


import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






def 			UpperCamelCase (     __magic_name__					:							Optional[Any]					)       ->  Optional[Any]:
		"""simple docstring"""




		lowercase__					      =		image.size
		lowercase__					      =		(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
		lowercase__					      =		image.resize((w, h)   ,   resample=PIL_INTERPOLATION["""lanczos"""]					)
		lowercase__					      =		np.array(lowerCamelCase_					).astype(np.floataa					) / 2_5_5.0
		lowercase__					      =		image[None].transpose(0   ,   3   ,   1   ,   2					)
		lowercase__					      =		torch.from_numpy(lowerCamelCase_					)
		return 2.0 * image - 1.0







class 							A   (	lowerCamelCase__							):
					'''simple docstring'''






					def __init__(self   :						str				, _UpperCAmelCase   :						Optional[Any]				, _UpperCAmelCase   :						Optional[int]				, _UpperCAmelCase   :						Optional[Any]				, )						->       Dict:




							"""simple docstring"""


							super().__init__()
							self.register_modules(vqvae=__lowerCamelCase				, unet=__lowerCamelCase				, scheduler=__lowerCamelCase		)


					@torch.no_grad()
					def __call__(self   :						Tuple				, _UpperCAmelCase   :						Optional[Any] = None				, _UpperCAmelCase   :						Tuple = 1				, _UpperCAmelCase   :						Any = 100				, _UpperCAmelCase   :						List[str] = 0.0				, _UpperCAmelCase   :						Optional[Any] = None				, _UpperCAmelCase   :						Union[str, Any] = "pil"				, _UpperCAmelCase   :						Any = True				, )						->       Union[Tuple, ImagePipelineOutput]:




							"""simple docstring"""


							if isinstance(__lowerCamelCase				, PIL.Image.Image		):
									lowercase__					      =		1
							elif isinstance(__lowerCamelCase				, torch.Tensor		):
									lowercase__					      =		image.shape[0]
							else:
									raise ValueError(f'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__lowerCamelCase		)}'''		)

							if isinstance(__lowerCamelCase				, PIL.Image.Image		):
									lowercase__					      =		preprocess(__lowerCamelCase		)

							lowercase__					      =		image.shape[-2:]

							# in_channels should be 6: 3 for latents, 3 for low resolution image
							lowercase__					      =		(batch_size, self.unet.config.in_channels // 2, height, width)
							lowercase__					      =		next(self.unet.parameters()		).dtype

							lowercase__					      =		randn_tensor(__lowerCamelCase				, generator=__lowerCamelCase				, device=self.device				, dtype=__lowerCamelCase		)

							lowercase__					      =		image.to(device=self.device				, dtype=__lowerCamelCase		)

							# set timesteps and move to the correct device
							self.scheduler.set_timesteps(__lowerCamelCase				, device=self.device		)
							lowercase__					      =		self.scheduler.timesteps

							# scale the initial noise by the standard deviation required by the scheduler
							lowercase__					      =		latents * self.scheduler.init_noise_sigma

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
							# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
							# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
							# and should be between [0, 1]
							lowercase__					      =		'''eta''' in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
							lowercase__					      =		{}
							if accepts_eta:
									lowercase__					      =		eta

							for t in self.progress_bar(__lowerCamelCase		):
									# concat latents and low resolution image in the channel dimension.
									lowercase__					      =		torch.cat([latents, image]				, dim=1		)
									lowercase__					      =		self.scheduler.scale_model_input(__lowerCamelCase				, __lowerCamelCase		)
									# predict the noise residual
									lowercase__					      =		self.unet(__lowerCamelCase				, __lowerCamelCase		).sample
									# compute the previous noisy sample x_t -> x_t-1
									lowercase__					      =		self.scheduler.step(__lowerCamelCase				, __lowerCamelCase				, __lowerCamelCase				, **__lowerCamelCase		).prev_sample

							# decode the image latents with the VQVAE
							lowercase__					      =		self.vqvae.decode(__lowerCamelCase		).sample
							lowercase__					      =		torch.clamp(__lowerCamelCase				, -1.0				, 1.0		)
							lowercase__					      =		image / 2 + 0.5
							lowercase__					      =		image.cpu().permute(0				, 2				, 3				, 1		).numpy()

							if output_type == "pil":
									lowercase__					      =		self.numpy_to_pil(__lowerCamelCase		)

							if not return_dict:
									return (image,)

							return ImagePipelineOutput(images=__lowerCamelCase		)





import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
	import jax
	import jax.numpy as jnp



@slow
@require_flax
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					def   lowerCamelCase__					(self   :						Any				, _UpperCAmelCase   :						List[str]				, _UpperCAmelCase   :						Union[str, Any]		)						->       int:




							"""simple docstring"""


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

					def   lowerCamelCase__					(self   :						Any		)						->       int:




							"""simple docstring"""


							super().tearDown()
							gc.collect()

					def   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						Any=0				, _UpperCAmelCase   :						Optional[Any]=(4, 4, 64, 64)				, _UpperCAmelCase   :						Any=False		)						->       List[Any]:




							"""simple docstring"""


							lowercase__					      =		jnp.bfloataa if fpaa else jnp.floataa
							lowercase__					      =		jnp.array(load_hf_numpy(self.get_file_format(_UpperCAmelCase				, _UpperCAmelCase		)		)				, dtype=_UpperCAmelCase		)
							return image

					def   lowerCamelCase__					(self   :						Optional[Any]				, _UpperCAmelCase   :						str=False				, _UpperCAmelCase   :						str="CompVis/stable-diffusion-v1-4"		)						->       Dict:




							"""simple docstring"""


							lowercase__					      =		jnp.bfloataa if fpaa else jnp.floataa
							lowercase__					      =		"""bf16""" if fpaa else None

							lowercase__      ,     lowercase__					      =		FlaxUNetaDConditionModel.from_pretrained(
							    _UpperCAmelCase				, subfolder="""unet"""				, dtype=_UpperCAmelCase				, revision=_UpperCAmelCase		)
							return model, params

					def   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						int=0				, _UpperCAmelCase   :						Optional[int]=(4, 77, 768)				, _UpperCAmelCase   :						Any=False		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__					      =		jnp.bfloataa if fpaa else jnp.floataa
							lowercase__					      =		jnp.array(load_hf_numpy(self.get_file_format(_UpperCAmelCase				, _UpperCAmelCase		)		)				, dtype=_UpperCAmelCase		)
							return hidden_states

					@parameterized.expand(
					    [
					        # fmt: off
					        [83, 4, [-0.2_323, -0.1_304, 0.0_813, -0.3_093, -0.0_919, -0.1_571, -0.1_125, -0.5_806]],
					        [17, 0.55, [-0.0_831, -0.2_443, 0.0_901, -0.0_919, 0.3_396, 0.0_103, -0.3_743, 0.0_701]],
					        [8, 0.89, [-0.4_863, 0.0_859, 0.0_875, -0.1_658, 0.9_199, -0.0_114, 0.4_839, 0.4_639]],
					        [3, 1000, [-0.5_649, 0.2_402, -0.5_518, 0.1_248, 1.1_328, -0.2_443, -0.0_325, -1.0_078]],
					        # fmt: on
					    ]		)
					def   lowerCamelCase__					(self   :						int				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						Union[str, Any]				, _UpperCAmelCase   :						int		)						->       str:




							"""simple docstring"""


							lowercase__      ,     lowercase__					      =		self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""				, fpaa=_UpperCAmelCase		)
							lowercase__					      =		self.get_latents(_UpperCAmelCase				, fpaa=_UpperCAmelCase		)
							lowercase__					      =		self.get_encoder_hidden_states(_UpperCAmelCase				, fpaa=_UpperCAmelCase		)

							lowercase__					      =		model.apply(
							    {"""params""": params}				, _UpperCAmelCase				, jnp.array(_UpperCAmelCase				, dtype=jnp.intaa		)				, encoder_hidden_states=_UpperCAmelCase				, ).sample

							assert sample.shape == latents.shape

							lowercase__					      =		jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())		)				, dtype=jnp.floataa		)
							lowercase__					      =		jnp.array(_UpperCAmelCase				, dtype=jnp.floataa		)

							# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
							assert jnp.allclose(_UpperCAmelCase				, _UpperCAmelCase				, atol=1E-2		)


					@parameterized.expand(
					    [
					        # fmt: off
					        [83, 4, [0.1_514, 0.0_807, 0.1_624, 0.1_016, -0.1_896, 0.0_263, 0.0_677, 0.2_310]],
					        [17, 0.55, [0.1_164, -0.0_216, 0.0_170, 0.1_589, -0.3_120, 0.1_005, -0.0_581, -0.1_458]],
					        [8, 0.89, [-0.1_758, -0.0_169, 0.1_004, -0.1_411, 0.1_312, 0.1_103, -0.1_996, 0.2_139]],
					        [3, 1000, [0.1_214, 0.0_352, -0.0_731, -0.1_562, -0.0_994, -0.0_906, -0.2_340, -0.0_539]],
					        # fmt: on
					    ]		)
					def   lowerCamelCase__					(self   :						Optional[Any]				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						List[str]		)						->       str:




							"""simple docstring"""


							lowercase__      ,     lowercase__					      =		self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""				, fpaa=_UpperCAmelCase		)
							lowercase__					      =		self.get_latents(_UpperCAmelCase				, shape=(4, 4, 96, 96)				, fpaa=_UpperCAmelCase		)
							lowercase__					      =		self.get_encoder_hidden_states(_UpperCAmelCase				, shape=(4, 77, 1024)				, fpaa=_UpperCAmelCase		)

							lowercase__					      =		model.apply(
							    {"""params""": params}				, _UpperCAmelCase				, jnp.array(_UpperCAmelCase				, dtype=jnp.intaa		)				, encoder_hidden_states=_UpperCAmelCase				, ).sample

							assert sample.shape == latents.shape

							lowercase__					      =		jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())		)				, dtype=jnp.floataa		)
							lowercase__					      =		jnp.array(_UpperCAmelCase				, dtype=jnp.floataa		)

							# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
							assert jnp.allclose(_UpperCAmelCase				, _UpperCAmelCase				, atol=1E-2		)

