


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  ):



  """simple docstring"""



  _lowercase									=		AutoencoderKL
  _lowercase									=		'sample'
  _lowercase									=		1e-2

  @property
  def    _UpperCamelCase( self			:  str       ):
        a__	:  Any     =   4
        a__	:  Union[str, Any]     =   3
        a__	:  Dict     =   (32, 32)

        a__	:  int     =   floats_tensor((batch_size, num_channels) + sizes       ).to(lowerCamelCase__       )

        return {"sample": image}

  @property
  def    _UpperCamelCase( self			:  List[Any]       ):
        return (3, 32, 32)

  @property
  def    _UpperCamelCase( self			:  Dict       ):
        return (3, 32, 32)

  def    _UpperCamelCase( self			:  List[str]       ):
        a__	:  str     =   {
            "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__	:  List[str]     =   self.dummy_input
        return init_dict, inputs_dict

  def    _UpperCamelCase( self			:  Tuple       ):
        pass

  def    _UpperCamelCase( self			:  Any       ):
        pass

  @unittest.skipIf(torch_device == "mps"				,				"Gradient checkpointing skipped on MPS"       )
  def    _UpperCamelCase( self			:  List[str]       ):
        # enable deterministic behavior for gradient checkpointing
        a__,	a__	:  Union[str, Any]     =   self.prepare_init_args_and_inputs_for_common()
        a__	:  Dict     =   self.model_class(**lowerCamelCase__       )
        model.to(lowerCamelCase__       )

        assert not model.is_gradient_checkpointing and model.training

        a__	:  str     =   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__	:  Union[str, Any]     =   torch.randn_like(lowerCamelCase__       )
        a__	:  Optional[int]     =   (out - labels).mean()
        loss.backward()

        # re-instantiate the model now enabling gradient checkpointing
        a__	:  Any     =   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__	:  int     =   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__	:  Any     =   (out_a - labels).mean()
        loss_a.backward()

        # compare the output and parameters gradients
        self.assertTrue((loss - loss_a).abs() < 1E-5       )
        a__	:  str     =   dict(model.named_parameters()       )
        a__	:  List[str]     =   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    _UpperCamelCase( self			:  Dict       ):
        a__,	a__	:  Union[str, Any]     =   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__	:  Tuple     =   model(**self.dummy_input       )

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

  def    _UpperCamelCase( self			:  Optional[int]       ):
        a__	:  Optional[int]     =   AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"       )
        a__	:  List[str]     =   model.to(lowerCamelCase__       )
        model.eval()

        if torch_device == "mps":
              a__	:  int     =   torch.manual_seed(0       )
        else:
              a__	:  List[str]     =   torch.Generator(device=lowerCamelCase__       ).manual_seed(0       )

        a__	:  List[str]     =   torch.randn(
            1				,				model.config.in_channels				,				model.config.sample_size				,				model.config.sample_size				,				generator=torch.manual_seed(0       )				,				)
        a__	:  Optional[Any]     =   image.to(lowerCamelCase__       )
        with torch.no_grad():
              a__	:  str     =   model(lowerCamelCase__				,				sample_posterior=lowerCamelCase__				,				generator=lowerCamelCase__       ).sample

        a__	:  Dict     =   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__	:  Tuple     =   torch.tensor(
                  [
                      -4.00_78E-01,
                      -3.83_23E-04,
                      -1.26_81E-01,
                      -1.14_62E-01,
                      2.00_95E-01,
                      1.08_93E-01,
                      -8.82_47E-02,
                      -3.03_61E-01,
                      -9.86_44E-03,
                  ]       )
        elif torch_device == "cpu":
              a__	:  Tuple     =   torch.tensor(
                  [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]       )
        else:
              a__	:  Union[str, Any]     =   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 A__     ( unittest.TestCase  ):



  """simple docstring"""



  def    _UpperCamelCase( self			:  Optional[int]				,				lowerCamelCase__			:  Optional[Any]				,				lowerCamelCase__			:  List[Any]       ):
        return f'''gaussian_noise_s={seed}_shape={'_'.join([str(lowerCamelCase__       ) for s in shape]       )}.npy'''

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

  def    _UpperCamelCase( self			:  Union[str, Any]				,				lowerCamelCase__			:  Dict=0				,				lowerCamelCase__			:  Optional[int]=(4, 3, 512, 512)				,				lowerCamelCase__			:  Tuple=False       ):
        a__	:  Dict     =   torch.floataa if fpaa else torch.floataa
        a__	:  str     =   torch.from_numpy(load_hf_numpy(self.get_file_format(lowerCamelCase__				,				lowerCamelCase__       )       )       ).to(lowerCamelCase__       ).to(lowerCamelCase__       )
        return image

  def    _UpperCamelCase( self			:  Optional[int]				,				lowerCamelCase__			:  List[Any]="CompVis/stable-diffusion-v1-4"				,				lowerCamelCase__			:  Optional[Any]=False       ):
        a__	:  Tuple     =   "fp16" if fpaa else None
        a__	:  Optional[Any]     =   torch.floataa if fpaa else torch.floataa

        a__	:  Optional[int]     =   AutoencoderKL.from_pretrained(
            lowerCamelCase__				,				subfolder="vae"				,				torch_dtype=lowerCamelCase__				,				revision=lowerCamelCase__				,				)
        model.to(lowerCamelCase__       ).eval()

        return model

  def    _UpperCamelCase( self			:  Any				,				lowerCamelCase__			:  int=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    _UpperCamelCase( self			:  Optional[int]				,				lowerCamelCase__			:  Any				,				lowerCamelCase__			:  List[str]				,				lowerCamelCase__			:  str       ):
        a__	:  List[str]     =   self.get_sd_vae_model()
        a__	:  Dict     =   self.get_sd_image(lowerCamelCase__       )
        a__	:  List[str]     =   self.get_generator(lowerCamelCase__       )

        with torch.no_grad():
              a__	:  str     =   model(lowerCamelCase__				,				generator=lowerCamelCase__				,				sample_posterior=lowerCamelCase__       ).sample

        assert sample.shape == image.shape

        a__	:  Optional[Any]     =   sample[-1, -2:, -2:, :2].flatten().float().cpu()
        a__	:  Dict     =   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    _UpperCamelCase( self			:  Dict				,				lowerCamelCase__			:  List[str]				,				lowerCamelCase__			:  List[Any]       ):
        a__	:  Union[str, Any]     =   self.get_sd_vae_model(fpaa=lowerCamelCase__       )
        a__	:  List[str]     =   self.get_sd_image(lowerCamelCase__				,				fpaa=lowerCamelCase__       )
        a__	:  Tuple     =   self.get_generator(lowerCamelCase__       )

        with torch.no_grad():
              a__	:  List[str]     =   model(lowerCamelCase__				,				generator=lowerCamelCase__				,				sample_posterior=lowerCamelCase__       ).sample

        assert sample.shape == image.shape

        a__	:  Optional[Any]     =   sample[-1, -2:, :2, -2:].flatten().float().cpu()
        a__	:  Dict     =   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    _UpperCamelCase( self			:  Tuple				,				lowerCamelCase__			:  Tuple				,				lowerCamelCase__			:  List[Any]				,				lowerCamelCase__			:  List[Any]       ):
        a__	:  List[Any]     =   self.get_sd_vae_model()
        a__	:  str     =   self.get_sd_image(lowerCamelCase__       )

        with torch.no_grad():
              a__	:  Optional[int]     =   model(lowerCamelCase__       ).sample

        assert sample.shape == image.shape

        a__	:  Optional[Any]     =   sample[-1, -2:, -2:, :2].flatten().float().cpu()
        a__	:  List[Any]     =   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    _UpperCamelCase( self			:  Tuple				,				lowerCamelCase__			:  Any				,				lowerCamelCase__			:  List[str]       ):
        a__	:  Any     =   self.get_sd_vae_model()
        a__	:  Optional[Any]     =   self.get_sd_image(lowerCamelCase__				,				shape=(3, 4, 64, 64)       )

        with torch.no_grad():
              a__	:  Dict     =   model.decode(lowerCamelCase__       ).sample

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

        a__	:  Tuple     =   sample[-1, -2:, :2, -2:].flatten().cpu()
        a__	:  int     =   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    _UpperCamelCase( self			:  Union[str, Any]				,				lowerCamelCase__			:  Union[str, Any]				,				lowerCamelCase__			:  Tuple       ):
        a__	:  str     =   self.get_sd_vae_model(fpaa=lowerCamelCase__       )
        a__	:  Any     =   self.get_sd_image(lowerCamelCase__				,				shape=(3, 4, 64, 64)				,				fpaa=lowerCamelCase__       )

        with torch.no_grad():
              a__	:  List[str]     =   model.decode(lowerCamelCase__       ).sample

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

        a__	:  List[str]     =   sample[-1, -2:, :2, -2:].flatten().float().cpu()
        a__	:  Tuple     =   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    _UpperCamelCase( self			:  str				,				lowerCamelCase__			:  List[str]       ):
        a__	:  int     =   self.get_sd_vae_model(fpaa=lowerCamelCase__       )
        a__	:  Any     =   self.get_sd_image(lowerCamelCase__				,				shape=(3, 4, 64, 64)				,				fpaa=lowerCamelCase__       )

        with torch.no_grad():
              a__	:  Union[str, Any]     =   model.decode(lowerCamelCase__       ).sample

        model.enable_xformers_memory_efficient_attention()
        with torch.no_grad():
              a__	:  List[str]     =   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    _UpperCamelCase( self			:  List[str]				,				lowerCamelCase__			:  Optional[Any]       ):
        a__	:  Any     =   self.get_sd_vae_model()
        a__	:  int     =   self.get_sd_image(lowerCamelCase__				,				shape=(3, 4, 64, 64)       )

        with torch.no_grad():
              a__	:  Union[str, Any]     =   model.decode(lowerCamelCase__       ).sample

        model.enable_xformers_memory_efficient_attention()
        with torch.no_grad():
              a__	:  str     =   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    _UpperCamelCase( self			:  Optional[int]				,				lowerCamelCase__			:  Dict				,				lowerCamelCase__			:  Optional[int]       ):
        a__	:  Union[str, Any]     =   self.get_sd_vae_model()
        a__	:  Optional[Any]     =   self.get_sd_image(lowerCamelCase__       )
        a__	:  Any     =   self.get_generator(lowerCamelCase__       )

        with torch.no_grad():
              a__	:  Optional[Any]     =   model.encode(lowerCamelCase__       ).latent_dist
              a__	:  Any     =   dist.sample(generator=lowerCamelCase__       )

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

        a__	:  Any     =   sample[0, -1, -3:, -3:].flatten().cpu()
        a__	:  Dict     =   torch.tensor(lowerCamelCase__       )

        a__	:  Optional[int]     =   3E-3 if torch_device != "mps" else 1E-2
        assert torch_all_close(lowerCamelCase__				,				lowerCamelCase__				,				atol=lowerCamelCase__       )


'''simple docstring'''






from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)



