
"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 					UpperCAmelCase_     (   _a			,							_a			,							_a			,							unittest.TestCase):
      lowerCamelCase__		:   Any			  =  StableDiffusionImageVariationPipeline
      lowerCamelCase__		:   Tuple			  =  IMAGE_VARIATION_PARAMS
      lowerCamelCase__		:   Optional[Any]			  =  IMAGE_VARIATION_BATCH_PARAMS
      lowerCamelCase__		:   Union[str, Any]			  =  frozenset([])
      # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
      lowerCamelCase__		:   Optional[Any]			  =  frozenset([])


      def 		_UpperCAmelCase	(       self							)	-> Dict:
             torch.manual_seed(0							)
             lowercase__      :		Dict          =     UNetaDConditionModel(
                 block_out_channels=(3_2, 6_4)		,      layers_per_block=2		,      sample_size=3_2		,      in_channels=4		,      out_channels=4		,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')		,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')		,      cross_attention_dim=3_2		,      )
             lowercase__      :		Dict          =     PNDMScheduler(skip_prk_steps=a							)
             torch.manual_seed(0							)
             lowercase__      :		Optional[int]          =     AutoencoderKL(
                 block_out_channels=[3_2, 6_4]		,      in_channels=3		,      out_channels=3		,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,      latent_channels=4		,      )
             torch.manual_seed(0							)
             lowercase__      :		str          =     CLIPVisionConfig(
                 hidden_size=3_2		,      projection_dim=3_2		,      intermediate_size=3_7		,      layer_norm_eps=1e-05		,      num_attention_heads=4		,      num_hidden_layers=5		,      image_size=3_2		,      patch_size=4		,      )
             lowercase__      :		int          =     CLIPVisionModelWithProjection(a							)
             lowercase__      :		Optional[Any]          =     CLIPImageProcessor(crop_size=3_2		,      size=3_2							)

             lowercase__      :		Dict          =     {
                 'unet': unet,
                 'scheduler': scheduler,
                 'vae': vae,
                 'image_encoder': image_encoder,
                 'feature_extractor': feature_extractor,
                 'safety_checker': None,
             }
             return components


      def 		_UpperCAmelCase	(       self		,      a		,      a=0							)	-> Optional[Any]:
             lowercase__      :		int          =     floats_tensor((1, 3, 3_2, 3_2)		,      rng=random.Random(a							)							)
             lowercase__      :		Optional[Any]          =     image.cpu().permute(0		,      2		,      3		,      1							)[0]
             lowercase__      :		int          =     Image.fromarray(np.uinta(a							)							).convert('RGB'							).resize((3_2, 3_2)							)
             if str(a							).startswith('mps'							):
                    lowercase__      :		Union[str, Any]          =     torch.manual_seed(a							)
             else:
                    lowercase__      :		Union[str, Any]          =     torch.Generator(device=a							).manual_seed(a							)
             lowercase__      :		Union[str, Any]          =     {
                 'image': image,
                 'generator': generator,
                 'num_inference_steps': 2,
                 'guidance_scale': 6.0,
                 'output_type': 'numpy',
             }
             return inputs


      def 		_UpperCAmelCase	(       self							)	-> List[Any]:
             lowercase__      :		Union[str, Any]          =     'cpu'  # ensure determinism for the device-dependent torch.Generator
             lowercase__      :		int          =     self.get_dummy_components()
             lowercase__      :		str          =     StableDiffusionImageVariationPipeline(**a							)
             lowercase__      :		Optional[Any]          =     sd_pipe.to(a							)
             sd_pipe.set_progress_bar_config(disable=a							)

             lowercase__      :		Dict          =     self.get_dummy_inputs(a							)
             lowercase__      :		List[str]          =     sd_pipe(**a							).images
             lowercase__      :		Union[str, Any]          =     image[0, -3:, -3:, -1]

             assert image.shape == (1, 6_4, 6_4, 3)
             lowercase__      :		Union[str, Any]          =     np.array([0.5_239, 0.5_723, 0.4_796, 0.5_049, 0.5_550, 0.4_685, 0.5_329, 0.4_891, 0.4_921]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3


      def 		_UpperCAmelCase	(       self							)	-> List[Any]:
             lowercase__      :		int          =     'cpu'  # ensure determinism for the device-dependent torch.Generator
             lowercase__      :		str          =     self.get_dummy_components()
             lowercase__      :		Optional[int]          =     StableDiffusionImageVariationPipeline(**a							)
             lowercase__      :		Tuple          =     sd_pipe.to(a							)
             sd_pipe.set_progress_bar_config(disable=a							)

             lowercase__      :		Optional[int]          =     self.get_dummy_inputs(a							)
             lowercase__      :		Optional[int]          =     2 * [inputs['image']]
             lowercase__      :		Optional[Any]          =     sd_pipe(**a							)

             lowercase__      :		Optional[int]          =     output.images

             lowercase__      :		int          =     image[-1, -3:, -3:, -1]

             assert image.shape == (2, 6_4, 6_4, 3)
             lowercase__      :		str          =     np.array([0.6_892, 0.5_637, 0.5_836, 0.5_771, 0.6_254, 0.6_409, 0.5_580, 0.5_569, 0.5_289]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3


      def 		_UpperCAmelCase	(       self							)	-> Union[str, Any]:
             super().test_inference_batch_single_identical(expected_max_diff=3e-3							)





@slow
@require_torch_gpu
class 					UpperCAmelCase_     (   unittest.TestCase):


      def 		_UpperCAmelCase	(       self							)	-> Optional[Any]:
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()


      def 		_UpperCAmelCase	(       self		,      a		,      a="cpu"		,      a=torch.floataa		,      a=0							)	-> int:
             lowercase__      :		List[str]          =     torch.Generator(device=a							).manual_seed(a							)
             lowercase__      :		Tuple          =     load_image(
                 'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
                 '/stable_diffusion_imgvar/input_image_vermeer.png'							)
             lowercase__      :		Union[str, Any]          =     np.random.RandomState(a							).standard_normal((1, 4, 6_4, 6_4)							)
             lowercase__      :		Any          =     torch.from_numpy(a							).to(device=a		,      dtype=a							)
             lowercase__      :		Any          =     {
                 'image': init_image,
                 'latents': latents,
                 'generator': generator,
                 'num_inference_steps': 3,
                 'guidance_scale': 7.5,
                 'output_type': 'numpy',
             }
             return inputs


      def 		_UpperCAmelCase	(       self							)	-> List[str]:
             lowercase__      :		int          =     StableDiffusionImageVariationPipeline.from_pretrained(
                 'lambdalabs/sd-image-variations-diffusers'		,      safety_checker=a							)
             lowercase__      :		List[str]          =     sd_pipe.to(a							)
             sd_pipe.set_progress_bar_config(disable=a							)

             lowercase__      :		Tuple          =     self.get_inputs(a							)
             lowercase__      :		List[Any]          =     sd_pipe(**a							).images
             lowercase__      :		List[str]          =     image[0, -3:, -3:, -1].flatten()

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             lowercase__      :		List[str]          =     np.array([0.84_491, 0.90_789, 0.75_708, 0.78_734, 0.83_485, 0.70_099, 0.66_938, 0.68_727, 0.61_379]							)
             assert np.abs(image_slice - expected_slice							).max() < 6e-3


      def 		_UpperCAmelCase	(       self							)	-> Optional[Any]:
             lowercase__      :		str          =     0

             def callback_fn(a		,      a		,      a							) -> None:
                    lowercase__      :		Any          =     True
                    nonlocal number_of_steps
                    number_of_steps += 1
                    if step == 1:
                           lowercase__      :		str          =     latents.detach().cpu().numpy()
                           assert latents.shape == (1, 4, 6_4, 6_4)
                           lowercase__      :		Tuple          =     latents[0, -3:, -3:, -1]
                           lowercase__      :		Union[str, Any]          =     np.array(
                               [-0.1_621, 0.2_837, -0.7_979, -0.1_221, -1.3_057, 0.7_681, -2.1_191, 0.0_464, 1.6_309]							)

                           assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5e-2
                    elif step == 2:
                           lowercase__      :		Optional[Any]          =     latents.detach().cpu().numpy()
                           assert latents.shape == (1, 4, 6_4, 6_4)
                           lowercase__      :		str          =     latents[0, -3:, -3:, -1]
                           lowercase__      :		int          =     np.array([0.6_299, 1.7_500, 1.1_992, -2.1_582, -1.8_994, 0.7_334, -0.7_090, 1.0_137, 1.5_273]							)

                           assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5e-2

             lowercase__      :		Dict          =     False

             lowercase__      :		Tuple          =     StableDiffusionImageVariationPipeline.from_pretrained(
                 'fusing/sd-image-variations-diffusers'		,      safety_checker=a		,      torch_dtype=torch.floataa		,      )
             pipe.to(a							)
             pipe.set_progress_bar_config(disable=a							)
             pipe.enable_attention_slicing()

             lowercase__      :		Any          =     self.get_inputs(a		,      dtype=torch.floataa							)
             pipe(**a		,      callback=a		,      callback_steps=1							)
             assert callback_fn.has_been_called
             assert number_of_steps == inputs["num_inference_steps"]


      def 		_UpperCAmelCase	(       self							)	-> Union[str, Any]:
             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()
             torch.cuda.reset_peak_memory_stats()

             lowercase__      :		int          =     'fusing/sd-image-variations-diffusers'
             lowercase__      :		Union[str, Any]          =     StableDiffusionImageVariationPipeline.from_pretrained(
                 a		,      safety_checker=a		,      torch_dtype=torch.floataa							)
             lowercase__      :		List[Any]          =     pipe.to(a							)
             pipe.set_progress_bar_config(disable=a							)
             pipe.enable_attention_slicing(1							)
             pipe.enable_sequential_cpu_offload()

             lowercase__      :		Optional[int]          =     self.get_inputs(a		,      dtype=torch.floataa							)
             lowercase__      :		int          =     pipe(**a							)

             lowercase__      :		Union[str, Any]          =     torch.cuda.max_memory_allocated()
             # make sure that less than 2.6 GB is allocated
             assert mem_bytes < 2.6 * 1_0**9





@nightly
@require_torch_gpu
class 					UpperCAmelCase_     (   unittest.TestCase):


      def 		_UpperCAmelCase	(       self							)	-> List[str]:
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()


      def 		_UpperCAmelCase	(       self		,      a		,      a="cpu"		,      a=torch.floataa		,      a=0							)	-> Optional[Any]:
             lowercase__      :		Tuple          =     torch.Generator(device=a							).manual_seed(a							)
             lowercase__      :		List[str]          =     load_image(
                 'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
                 '/stable_diffusion_imgvar/input_image_vermeer.png'							)
             lowercase__      :		Dict          =     np.random.RandomState(a							).standard_normal((1, 4, 6_4, 6_4)							)
             lowercase__      :		Any          =     torch.from_numpy(a							).to(device=a		,      dtype=a							)
             lowercase__      :		Optional[int]          =     {
                 'image': init_image,
                 'latents': latents,
                 'generator': generator,
                 'num_inference_steps': 5_0,
                 'guidance_scale': 7.5,
                 'output_type': 'numpy',
             }
             return inputs


      def 		_UpperCAmelCase	(       self							)	-> int:
             lowercase__      :		Union[str, Any]          =     StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'							)
             sd_pipe.to(a							)
             sd_pipe.set_progress_bar_config(disable=a							)

             lowercase__      :		List[str]          =     self.get_inputs(a							)
             lowercase__      :		Any          =     sd_pipe(**a							).images[0]

             lowercase__      :		Optional[Any]          =     load_numpy(
                 'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
                 '/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy'							)
             lowercase__      :		Dict          =     np.abs(expected_image - image							).max()
             assert max_diff < 1e-3


      def 		_UpperCAmelCase	(       self							)	-> Dict:
             lowercase__      :		Any          =     StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'							)
             lowercase__      :		List[Any]          =     DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config							)
             sd_pipe.to(a							)
             sd_pipe.set_progress_bar_config(disable=a							)

             lowercase__      :		List[str]          =     self.get_inputs(a							)
             lowercase__      :		int          =     2_5
             lowercase__      :		Dict          =     sd_pipe(**a							).images[0]

             lowercase__      :		Optional[Any]          =     load_numpy(
                 'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
                 '/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy'							)
             lowercase__      :		List[str]          =     np.abs(expected_image - image							).max()
             assert max_diff < 1e-3


"""simple docstring"""





from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)


class 					UpperCAmelCase_     (   _a):


      def __init__(       self		,      a		,      a		,      a		,      a		,      a		,      a = 0.93		,      a = True		,      )	-> Any:
             lowercase__      :		Union[str, Any]          =     mixing_coeff
             lowercase__      :		Any          =     leapfrog_steps

             super().__init__()
             self.register_modules(
                 vae=a		,      text_encoder=a		,      tokenizer=a		,      unet=a		,      scheduler=a		,      )

             lowercase__      :		List[str]          =     2 ** (len(self.vae.config.block_out_channels							) - 1)
             lowercase__      :		Union[str, Any]          =     VaeImageProcessor(vae_scale_factor=self.vae_scale_factor							)


      def 		_UpperCAmelCase	(       self		,      a		,      a = None		,      a = False							)	-> Optional[int]:
             lowercase__      :		str          =     self.tokenizer(
                 a		,      padding='max_length'		,      max_length=self.tokenizer.model_max_length		,      truncation=a		,      return_tensors='pt'		,      )

             lowercase__      :		str          =     self.text_encoder(text_inputs.input_ids.to(self.device							)							).last_hidden_state

             lowercase__      :		Any          =     prompt_embeds.to(dtype=self.text_encoder.dtype		,      device=self.device							)

             if do_classifier_free_guidance:
                    lowercase__      :		Tuple          =     '' if negative_prompt is None else negative_prompt

                    lowercase__      :		int          =     self.tokenizer(
                        a		,      padding='max_length'		,      max_length=self.tokenizer.model_max_length		,      truncation=a		,      return_tensors='pt'		,      )

                    lowercase__      :		str          =     self.text_encoder(uncond_input.input_ids.to(self.device							)							).last_hidden_state

                    lowercase__      :		str          =     torch.cat([negative_prompt_embeds, prompt_embeds]							)

             return prompt_embeds


      def 		_UpperCAmelCase	(       self		,      a		,      a							)	-> Optional[Any]:
             lowercase__      :		int          =     self.mixing_coeff * x + (1 - self.mixing_coeff) * y
             lowercase__      :		Tuple          =     self.mixing_coeff * y + (1 - self.mixing_coeff) * x

             return [x, y]


      def 		_UpperCAmelCase	(       self		,      a		,      a							)	-> List[str]:
             lowercase__      :		str          =     (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
             lowercase__      :		Union[str, Any]          =     (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

             return [x, y]


      def 		_UpperCAmelCase	(       self		,      a							)	-> List[str]:
             # as self.alphas_cumprod is always in cpu
             lowercase__      :		Any          =     int(a							)

             lowercase__      :		Any          =     self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

             return alpha_prod, 1 - alpha_prod


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a		,      )	-> int:
             lowercase__      :		List[Any]          =     timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

             lowercase__   ,				lowercase__      :		Optional[Any]          =     self._get_alpha_and_beta(a							)
             lowercase__   ,				lowercase__      :		str          =     self._get_alpha_and_beta(a							)

             lowercase__      :		Tuple          =     (alpha_prod_t_prev / alpha_prod_t) ** 0.5
             lowercase__      :		Optional[int]          =     -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

             lowercase__      :		int          =     (base - b_t * model_output) / a_t

             return model_input, next_model_input.to(base.dtype							)


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a		,      )	-> Tuple:
             lowercase__      :		Optional[int]          =     timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

             lowercase__   ,				lowercase__      :		Optional[int]          =     self._get_alpha_and_beta(a							)
             lowercase__   ,				lowercase__      :		List[Any]          =     self._get_alpha_and_beta(a							)

             lowercase__      :		Union[str, Any]          =     (alpha_prod_t_prev / alpha_prod_t) ** 0.5
             lowercase__      :		Optional[Any]          =     -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
             lowercase__      :		List[str]          =     a_t * base + b_t * model_output

             return model_input, next_model_input.to(base.dtype							)


      @torch.no_grad()
      def 		_UpperCAmelCase	(       self		,      a							)	-> Optional[Any]:
             lowercase__      :		Union[str, Any]          =     1 / self.vae.config.scaling_factor * latents
             lowercase__      :		Union[str, Any]          =     self.vae.decode(a							).sample
             lowercase__      :		int          =     (image / 2 + 0.5).clamp(0		,      1							)
             return image


      @torch.no_grad()
      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a		,      a = None		,      )	-> Dict:
             lowercase__      :		List[str]          =     guidance_scale > 1.0

             lowercase__      :		Union[str, Any]          =     image.to(device=self.device		,      dtype=text_embeds.dtype							)
             lowercase__      :		Tuple          =     self.vae.encode(a							).latent_dist.sample(a							)

             lowercase__      :		Optional[Any]          =     self.vae.config.scaling_factor * latent

             lowercase__      :		Tuple          =     [latent.clone(), latent.clone()]

             for i, t in tqdm(enumerate(a							)		,      total=len(a							)							):
                    lowercase__      :		Tuple          =     self.noise_mixing_layer(x=coupled_latents[0]		,      y=coupled_latents[1]							)

                    # j - model_input index, k - base index
                    for j in range(2							):
                           lowercase__      :		Any          =     j ^ 1

                           if self.leapfrog_steps:
                                  if i % 2 == 0:
                                         lowercase__   ,				lowercase__      :		Tuple          =     j, k

                           lowercase__      :		Union[str, Any]          =     coupled_latents[j]
                           lowercase__      :		Tuple          =     coupled_latents[k]

                           lowercase__      :		Dict          =     torch.cat([model_input] * 2							) if do_classifier_free_guidance else model_input

                           lowercase__      :		Union[str, Any]          =     self.unet(a		,      a		,      encoder_hidden_states=a							).sample

                           if do_classifier_free_guidance:
                                  lowercase__   ,				lowercase__      :		Any          =     noise_pred.chunk(2							)
                                  lowercase__      :		Any          =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                           lowercase__   ,				lowercase__      :		Optional[Any]          =     self.noise_step(
                               base=a		,      model_input=a		,      model_output=a		,      timestep=a		,      )

                           lowercase__      :		str          =     model_input

             return coupled_latents


      @torch.no_grad()
      def __call__(       self		,      a		,      a		,      a		,      a = 3.0		,      a = 5_0		,      a = 0.8		,      a = None		,      a = None		,      a = "pil"		,      )	-> Dict:
             lowercase__      :		List[Any]          =     guidance_scale > 1.0

             lowercase__      :		List[str]          =     self.image_processor.preprocess(a							)

             lowercase__      :		Union[str, Any]          =     self._encode_prompt(a		,      a		,      a							)
             lowercase__      :		int          =     self._encode_prompt(a		,      a		,      a							)

             self.scheduler.set_timesteps(a		,      self.device							)

             lowercase__      :		Dict          =     num_inference_steps - int(num_inference_steps * strength							)
             lowercase__      :		str          =     self.scheduler.timesteps[t_limit:]
             lowercase__      :		int          =     fwd_timesteps.flip(0							)

             lowercase__      :		int          =     self.prepare_latents(a		,      a		,      a		,      a		,      a							)

             for i, t in tqdm(enumerate(a							)		,      total=len(a							)							):
                    # j - model_input index, k - base index
                    for k in range(2							):
                           lowercase__      :		Optional[Any]          =     k ^ 1

                           if self.leapfrog_steps:
                                  if i % 2 == 1:
                                         lowercase__   ,				lowercase__      :		int          =     j, k

                           lowercase__      :		Optional[int]          =     coupled_latents[j]
                           lowercase__      :		int          =     coupled_latents[k]

                           lowercase__      :		List[Any]          =     torch.cat([model_input] * 2							) if do_classifier_free_guidance else model_input

                           lowercase__      :		List[str]          =     self.unet(a		,      a		,      encoder_hidden_states=a							).sample

                           if do_classifier_free_guidance:
                                  lowercase__   ,				lowercase__      :		int          =     noise_pred.chunk(2							)
                                  lowercase__      :		Any          =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                           lowercase__   ,				lowercase__      :		List[str]          =     self.denoise_step(
                               base=a		,      model_input=a		,      model_output=a		,      timestep=a		,      )

                           lowercase__      :		List[str]          =     model_input

                    lowercase__      :		Union[str, Any]          =     self.denoise_mixing_layer(x=coupled_latents[0]		,      y=coupled_latents[1]							)

             # either one is fine
             lowercase__      :		Optional[Any]          =     coupled_latents[0]

             if output_type not in ["latent", "pt", "np", "pil"]:
                    lowercase__      :		Any          =     (
                        f"""the output_type {output_type} is outdated. Please make sure to set it to one of these instead: """
                        '`pil`, `np`, `pt`, `latent`'
                    )
                    deprecate('Unsupported output_type'		,      '1.0.0'		,      a		,      standard_warn=a							)
                    lowercase__      :		str          =     'np'

             if output_type == "latent":
                    lowercase__      :		List[str]          =     final_latent
             else:
                    lowercase__      :		Optional[int]          =     self.decode_latents(a							)
                    lowercase__      :		Optional[int]          =     self.image_processor.postprocess(a		,      output_type=a							)

             return image

