





'''simple docstring'''


import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


_SCREAMING_SNAKE_CASE									=				{
    "gwf-440k": {
        "url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
        "sample_rate": 4_80_00,
        "sample_size": 6_55_36,
    },
    "jmann-small-190k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
        "sample_rate": 4_80_00,
        "sample_size": 6_55_36,
    },
    "jmann-large-580k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
        "sample_rate": 4_80_00,
        "sample_size": 13_10_72,
    },
    "maestro-uncond-150k": {
        "url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
        "sample_rate": 1_60_00,
        "sample_size": 6_55_36,
    },
    "unlocked-uncond-250k": {
        "url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
        "sample_rate": 1_60_00,
        "sample_size": 6_55_36,
    },
    "honk-140k": {
        "url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
        "sample_rate": 1_60_00,
        "sample_size": 6_55_36,
    },
}


def 						__a(SCREAMING_SNAKE_CASE_	:       List[str]		,							SCREAMING_SNAKE_CASE_	:       Any     ):



    '''simple docstring'''





    return torch.atana(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     ) / math.pi * 2


def 						__a(SCREAMING_SNAKE_CASE_	:       int     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   torch.sin(t * math.pi / 2     ) ** 2
    _lowerCAmelCase						      =   (1 - sigma**2) ** 0.5
    return alpha_sigma_to_t(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )

class  lowerCAmelCase_		(		__magic_name__		):
 pass







class  lowerCAmelCase_		(		nn.Module		):




 def __init__(						self      ,  _lowerCAmelCase		)     ->				Any:
     super().__init__()

     _lowerCAmelCase						      =   DiffusionAttnUnetaD(_lowerCAmelCase      ,  n_attn_layers=4		)
     _lowerCAmelCase						      =   deepcopy(self.diffusion		)
     _lowerCAmelCase						      =   torch.quasirandom.SobolEngine(1      ,  scramble=_lowerCAmelCase		)







def 						__a(SCREAMING_SNAKE_CASE_	:       List[str]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   MODELS_MAP[model_name]["url"]
    os.system(F'''wget {url} ./'''     )

    return F'''./{model_name}.ckpt'''


_SCREAMING_SNAKE_CASE									=				{
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
}
_SCREAMING_SNAKE_CASE									=				{
    "8": "resnets.0",
    "9": "attentions.0",
    "10": "resnets.1",
    "11": "attentions.1",
    "12": "resnets.2",
    "13": "attentions.2",
}
_SCREAMING_SNAKE_CASE									=				{
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
    "8": "resnets.3",
    "9": "attentions.3",
    "10": "resnets.4",
    "11": "attentions.4",
    "12": "resnets.5",
    "13": "attentions.5",
}
_SCREAMING_SNAKE_CASE									=				{
    "0": "resnets.0",
    "1": "resnets.1",
    "2": "resnets.2",
    "4": "resnets.0",
    "5": "resnets.1",
    "6": "resnets.2",
}

_SCREAMING_SNAKE_CASE									=				{
    "skip": "conv_skip",
    "main.0": "conv_1",
    "main.1": "group_norm_1",
    "main.3": "conv_2",
    "main.4": "group_norm_2",
}

_SCREAMING_SNAKE_CASE									=				{
    "norm": "group_norm",
    "qkv_proj": ["query", "key", "value"],
    "out_proj": ["proj_attn"],
}


def 						__a(SCREAMING_SNAKE_CASE_	:       int     ):



    '''simple docstring'''





    if name.startswith("skip"     ):
        return name.replace("skip"		,							RES_CONV_MAP["skip"]     )

    # name has to be of format main.{digit}
    if not name.startswith("main."     ):
        raise ValueError(F'''ResConvBlock error with {name}'''     )

    return name.replace(name[:6]		,							RES_CONV_MAP[name[:6]]     )


def 						__a(SCREAMING_SNAKE_CASE_	:       int     ):



    '''simple docstring'''





    for key, value in ATTN_MAP.items():
        if name.startswith(SCREAMING_SNAKE_CASE_     ) and not isinstance(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     ):
            return name.replace(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )
        elif name.startswith(SCREAMING_SNAKE_CASE_     ):
            return [name.replace(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     ) for v in value]
    raise ValueError(F'''Attn error with {name}'''     )


def 						__a(SCREAMING_SNAKE_CASE_	:       List[Any]		,							SCREAMING_SNAKE_CASE_	:       str=13     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   input_string

    if string.split("."     )[0] == "timestep_embed":
        return string.replace("timestep_embed"		,							"time_proj"     )

    _lowerCAmelCase						      =   0
    if string.startswith("net.3."     ):
        depth += 1
        _lowerCAmelCase						      =   string[6:]
    elif string.startswith("net."     ):
        _lowerCAmelCase						      =   string[4:]

    while string.startswith("main.7."     ):
        depth += 1
        _lowerCAmelCase						      =   string[7:]

    if string.startswith("main."     ):
        _lowerCAmelCase						      =   string[5:]

    # mid block
    if string[:2].isdigit():
        _lowerCAmelCase						      =   string[:2]
        _lowerCAmelCase						      =   string[2:]
    else:
        _lowerCAmelCase						      =   string[0]
        _lowerCAmelCase						      =   string[1:]

    if depth == max_depth:
        _lowerCAmelCase						      =   MID_NUM_TO_LAYER[layer_num]
        _lowerCAmelCase						      =   "mid_block"
    elif depth > 0 and int(SCREAMING_SNAKE_CASE_     ) < 7:
        _lowerCAmelCase						      =   DOWN_NUM_TO_LAYER[layer_num]
        _lowerCAmelCase						      =   F'''down_blocks.{depth}'''
    elif depth > 0 and int(SCREAMING_SNAKE_CASE_     ) > 7:
        _lowerCAmelCase						      =   UP_NUM_TO_LAYER[layer_num]
        _lowerCAmelCase						      =   F'''up_blocks.{max_depth - depth - 1}'''
    elif depth == 0:
        _lowerCAmelCase						      =   DEPTH_0_TO_LAYER[layer_num]
        _lowerCAmelCase						      =   F'''up_blocks.{max_depth - 1}''' if int(SCREAMING_SNAKE_CASE_     ) > 3 else "down_blocks.0"

    if not string_left.startswith("."     ):
        raise ValueError(F'''Naming error with {input_string} and string_left: {string_left}.'''     )

    _lowerCAmelCase						      =   string_left[1:]

    if "resnets" in new_layer:
        _lowerCAmelCase						      =   convert_resconv_naming(SCREAMING_SNAKE_CASE_     )
    elif "attentions" in new_layer:
        _lowerCAmelCase						      =   convert_attn_naming(SCREAMING_SNAKE_CASE_     )
        _lowerCAmelCase						      =   new_string_left

    if not isinstance(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     ):
        _lowerCAmelCase						      =   prefix + "." + new_layer + "." + string_left
    else:
        _lowerCAmelCase						      =   [prefix + "." + new_layer + "." + s for s in string_left]
    return new_string


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[Any]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   {}
    for k, v in state_dict.items():
        if k.endswith("kernel"     ):
            # up- and downsample layers, don't have trainable weights
            continue

        _lowerCAmelCase						      =   rename(SCREAMING_SNAKE_CASE_     )

        # check if we need to transform from Conv => Linear for attention
        if isinstance(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     ):
            _lowerCAmelCase						      =   transform_conv_attns(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )
        else:
            _lowerCAmelCase						      =   v

    return new_state_dict


def 						__a(SCREAMING_SNAKE_CASE_	:       Dict		,							SCREAMING_SNAKE_CASE_	:       List[str]		,							SCREAMING_SNAKE_CASE_	:       Optional[Any]     ):



    '''simple docstring'''





    if len(SCREAMING_SNAKE_CASE_     ) == 1:
        if len(v.shape     ) == 3:
            # weight
            _lowerCAmelCase						      =   v[:, :, 0]
        else:
            # bias
            _lowerCAmelCase						      =   v
    else:
        # qkv matrices
        _lowerCAmelCase						      =   v.shape[0]
        _lowerCAmelCase						      =   trippled_shape // 3
        for i in range(3     ):
            if len(v.shape     ) == 3:
                _lowerCAmelCase						      =   v[i * single_shape : (i + 1) * single_shape, :, 0]
            else:
                _lowerCAmelCase						      =   v[i * single_shape : (i + 1) * single_shape]
    return new_state_dict


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[Any]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   torch.device("cuda" if torch.cuda.is_available() else "cpu"     )

    _lowerCAmelCase						      =   args.model_path.split("/"     )[-1].split("."     )[0]
    if not os.path.isfile(args.model_path     ):
        assert (
            model_name == args.model_path
        ), F'''Make sure to provide one of the official model names {MODELS_MAP.keys()}'''
        _lowerCAmelCase						      =   download(SCREAMING_SNAKE_CASE_     )

    _lowerCAmelCase						      =   MODELS_MAP[model_name]["sample_rate"]
    _lowerCAmelCase						      =   MODELS_MAP[model_name]["sample_size"]

    _lowerCAmelCase						      =   Object()
    _lowerCAmelCase						      =   sample_size
    _lowerCAmelCase						      =   sample_rate
    _lowerCAmelCase						      =   0

    _lowerCAmelCase						      =   UNetaDModel(sample_size=SCREAMING_SNAKE_CASE_		,							sample_rate=SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   diffusers_model.state_dict()

    _lowerCAmelCase						      =   DiffusionUncond(SCREAMING_SNAKE_CASE_     )
    orig_model.load_state_dict(torch.load(args.model_path		,							map_location=SCREAMING_SNAKE_CASE_     )["state_dict"]     )
    _lowerCAmelCase						      =   orig_model.diffusion_ema.eval()
    _lowerCAmelCase						      =   orig_model.state_dict()
    _lowerCAmelCase						      =   rename_orig_weights(SCREAMING_SNAKE_CASE_     )

    _lowerCAmelCase						      =   set(renamed_state_dict.keys()     ) - set(diffusers_state_dict.keys()     )
    _lowerCAmelCase						      =   set(diffusers_state_dict.keys()     ) - set(renamed_state_dict.keys()     )

    assert len(SCREAMING_SNAKE_CASE_     ) == 0, F'''Problem with {renamed_minus_diffusers}'''
    assert all(k.endswith("kernel"     ) for k in list(SCREAMING_SNAKE_CASE_     )     ), F'''Problem with {diffusers_minus_renamed}'''

    for key, value in renamed_state_dict.items():
        assert (
            diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
        ), F'''Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}'''
        if key == "time_proj.weight":
            _lowerCAmelCase						      =   value.squeeze()

        _lowerCAmelCase						      =   value

    diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE_     )

    _lowerCAmelCase						      =   100
    _lowerCAmelCase						      =   33

    _lowerCAmelCase						      =   IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE_     )

    _lowerCAmelCase						      =   torch.manual_seed(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   torch.randn([1, 2, config.sample_size]		,							generator=SCREAMING_SNAKE_CASE_     ).to(SCREAMING_SNAKE_CASE_     )

    _lowerCAmelCase						      =   torch.linspace(1		,							0		,							steps + 1		,							device=SCREAMING_SNAKE_CASE_     )[:-1]
    _lowerCAmelCase						      =   get_crash_schedule(SCREAMING_SNAKE_CASE_     )

    _lowerCAmelCase						      =   DanceDiffusionPipeline(unet=SCREAMING_SNAKE_CASE_		,							scheduler=SCREAMING_SNAKE_CASE_     )

    _lowerCAmelCase						      =   torch.manual_seed(33     )
    _lowerCAmelCase						      =   pipe(num_inference_steps=SCREAMING_SNAKE_CASE_		,							generator=SCREAMING_SNAKE_CASE_     ).audios

    _lowerCAmelCase						      =   sampling.iplms_sample(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							{}     )
    _lowerCAmelCase						      =   generated.clamp(-1		,							1     )

    _lowerCAmelCase						      =   (generated - audio).abs().sum()
    _lowerCAmelCase						      =   (generated - audio).abs().max()

    if args.save:
        pipe.save_pretrained(args.checkpoint_path     )

    print("Diff sum"		,							SCREAMING_SNAKE_CASE_     )
    print("Diff max"		,							SCREAMING_SNAKE_CASE_     )

    assert diff_max < 1e-3, F'''Diff max: {diff_max} is too much :-/'''

    print(F'''Conversion for {model_name} successful!'''     )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE									=				argparse.ArgumentParser()

       parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
       parser.add_argument(
           "--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
       )
       parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
       _SCREAMING_SNAKE_CASE									=				parser.parse_args()

       main(args)









'''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		)


