





"""simple docstring"""





from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowercase__				:Optional[Any]				   =       transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Optional[int]:





  """simple docstring"""


  if isinstance(UpperCAmelCase_  ,			torch.Tensor  ):
    return image
  elif isinstance(UpperCAmelCase_  ,			PIL.Image.Image  ):
    __UpperCAmelCase    :						Optional[int]       =						[image]

  __UpperCAmelCase    :						List[str]       =						[trans(img.convert('''RGB'''  )  ) for img in image]
  __UpperCAmelCase    :						Optional[Any]       =						torch.stack(UpperCAmelCase_  )
  return image







class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      def __init__(							self       :      Any						,				__lowercase       :      int						,				__lowercase       :      List[Any] ):


        '''simple docstring'''




        super().__init__()

        # make sure scheduler can always be converted to DDIM
        __UpperCAmelCase    :						Optional[int]       =						DDIMScheduler.from_config(scheduler.config )

        self.register_modules(unet=__lowercase						,				scheduler=__lowercase )

      def       A_					(							self       :      Dict						,				__lowercase       :      List[Any] ):


        '''simple docstring'''




        if strength < 0 or strength > 1:
          raise ValueError(f'''The value of strength should in [0.0, 1.0] but is {strength}''' )

      def       A_					(							self       :      Dict						,				__lowercase       :      Any						,				__lowercase       :      Optional[int]						,				__lowercase       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[Any]       =						min(int(num_inference_steps * strength )						,				__lowercase )

        __UpperCAmelCase    :						Any       =						max(num_inference_steps - init_timestep						,				0 )
        __UpperCAmelCase    :						Tuple       =						self.scheduler.timesteps[t_start:]

        return timesteps, num_inference_steps - t_start

      def       A_					(							self       :      List[str]						,				__lowercase       :      int						,				__lowercase       :      List[Any]						,				__lowercase       :      Tuple						,				__lowercase       :      str						,				__lowercase       :      Optional[int]						,				__lowercase       :      List[str]=None ):


        '''simple docstring'''




        if not isinstance(__lowercase						,				(torch.Tensor, PIL.Image.Image, list) ):
          raise ValueError(
              f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__lowercase )}''' )

        __UpperCAmelCase    :						List[str]       =						image.to(device=__lowercase						,				dtype=__lowercase )

        if isinstance(__lowercase						,				__lowercase ) and len(__lowercase ) != batch_size:
          raise ValueError(
              f'''You have passed a list of generators of length {len(__lowercase )}, but requested an effective batch'''
              f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )

        __UpperCAmelCase    :						List[Any]       =						init_latents.shape
        __UpperCAmelCase    :						Union[str, Any]       =						randn_tensor(__lowercase						,				generator=__lowercase						,				device=__lowercase						,				dtype=__lowercase )

        # get latents
        print('''add noise to latents at timestep'''						,				__lowercase )
        __UpperCAmelCase    :						Any       =						self.scheduler.add_noise(__lowercase						,				__lowercase						,				__lowercase )
        __UpperCAmelCase    :						Optional[int]       =						init_latents

        return latents







      @torch.no_grad()
      def __call__(							self       :      Any						,				__lowercase       :      Union[torch.FloatTensor, PIL.Image.Image] = None						,				__lowercase       :      float = 0.8						,				__lowercase       :      int = 1						,				__lowercase       :      Optional[Union[torch.Generator, List[torch.Generator]]] = None						,				__lowercase       :      float = 0.0						,				__lowercase       :      int = 50						,				__lowercase       :      Optional[bool] = None						,				__lowercase       :      Optional[str] = "pil"						,				__lowercase       :      bool = True						,				):


        '''simple docstring'''




        self.check_inputs(__lowercase )

        # 2. Preprocess image
        __UpperCAmelCase    :						List[str]       =						preprocess(__lowercase )

        # 3. set timesteps
        self.scheduler.set_timesteps(__lowercase						,				device=self.device )
        __UpperCAmelCase    :						Dict       =						self.get_timesteps(__lowercase						,				__lowercase						,				self.device )
        __UpperCAmelCase    :						Tuple       =						timesteps[:1].repeat(__lowercase )

        # 4. Prepare latent variables
        __UpperCAmelCase    :						Union[str, Any]       =						self.prepare_latents(__lowercase						,				__lowercase						,				__lowercase						,				self.unet.dtype						,				self.device						,				__lowercase )
        __UpperCAmelCase    :						str       =						latents

        # 5. Denoising loop
        for t in self.progress_bar(__lowercase ):
          # 1. predict noise model_output
          __UpperCAmelCase    :						List[Any]       =						self.unet(__lowercase						,				__lowercase ).sample

          # 2. predict previous mean of image x_t-1 and add variance depending on eta
          # eta corresponds to η in paper and should be between [0, 1]
          # do x_t -> x_t-1
          __UpperCAmelCase    :						List[Any]       =						self.scheduler.step(
              __lowercase						,				__lowercase						,				__lowercase						,				eta=__lowercase						,				use_clipped_model_output=__lowercase						,				generator=__lowercase						,				).prev_sample

        __UpperCAmelCase    :						str       =						(image / 2 + 0.5).clamp(0						,				1 )
        __UpperCAmelCase    :						List[Any]       =						image.cpu().permute(0						,				2						,				3						,				1 ).numpy()
        if output_type == "pil":
          __UpperCAmelCase    :						Optional[int]       =						self.numpy_to_pil(__lowercase )

        if not return_dict:
          return (image, latent_timestep.item())

        return ImagePipelineOutput(images=__lowercase )






"""simple docstring"""





import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
  import jax
  import jax.numpy as jnp
  from flax.jax_utils import replicate
  from flax.training.common_utils import shard




@slow
@require_flax
class 							snake_case							(			unittest.TestCase ):

      '''simple docstring'''



      def       A_					(							self       :      str ):


        '''simple docstring'''




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

      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        __UpperCAmelCase       ,     __UpperCAmelCase    :						List[Any]       =						FlaxStableDiffusionPipeline.from_pretrained(
            '''stabilityai/stable-diffusion-2'''						,				revision='''bf16'''						,				dtype=jnp.bfloataa						,				)

        __UpperCAmelCase    :						Dict       =						'''A painting of a squirrel eating a burger'''
        __UpperCAmelCase    :						Any       =						jax.device_count()
        __UpperCAmelCase    :						Optional[int]       =						num_samples * [prompt]
        __UpperCAmelCase    :						Tuple       =						sd_pipe.prepare_inputs(__lowercase )

        __UpperCAmelCase    :						List[Any]       =						replicate(__lowercase )
        __UpperCAmelCase    :						Optional[Any]       =						shard(__lowercase )

        __UpperCAmelCase    :						str       =						jax.random.PRNGKey(0 )
        __UpperCAmelCase    :						Dict       =						jax.random.split(__lowercase						,				jax.device_count() )

        __UpperCAmelCase    :						Tuple       =						sd_pipe(__lowercase						,				__lowercase						,				__lowercase						,				num_inference_steps=25						,				jit=__lowercase )[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

        __UpperCAmelCase    :						Any       =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
        __UpperCAmelCase    :						Union[str, Any]       =						images[0, 253:256, 253:256, -1]

        __UpperCAmelCase    :						str       =						jnp.asarray(jax.device_get(image_slice.flatten() ) )
        __UpperCAmelCase    :						List[Any]       =						jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2] )
        print(f'''output_slice: {output_slice}''' )
        assert jnp.abs(output_slice - expected_slice ).max() < 1e-2







      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						'''stabilityai/stable-diffusion-2'''
        __UpperCAmelCase       ,     __UpperCAmelCase    :						Any       =						FlaxDPMSolverMultistepScheduler.from_pretrained(__lowercase						,				subfolder='''scheduler''' )
        __UpperCAmelCase       ,     __UpperCAmelCase    :						Optional[int]       =						FlaxStableDiffusionPipeline.from_pretrained(
            __lowercase						,				scheduler=__lowercase						,				revision='''bf16'''						,				dtype=jnp.bfloataa						,				)
        __UpperCAmelCase    :						Dict       =						scheduler_params

        __UpperCAmelCase    :						int       =						'''A painting of a squirrel eating a burger'''
        __UpperCAmelCase    :						List[Any]       =						jax.device_count()
        __UpperCAmelCase    :						Optional[int]       =						num_samples * [prompt]
        __UpperCAmelCase    :						Tuple       =						sd_pipe.prepare_inputs(__lowercase )

        __UpperCAmelCase    :						Optional[int]       =						replicate(__lowercase )
        __UpperCAmelCase    :						List[Any]       =						shard(__lowercase )

        __UpperCAmelCase    :						Union[str, Any]       =						jax.random.PRNGKey(0 )
        __UpperCAmelCase    :						List[Any]       =						jax.random.split(__lowercase						,				jax.device_count() )

        __UpperCAmelCase    :						List[Any]       =						sd_pipe(__lowercase						,				__lowercase						,				__lowercase						,				num_inference_steps=25						,				jit=__lowercase )[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

        __UpperCAmelCase    :						Tuple       =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
        __UpperCAmelCase    :						Any       =						images[0, 253:256, 253:256, -1]

        __UpperCAmelCase    :						List[str]       =						jnp.asarray(jax.device_get(image_slice.flatten() ) )
        __UpperCAmelCase    :						Any       =						jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7] )
        print(f'''output_slice: {output_slice}''' )
        assert jnp.abs(output_slice - expected_slice ).max() < 1e-2