
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A     :	Union[str, Any]    = logging.get_logger(__name__)

A     :	List[Any]    = {
    'sail/poolformer_s12': 'https://huggingface.co/sail/poolformer_s12/resolve/main/config.json',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}







class 			__A( a			):
				snake_case_						   =    '''poolformer'''




				def __init__(     self		,     _snake_case=3		,     _snake_case=16		,     _snake_case=16		,     _snake_case=3		,     _snake_case=4.0		,     _snake_case=[2, 2, 6, 2]		,     _snake_case=[64, 128, 320, 512]		,     _snake_case=[7, 3, 3, 3]		,     _snake_case=[4, 2, 2, 2]		,     _snake_case=[2, 1, 1, 1]		,     _snake_case=4		,     _snake_case=0.0		,     _snake_case="gelu"		,     _snake_case=True		,     _snake_case=1E-5		,     _snake_case=0.02		,     **_snake_case		,     )    ->      str:


								'''simple docstring'''




								__a										=    num_channels
								__a										=    patch_size
								__a										=    stride
								__a										=    padding
								__a										=    pool_size
								__a										=    hidden_sizes
								__a										=    mlp_ratio
								__a										=    depths
								__a										=    patch_sizes
								__a										=    strides
								__a										=    num_encoder_blocks
								__a										=    drop_path_rate
								__a										=    hidden_act
								__a										=    use_layer_scale
								__a										=    layer_scale_init_value
								__a										=    initializer_range
								super().__init__(**_snake_case				)







class 			__A( a			):
				snake_case_						   =    version.parse('''1.11'''			)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Mapping[str, Mapping[int, str]]:


								'''simple docstring'''




								return OrderedDict(
								    [
								        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
								    ]				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      float:


								'''simple docstring'''




								return 2E-3

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			):
				snake_case_						   =    AutoencoderKL
				snake_case_						   =    '''sample'''
				snake_case_						   =    1E-2




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    4
								__a										=    3
								__a										=    (32, 32)

								__a										=    floats_tensor((batch_size, num_channels) + sizes				).to(_snake_case				)

								return {"sample": image}




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								return (3, 32, 32)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union[str, Any]:


								'''simple docstring'''




								return (3, 32, 32)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								__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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								pass




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[int]:


								'''simple docstring'''




								pass




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


								'''simple docstring'''




								__a							,     __a										=    self.prepare_init_args_and_inputs_for_common()
								__a										=    self.model_class(**_snake_case				)
								model.to(_snake_case				)

								assert not model.is_gradient_checkpointing and model.training

								__a										=    model(**_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()

								__a										=    torch.randn_like(_snake_case				)
								__a										=    (out - labels).mean()
								loss.backward()

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

								assert model_a.is_gradient_checkpointing and model_a.training

								__a										=    model_a(**_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()
								__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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




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

								model.to(_snake_case				)
								__a										=    model(**self.dummy_input				)

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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[str]:


								'''simple docstring'''




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

								if torch_device == "mps":
												__a										=    torch.manual_seed(0				)
								else:
												__a										=    torch.Generator(device=_snake_case				).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(_snake_case				)
								with torch.no_grad():
												__a										=    model(_snake_case		,     sample_posterior=_snake_case		,     generator=_snake_case				).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_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":
												__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(_snake_case		,     _snake_case		,     rtol=1E-2				)				)







@slow
class 			__A( unittest.TestCase			):




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=0		,     _snake_case=(4, 3, 512, 512)		,     _snake_case=False				)    ->      Any:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case="CompVis/stable-diffusion-v1-4"		,     _snake_case=False				)    ->      Optional[Any]:


								'''simple docstring'''




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

								__a										=    AutoencoderKL.from_pretrained(
								    _snake_case		,     subfolder='''vae'''		,     torch_dtype=_snake_case		,     revision=_snake_case		,     )
								model.to(_snake_case				).eval()

								return model




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=0				)    ->      Tuple:


								'''simple docstring'''




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




				@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 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case				)    ->      List[Any]:


								'''simple docstring'''




								__a										=    self.get_sd_vae_model()
								__a										=    self.get_sd_image(_snake_case				)
								__a										=    self.get_generator(_snake_case				)

								with torch.no_grad():
												__a										=    model(_snake_case		,     generator=_snake_case		,     sample_posterior=_snake_case				).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(_snake_case		,     _snake_case		,     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 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Tuple:


								'''simple docstring'''




								__a										=    self.get_sd_vae_model(fpaa=_snake_case				)
								__a										=    self.get_sd_image(_snake_case		,     fpaa=_snake_case				)
								__a										=    self.get_generator(_snake_case				)

								with torch.no_grad():
												__a										=    model(_snake_case		,     generator=_snake_case		,     sample_posterior=_snake_case				).sample

								assert sample.shape == image.shape

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

								assert torch_all_close(_snake_case		,     _snake_case		,     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 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    self.get_sd_vae_model()
								__a										=    self.get_sd_image(_snake_case				)

								with torch.no_grad():
												__a										=    model(_snake_case				).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(_snake_case		,     _snake_case		,     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 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    self.get_sd_vae_model()
								__a										=    self.get_sd_image(_snake_case		,     shape=(3, 4, 64, 64)				)

								with torch.no_grad():
												__a										=    model.decode(_snake_case				).sample

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

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

								assert torch_all_close(_snake_case		,     _snake_case		,     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 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    self.get_sd_vae_model(fpaa=_snake_case				)
								__a										=    self.get_sd_image(_snake_case		,     shape=(3, 4, 64, 64)		,     fpaa=_snake_case				)

								with torch.no_grad():
												__a										=    model.decode(_snake_case				).sample

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

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

								assert torch_all_close(_snake_case		,     _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 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    self.get_sd_vae_model(fpaa=_snake_case				)
								__a										=    self.get_sd_image(_snake_case		,     shape=(3, 4, 64, 64)		,     fpaa=_snake_case				)

								with torch.no_grad():
												__a										=    model.decode(_snake_case				).sample

								model.enable_xformers_memory_efficient_attention()
								with torch.no_grad():
												__a										=    model.decode(_snake_case				).sample

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

								assert torch_all_close(_snake_case		,     _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 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      List[str]:


								'''simple docstring'''




								__a										=    self.get_sd_vae_model()
								__a										=    self.get_sd_image(_snake_case		,     shape=(3, 4, 64, 64)				)

								with torch.no_grad():
												__a										=    model.decode(_snake_case				).sample

								model.enable_xformers_memory_efficient_attention()
								with torch.no_grad():
												__a										=    model.decode(_snake_case				).sample

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

								assert torch_all_close(_snake_case		,     _snake_case		,     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 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    self.get_sd_vae_model()
								__a										=    self.get_sd_image(_snake_case				)
								__a										=    self.get_generator(_snake_case				)

								with torch.no_grad():
												__a										=    model.encode(_snake_case				).latent_dist
												__a										=    dist.sample(generator=_snake_case				)

								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(_snake_case				)

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