"""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  snake_case__      (       snake_case_,						snake_case_,						unittest.TestCase       ):
 _snake_case       :					Optional[Any]							  =						AutoencoderKL
 _snake_case       :					List[Any]							  =						"sample"
 _snake_case       :					Tuple							  =						1E-2


 @property
 def 						a__   ( self						):
        __a       =    4
        __a       =    3
        __a       =    (32, 32)

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

        return {"sample": image}


 @property
 def 						a__   ( self						):
        return (3, 32, 32)


 @property
 def 						a__   ( self						):
        return (3, 32, 32)


 def 						a__   ( self						):
        __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 						a__   ( self						):
        pass


 def 						a__   ( self						):
        pass


 @unittest.skipIf(torch_device == "mps"    ,		"Gradient checkpointing skipped on MPS"						)
 def 						a__   ( self						):
        # enable deterministic behavior for gradient checkpointing
        __a ,   __a       =    self.prepare_init_args_and_inputs_for_common()
        __a       =    self.model_class(**_lowerCAmelCase						)
        model.to(_lowerCAmelCase						)

        assert not model.is_gradient_checkpointing and model.training

        __a       =    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()

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

        # re-instantiate the model now enabling gradient checkpointing
        __a       =    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

        __a       =    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()
        __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 						a__   ( self						):
        __a ,   __a       =    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						)
        __a       =    model(**self.dummy_input						)

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






 def 						a__   ( self						):
        __a       =    AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"						)
        __a       =    model.to(_lowerCAmelCase						)
        model.eval()

        if torch_device == "mps":
               __a       =    torch.manual_seed(0						)
        else:
               __a       =    torch.Generator(device=_lowerCAmelCase						).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(_lowerCAmelCase						)
        with torch.no_grad():
               __a       =    model(_lowerCAmelCase    ,		sample_posterior=_lowerCAmelCase    ,		generator=_lowerCAmelCase						).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(
                   [
                       -4.0078E-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":
               __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(_lowerCAmelCase    ,		_lowerCAmelCase    ,		rtol=1E-2						)						)






@slow
class  snake_case__      (       unittest.TestCase       ):


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase						):
        return F"gaussian_noise_s={seed}_shape={'_'.join([str(_lowerCAmelCase						) for s in shape]						)}.npy"


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


 def 						a__   ( self    ,		lowerCamelCase=0    ,		lowerCamelCase=(4, 3, 512, 512)    ,		lowerCamelCase=False						):
        __a       =    torch.floataa if fpaa else torch.floataa
        __a       =    torch.from_numpy(load_hf_numpy(self.get_file_format(_lowerCAmelCase    ,		_lowerCAmelCase						)						)						).to(_lowerCAmelCase						).to(_lowerCAmelCase						)
        return image


 def 						a__   ( self    ,		lowerCamelCase="CompVis/stable-diffusion-v1-4"    ,		lowerCamelCase=False						):
        __a       =    "fp16" if fpaa else None
        __a       =    torch.floataa if fpaa else torch.floataa

        __a       =    AutoencoderKL.from_pretrained(
            _lowerCAmelCase    ,		subfolder="vae"    ,		torch_dtype=_lowerCAmelCase    ,		revision=_lowerCAmelCase    ,		)
        model.to(_lowerCAmelCase						).eval()

        return model


 def 						a__   ( self    ,		lowerCamelCase=0						):
        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 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    self.get_sd_vae_model()
        __a       =    self.get_sd_image(_lowerCAmelCase						)
        __a       =    self.get_generator(_lowerCAmelCase						)

        with torch.no_grad():
               __a       =    model(_lowerCAmelCase    ,		generator=_lowerCAmelCase    ,		sample_posterior=_lowerCAmelCase						).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(_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 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    self.get_sd_vae_model(fpaa=_lowerCAmelCase						)
        __a       =    self.get_sd_image(_lowerCAmelCase    ,		fpaa=_lowerCAmelCase						)
        __a       =    self.get_generator(_lowerCAmelCase						)

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

        assert sample.shape == image.shape

        __a       =    sample[-1, -2:, :2, -2:].flatten().float().cpu()
        __a       =    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 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    self.get_sd_vae_model()
        __a       =    self.get_sd_image(_lowerCAmelCase						)

        with torch.no_grad():
               __a       =    model(_lowerCAmelCase						).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(_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 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    self.get_sd_vae_model()
        __a       =    self.get_sd_image(_lowerCAmelCase    ,		shape=(3, 4, 64, 64)						)

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

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

        __a       =    sample[-1, -2:, :2, -2:].flatten().cpu()
        __a       =    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 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    self.get_sd_vae_model(fpaa=_lowerCAmelCase						)
        __a       =    self.get_sd_image(_lowerCAmelCase    ,		shape=(3, 4, 64, 64)    ,		fpaa=_lowerCAmelCase						)

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

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

        __a       =    sample[-1, -2:, :2, -2:].flatten().float().cpu()
        __a       =    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 						a__   ( self    ,		lowerCamelCase						):
        __a       =    self.get_sd_vae_model(fpaa=_lowerCAmelCase						)
        __a       =    self.get_sd_image(_lowerCAmelCase    ,		shape=(3, 4, 64, 64)    ,		fpaa=_lowerCAmelCase						)

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

        model.enable_xformers_memory_efficient_attention()
        with torch.no_grad():
               __a       =    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 						a__   ( self    ,		lowerCamelCase						):
        __a       =    self.get_sd_vae_model()
        __a       =    self.get_sd_image(_lowerCAmelCase    ,		shape=(3, 4, 64, 64)						)

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

        model.enable_xformers_memory_efficient_attention()
        with torch.no_grad():
               __a       =    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 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    self.get_sd_vae_model()
        __a       =    self.get_sd_image(_lowerCAmelCase						)
        __a       =    self.get_generator(_lowerCAmelCase						)

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

        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(_lowerCAmelCase						)

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







'''simple docstring'''


import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

class  lowerCAmelCase_		(		__magic_name__		):




 def __init__(						self      ,  *_lowerCAmelCase      ,  **_lowerCAmelCase		)     ->				None:
     warnings.warn(
         "The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
         " use YolosImageProcessor instead."      ,  _lowerCAmelCase      ,  )
     super().__init__(*_lowerCAmelCase      ,  **_lowerCAmelCase		)


