
		__lowerCAmelCase:			Union[str, Any]        =     parent_a[:random_slice] + parent_a[random_slice:]
		__lowerCAmelCase:			str        =     parent_a[:random_slice] + parent_a[random_slice:]
		return (child_a, child_a)






def        a__       (       __SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE					)      ->      Optional[int]:
		__lowerCAmelCase:			Optional[Any]        =     list(SCREAMING_SNAKE_CASE_					)
		if random.uniform(0   ,					1					) < MUTATION_PROBABILITY:
				__lowerCAmelCase:			List[Any]        =     random.choice(SCREAMING_SNAKE_CASE_					)
		return "".join(SCREAMING_SNAKE_CASE_					)






def        a__       (       __SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					)      ->      Dict:
		__lowerCAmelCase:			Dict        =     []
		# Generate more children proportionally to the fitness score.
		__lowerCAmelCase:			List[Any]        =     int(parent_a[1] * 1_0_0					) + 1
		__lowerCAmelCase:			List[str]        =     1_0 if child_n >= 1_0 else child_n
		for _ in range(SCREAMING_SNAKE_CASE_					):
				__lowerCAmelCase:			Any        =     population_score[random.randint(0   ,					SCREAMING_SNAKE_CASE_					)][0]

				__lowerCAmelCase ,					__lowerCAmelCase:			List[str]        =     crossover(parent_a[0]   ,					SCREAMING_SNAKE_CASE_					)
				# Append new string to the population list.
				pop.append(mutate(SCREAMING_SNAKE_CASE_   ,					SCREAMING_SNAKE_CASE_					)					)
				pop.append(mutate(SCREAMING_SNAKE_CASE_   ,					SCREAMING_SNAKE_CASE_					)					)
		return pop






def        a__       (       __SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE = True					)      ->      int:





		if N_POPULATION < N_SELECTED:
				__lowerCAmelCase:			Any        =     F"{N_POPULATION} must be bigger than {N_SELECTED}"
				raise ValueError(SCREAMING_SNAKE_CASE_					)
		# Verify that the target contains no genes besides the ones inside genes variable.
		__lowerCAmelCase:			Optional[int]        =     sorted({c for c in target if c not in genes}					)
		if not_in_genes_list:
				__lowerCAmelCase:			Optional[Any]        =     F"{not_in_genes_list} is not in genes list, evolution cannot converge"
				raise ValueError(SCREAMING_SNAKE_CASE_					)

		# Generate random starting population.
		__lowerCAmelCase:			Optional[Any]        =     []
		for _ in range(SCREAMING_SNAKE_CASE_					):
				population.append("".join([random.choice(SCREAMING_SNAKE_CASE_					) for i in range(len(SCREAMING_SNAKE_CASE_					)					)]					)					)

		# Just some logs to know what the algorithms is doing.
		__lowerCAmelCase ,					__lowerCAmelCase:			Any        =     0, 0

		# This loop will end when we find a perfect match for our target.
		while True:
				generation += 1
				total_population += len(SCREAMING_SNAKE_CASE_					)

				# Random population created. Now it's time to evaluate.

				# Adding a bit of concurrency can make everything faster,
				#
				# import concurrent.futures
				# population_score: list[tuple[str, float]] = []
				# with concurrent.futures.ThreadPoolExecutor(
				#                                   max_workers=NUM_WORKERS) as executor:
				#     futures = {executor.submit(evaluate, item) for item in population}
				#     concurrent.futures.wait(futures)
				#     population_score = [item.result() for item in futures]
				#
				# but with a simple algorithm like this, it will probably be slower.
				# We just need to call evaluate for every item inside the population.
				__lowerCAmelCase:			str        =     [evaluate(SCREAMING_SNAKE_CASE_   ,					SCREAMING_SNAKE_CASE_					) for item in population]

				# Check if there is a matching evolution.
				__lowerCAmelCase:			Any        =     sorted(SCREAMING_SNAKE_CASE_   ,					key=lambda __SCREAMING_SNAKE_CASE					: x[1]   ,					reverse=SCREAMING_SNAKE_CASE_					)
				if population_score[0][0] == target:
						return (generation, total_population, population_score[0][0])

				# Print the best result every 10 generation.
				# Just to know that the algorithm is working.
				if debug and generation % 1_0 == 0:
						print(
						    F"\nGeneration: {generation}"
						    F"\nTotal Population:{total_population}"
						    F"\nBest score: {population_score[0][1]}"
						    F"\nBest string: {population_score[0][0]}"					)

				# Flush the old population, keeping some of the best evolutions.
				# Keeping this avoid regression of evolution.
				__lowerCAmelCase:			str        =     population[: int(N_POPULATION / 3					)]
				population.clear()
				population.extend(SCREAMING_SNAKE_CASE_					)
				# Normalize population score to be between 0 and 1.
				__lowerCAmelCase:			Dict        =     [
				    (item, score / len(SCREAMING_SNAKE_CASE_					)) for item, score in population_score
				]

				# This is selection
				for i in range(SCREAMING_SNAKE_CASE_					):
						population.extend(select(population_score[int(SCREAMING_SNAKE_CASE_					)]   ,					SCREAMING_SNAKE_CASE_   ,					SCREAMING_SNAKE_CASE_					)					)
						# Check if the population has already reached the maximum value and if so,
						# break the cycle.  If this check is disabled, the algorithm will take
						# forever to compute large strings, but will also calculate small strings in
						# a far fewer generations.
						if len(SCREAMING_SNAKE_CASE_					) > N_POPULATION:
								break


if __name__ == "__main__":
			__A		  =     (
			    "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
			)
			__A		  =     list(
			    " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
			    "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
			)
			__A  ,							__A  ,							__A		  =     basic(target_str, genes_list)
			print(
			    F'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
			)









'''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  lowerCAmelCase_		(		__magic_name__    ,__magic_name__    ,unittest.TestCase		):
 __lowerCamelCase    :				Optional[Any]								=       AutoencoderKL
 __lowerCamelCase    :				List[Any]								=       "sample"
 __lowerCamelCase    :				Tuple								=       1e-2




 @property
 def 				_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 				_snake_case (						self		)     ->				Any:
     return (3, 32, 32)




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




 def 				_snake_case (						self		)     ->				str:
     _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 				_snake_case (						self		)     ->				Optional[int]:
     pass




 def 				_snake_case (						self		)     ->				Any:
     pass




 @unittest.skipIf(torch_device == "mps"      ,  "Gradient checkpointing skipped on MPS"		)
 def 				_snake_case (						self		)     ->				str:
     # 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 				_snake_case (						self		)     ->				Optional[Any]:
     _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 				_snake_case (						self		)     ->				Dict:
     _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(
             [
                 -4.0078E-01,
                 -3.8323E-04,
                 -1.2681E-01,
                 -1.1462E-01,
                 2.0095E-01,
                 1.0893E-01,
                 -8.8247E-02,
                 -3.0361E-01,
                 -9.8644E-03,
             ]		)
     elif torch_device == "cpu":
         _lowerCAmelCase						      =   torch.tensor(
             [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]		)
     else:
         _lowerCAmelCase						      =   torch.tensor(
             [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]		)

     self.assertTrue(torch_all_close(_lowerCAmelCase      ,  _lowerCAmelCase      ,  rtol=1E-2		)		)







@slow
class  lowerCAmelCase_		(		unittest.TestCase		):




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Union[str, Any]:
     return f'''gaussian_noise_s={seed}_shape={'_'.join([str(_lowerCAmelCase		) for s in shape]		)}.npy'''




 def 				_snake_case (						self		)     ->				List[str]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




 def 				_snake_case (						self      ,  _lowerCAmelCase=0      ,  _lowerCAmelCase=(4, 3, 512, 512)      ,  _lowerCAmelCase=False		)     ->				Tuple:
     _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 				_snake_case (						self      ,  _lowerCAmelCase="CompVis/stable-diffusion-v1-4"      ,  _lowerCAmelCase=False		)     ->				Tuple:
     _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 				_snake_case (						self      ,  _lowerCAmelCase=0		)     ->				str:
     if torch_device == "mps":
         return torch.manual_seed(_lowerCAmelCase		)
     return torch.Generator(device=_lowerCAmelCase		).manual_seed(_lowerCAmelCase		)




 @parameterized.expand(
     [
         # fmt: off
         [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
         [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
         # fmt: on
     ]		)
 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Dict:
     _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, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
         [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
         # fmt: on
     ]		)
 @require_torch_gpu
 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Optional[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, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
         [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
         # fmt: on
     ]		)
 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Tuple:
     _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, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
         [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
         # fmt: on
     ]		)
 @require_torch_gpu
 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Optional[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

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

     _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, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
         [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
         # fmt: on
     ]		)
 @require_torch_gpu
 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				str:
     _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, 512, 512]

     _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 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				List[str]:
     _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, 512, 512]

     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 				_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, 512, 512]

     assert torch_all_close(_lowerCAmelCase      ,  _lowerCAmelCase      ,  atol=1E-2		)





 @parameterized.expand(
     [
         # fmt: off
         [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
         [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
         # fmt: on
     ]		)
 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				int:
     _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		)


