




"""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 							lowercase_			(	SCREAMING_SNAKE_CASE__     ,      SCREAMING_SNAKE_CASE__     ,      unittest.TestCase				):



 '''simple docstring'''
 UpperCAmelCase       :	str     =      AutoencoderKL
 UpperCAmelCase       :	int     =      '''sample'''
 UpperCAmelCase       :	Dict     =      1E-2

 @property
 def      lowerCAmelCase_				(					self			:			List[Any]						):
       _A     =  4
       _A     =  3
       _A     =  (32, 32)

       _A     =  floats_tensor((batch_size, num_channels) + sizes						).to(_UpperCAmelCase						)

       return {"sample": image}

 @property
 def      lowerCAmelCase_				(					self			:			str						):
       return (3, 32, 32)

 @property
 def      lowerCAmelCase_				(					self			:			Tuple						):
       return (3, 32, 32)

 def      lowerCAmelCase_				(					self			:			int						):
       _A     =  {
           'block_out_channels': [32, 64],
           'in_channels': 3,
           'out_channels': 3,
           'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
           'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
           'latent_channels': 4,
       }
       _A     =  self.dummy_input
       return init_dict, inputs_dict

 def      lowerCAmelCase_				(					self			:			Tuple						):
       pass

 def      lowerCAmelCase_				(					self			:			List[str]						):
       pass

 @unittest.skipIf(torch_device == 'mps'       ,				'Gradient checkpointing skipped on MPS'						)
 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       _A  ,       _A     =  self.prepare_init_args_and_inputs_for_common()
       _A     =  self.model_class(**_UpperCAmelCase						)
       model.to(_UpperCAmelCase						)

       assert not model.is_gradient_checkpointing and model.training

       _A     =  model(**_UpperCAmelCase						).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()

       _A     =  torch.randn_like(_UpperCAmelCase						)
       _A     =  (out - labels).mean()
       loss.backward()

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

       assert model_a.is_gradient_checkpointing and model_a.training

       _A     =  model_a(**_UpperCAmelCase						).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()
       _A     =  (out_a - labels).mean()
       loss_a.backward()

       # compare the output and parameters gradients
       self.assertTrue((loss - loss_a).abs() < 1E-5						)
       _A     =  dict(model.named_parameters()						)
       _A     =  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      lowerCAmelCase_				(					self			:			List[Any]						):
       _A  ,       _A     =  AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'       ,				output_loading_info=_UpperCAmelCase						)
       self.assertIsNotNone(_UpperCAmelCase						)
       self.assertEqual(len(loading_info['missing_keys']						)       ,				0						)

       model.to(_UpperCAmelCase						)
       _A     =  model(**self.dummy_input						)

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



 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       _A     =  AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'						)
       _A     =  model.to(_UpperCAmelCase						)
       model.eval()

       if torch_device == "mps":
             _A     =  torch.manual_seed(0						)
       else:
             _A     =  torch.Generator(device=_UpperCAmelCase						).manual_seed(0						)

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

       _A     =  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":
             _A     =  torch.tensor(
                 [
                     -4.0_0_7_8E-0_1,
                     -3.8_3_2_3E-0_4,
                     -1.2_6_8_1E-0_1,
                     -1.1_4_6_2E-0_1,
                     2.0_0_9_5E-0_1,
                     1.0_8_9_3E-0_1,
                     -8.8_2_4_7E-0_2,
                     -3.0_3_6_1E-0_1,
                     -9.8_6_4_4E-0_3,
                 ]						)
       elif torch_device == "cpu":
             _A     =  torch.tensor(
                 [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]						)
       else:
             _A     =  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(_UpperCAmelCase       ,				_UpperCAmelCase       ,				rtol=1E-2						)						)





@slow
class 							lowercase_			(	unittest.TestCase				):



 '''simple docstring'''

 def      lowerCAmelCase_				(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			int       ,				_UpperCAmelCase			:			Optional[int]						):
       return F'''gaussian_noise_s={seed}_shape={"_".join([str(_UpperCAmelCase						) for s in shape]						)}.npy'''

 def      lowerCAmelCase_				(					self			:			Any						):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

 def      lowerCAmelCase_				(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			Any=0       ,				_UpperCAmelCase			:			Tuple=(4, 3, 512, 512)       ,				_UpperCAmelCase			:			List[str]=False						):
       _A     =  torch.floataa if fpaa else torch.floataa
       _A     =  torch.from_numpy(load_hf_numpy(self.get_file_format(_UpperCAmelCase       ,				_UpperCAmelCase						)						)						).to(_UpperCAmelCase						).to(_UpperCAmelCase						)
       return image

 def      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			List[Any]="CompVis/stable-diffusion-v1-4"       ,				_UpperCAmelCase			:			Dict=False						):
       _A     =  'fp16' if fpaa else None
       _A     =  torch.floataa if fpaa else torch.floataa

       _A     =  AutoencoderKL.from_pretrained(
           _UpperCAmelCase       ,				subfolder='vae'       ,				torch_dtype=_UpperCAmelCase       ,				revision=_UpperCAmelCase       ,				)
       model.to(_UpperCAmelCase						).eval()

       return model

 def      lowerCAmelCase_				(					self			:			Any       ,				_UpperCAmelCase			:			Tuple=0						):



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

 @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      lowerCAmelCase_				(					self			:			Optional[int]       ,				_UpperCAmelCase			:			str       ,				_UpperCAmelCase			:			Optional[Any]       ,				_UpperCAmelCase			:			Any						):
       _A     =  self.get_sd_vae_model()
       _A     =  self.get_sd_image(_UpperCAmelCase						)
       _A     =  self.get_generator(_UpperCAmelCase						)

       with torch.no_grad():
             _A     =  model(_UpperCAmelCase       ,				generator=_UpperCAmelCase       ,				sample_posterior=_UpperCAmelCase						).sample

       assert sample.shape == image.shape

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

       assert torch_all_close(_UpperCAmelCase       ,				_UpperCAmelCase       ,				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      lowerCAmelCase_				(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			str       ,				_UpperCAmelCase			:			List[str]						):
       _A     =  self.get_sd_vae_model(fpaa=_UpperCAmelCase						)
       _A     =  self.get_sd_image(_UpperCAmelCase       ,				fpaa=_UpperCAmelCase						)
       _A     =  self.get_generator(_UpperCAmelCase						)

       with torch.no_grad():
             _A     =  model(_UpperCAmelCase       ,				generator=_UpperCAmelCase       ,				sample_posterior=_UpperCAmelCase						).sample

       assert sample.shape == image.shape

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

       assert torch_all_close(_UpperCAmelCase       ,				_UpperCAmelCase       ,				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      lowerCAmelCase_				(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			int       ,				_UpperCAmelCase			:			str       ,				_UpperCAmelCase			:			Union[str, Any]						):
       _A     =  self.get_sd_vae_model()
       _A     =  self.get_sd_image(_UpperCAmelCase						)

       with torch.no_grad():
             _A     =  model(_UpperCAmelCase						).sample

       assert sample.shape == image.shape

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

       assert torch_all_close(_UpperCAmelCase       ,				_UpperCAmelCase       ,				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      lowerCAmelCase_				(					self			:			Dict       ,				_UpperCAmelCase			:			Optional[int]       ,				_UpperCAmelCase			:			Optional[int]						):
       _A     =  self.get_sd_vae_model()
       _A     =  self.get_sd_image(_UpperCAmelCase       ,				shape=(3, 4, 64, 64)						)

       with torch.no_grad():
             _A     =  model.decode(_UpperCAmelCase						).sample

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

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

       assert torch_all_close(_UpperCAmelCase       ,				_UpperCAmelCase       ,				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      lowerCAmelCase_				(					self			:			List[str]       ,				_UpperCAmelCase			:			str       ,				_UpperCAmelCase			:			Dict						):
       _A     =  self.get_sd_vae_model(fpaa=_UpperCAmelCase						)
       _A     =  self.get_sd_image(_UpperCAmelCase       ,				shape=(3, 4, 64, 64)       ,				fpaa=_UpperCAmelCase						)

       with torch.no_grad():
             _A     =  model.decode(_UpperCAmelCase						).sample

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

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

       assert torch_all_close(_UpperCAmelCase       ,				_UpperCAmelCase       ,				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      lowerCAmelCase_				(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			Optional[int]						):
       _A     =  self.get_sd_vae_model(fpaa=_UpperCAmelCase						)
       _A     =  self.get_sd_image(_UpperCAmelCase       ,				shape=(3, 4, 64, 64)       ,				fpaa=_UpperCAmelCase						)

       with torch.no_grad():
             _A     =  model.decode(_UpperCAmelCase						).sample

       model.enable_xformers_memory_efficient_attention()
       with torch.no_grad():
             _A     =  model.decode(_UpperCAmelCase						).sample

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

       assert torch_all_close(_UpperCAmelCase       ,				_UpperCAmelCase       ,				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      lowerCAmelCase_				(					self			:			Optional[int]       ,				_UpperCAmelCase			:			int						):
       _A     =  self.get_sd_vae_model()
       _A     =  self.get_sd_image(_UpperCAmelCase       ,				shape=(3, 4, 64, 64)						)

       with torch.no_grad():
             _A     =  model.decode(_UpperCAmelCase						).sample

       model.enable_xformers_memory_efficient_attention()
       with torch.no_grad():
             _A     =  model.decode(_UpperCAmelCase						).sample

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

       assert torch_all_close(_UpperCAmelCase       ,				_UpperCAmelCase       ,				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      lowerCAmelCase_				(					self			:			Tuple       ,				_UpperCAmelCase			:			List[Any]       ,				_UpperCAmelCase			:			List[Any]						):
       _A     =  self.get_sd_vae_model()
       _A     =  self.get_sd_image(_UpperCAmelCase						)
       _A     =  self.get_generator(_UpperCAmelCase						)

       with torch.no_grad():
             _A     =  model.encode(_UpperCAmelCase						).latent_dist
             _A     =  dist.sample(generator=_UpperCAmelCase						)

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

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

       _A     =  3E-3 if torch_device != 'mps' else 1E-2
       assert torch_all_close(_UpperCAmelCase       ,				_UpperCAmelCase       ,				atol=_UpperCAmelCase						)







from __future__ import annotations

from collections.abc import MutableSequence



class       a_       :





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					None:




				"""simple docstring"""



				if len(SCREAMING_SNAKE_CASE      ) != degree + 1:
							raise ValueError(
							    'The number of coefficients should be equal to the degree + 1.'      )

				SCREAMING_SNAKE_CASE_								=						list(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						degree





	def __add__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Polynomial:




				"""simple docstring"""



				if self.degree > polynomial_a.degree:
							SCREAMING_SNAKE_CASE_								=						self.coefficients[:]
							for i in range(polynomial_a.degree + 1      ):
										coefficients[i] += polynomial_a.coefficients[i]
							return Polynomial(self.degree      ,     SCREAMING_SNAKE_CASE      )
				else:
							SCREAMING_SNAKE_CASE_								=						polynomial_a.coefficients[:]
							for i in range(self.degree + 1      ):
										coefficients[i] += self.coefficients[i]
							return Polynomial(polynomial_a.degree      ,     SCREAMING_SNAKE_CASE      )





	def __sub__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Polynomial:




				"""simple docstring"""



				return self + polynomial_a * Polynomial(0      ,     [-1]      )





	def __neg__(							self      )  ->					Polynomial:




				"""simple docstring"""



				return Polynomial(self.degree      ,     [-c for c in self.coefficients]      )





	def __mul__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Polynomial:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[0] * (self.degree + polynomial_a.degree + 1)
				for i in range(self.degree + 1      ):
							for j in range(polynomial_a.degree + 1      ):
										coefficients[i + j] += (
										    self.coefficients[i] * polynomial_a.coefficients[j]
										)

				return Polynomial(self.degree + polynomial_a.degree      ,     SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					int | float:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						0
				for i in range(self.degree + 1      ):
							result += self.coefficients[i] * (substitution**i)
				return result





	def __str__(							self      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						''
				for i in range(self.degree      ,     -1      ,     -1      ):
							if self.coefficients[i] == 0:
										continue
							elif self.coefficients[i] > 0:
										if polynomial:
													polynomial += " + "
							else:
										polynomial += " - "

							if i == 0:
										polynomial += str(abs(self.coefficients[i]      )      )
							elif i == 1:
										polynomial += str(abs(self.coefficients[i]      )      ) + "x"
							else:
										polynomial += str(abs(self.coefficients[i]      )      ) + "x^" + str(SCREAMING_SNAKE_CASE      )

				return polynomial





	def __repr__(							self      )  ->					str:




				"""simple docstring"""



				return self.__str__()





	def 						A_(							self      )  ->					Polynomial:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[0] * self.degree
				for i in range(self.degree      ):
							SCREAMING_SNAKE_CASE_								=						self.coefficients[i + 1] * (i + 1)
				return Polynomial(self.degree - 1      ,     SCREAMING_SNAKE_CASE      )





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




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[0] * (self.degree + 2)
				SCREAMING_SNAKE_CASE_								=						constant
				for i in range(self.degree + 1      ):
							SCREAMING_SNAKE_CASE_								=						self.coefficients[i] / (i + 1)
				return Polynomial(self.degree + 1      ,     SCREAMING_SNAKE_CASE      )





	def __eq__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					bool:




				"""simple docstring"""



				if not isinstance(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ):
							return False

				if self.degree != polynomial_a.degree:
							return False

				for i in range(self.degree + 1      ):
							if self.coefficients[i] != polynomial_a.coefficients[i]:
										return False

				return True





	def __ne__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					bool:




				"""simple docstring"""



				return not self.__eq__(SCREAMING_SNAKE_CASE      )
