






'''simple docstring'''

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

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):




       def __init__(					self      : Tuple	,    A__      : TransformeraDModel	,    A__      : AutoencoderKL	,    A__      : KarrasDiffusionSchedulers	,    A__      : Optional[Dict[int, str]] = None	,    ):


            """simple docstring"""







            super().__init__()
            self.register_modules(transformer=A__	,    vae=A__	,    scheduler=A__					)

            # create a imagenet -> id dictionary for easier use
            __lowerCamelCase						:       List[str]				   =				{}
            if idalabel is not None:
                 for key, value in idalabel.items():
                      for label in value.split(""","""					):
                           __lowerCamelCase						:       str				   =				int(A__					)
                 __lowerCamelCase						:       Tuple				   =				dict(sorted(self.labels.items()					)					)




       def 					a_					(					self      : Any	,    A__      : Union[str, List[str]]					):


            """simple docstring"""







            if not isinstance(A__	,    A__					):
                 __lowerCamelCase						:       Tuple				   =				list(A__					)

            for l in label:
                 if l not in self.labels:
                      raise ValueError(
                          f"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."					)

            return [self.labels[l] for l in label]




       @torch.no_grad()
       def __call__(					self      : Tuple	,    A__      : List[int]	,    A__      : float = 4.0	,    A__      : Optional[Union[torch.Generator, List[torch.Generator]]] = None	,    A__      : int = 50	,    A__      : Optional[str] = "pil"	,    A__      : bool = True	,    ):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				len(A__					)
            __lowerCamelCase						:       List[str]				   =				self.transformer.config.sample_size
            __lowerCamelCase						:       List[Any]				   =				self.transformer.config.in_channels

            __lowerCamelCase						:       Any				   =				randn_tensor(
                shape=(batch_size, latent_channels, latent_size, latent_size)	,    generator=A__	,    device=self.device	,    dtype=self.transformer.dtype	,    )
            __lowerCamelCase						:       int				   =				torch.cat([latents] * 2					) if guidance_scale > 1 else latents

            __lowerCamelCase						:       Optional[int]				   =				torch.tensor(A__	,    device=self.device					).reshape(-1					)
            __lowerCamelCase						:       Optional[Any]				   =				torch.tensor([1000] * batch_size	,    device=self.device					)
            __lowerCamelCase						:       str				   =				torch.cat([class_labels, class_null]	,    0					) if guidance_scale > 1 else class_labels

            # set step values
            self.scheduler.set_timesteps(A__					)

            for t in self.progress_bar(self.scheduler.timesteps					):
                 if guidance_scale > 1:
                      __lowerCamelCase						:       Any				   =				latent_model_input[: len(A__					) // 2]
                      __lowerCamelCase						:       str				   =				torch.cat([half, half]	,    dim=0					)
                 __lowerCamelCase						:       List[Any]				   =				self.scheduler.scale_model_input(A__	,    A__					)

                 __lowerCamelCase						:       List[str]				   =				t
                 if not torch.is_tensor(A__					):
                      # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                      # This would be a good case for the `match` statement (Python 3.10+)
                      __lowerCamelCase						:       Optional[Any]				   =				latent_model_input.device.type == """mps"""
                      if isinstance(A__	,    A__					):
                           __lowerCamelCase						:       Union[str, Any]				   =				torch.floataa if is_mps else torch.floataa
                      else:
                           __lowerCamelCase						:       str				   =				torch.intaa if is_mps else torch.intaa
                      __lowerCamelCase						:       List[Any]				   =				torch.tensor([timesteps]	,    dtype=A__	,    device=latent_model_input.device					)
                 elif len(timesteps.shape					) == 0:
                      __lowerCamelCase						:       List[str]				   =				timesteps[None].to(latent_model_input.device					)
                 # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                 __lowerCamelCase						:       Optional[int]				   =				timesteps.expand(latent_model_input.shape[0]					)
                 # predict noise model_output
                 __lowerCamelCase						:       Union[str, Any]				   =				self.transformer(
                     A__	,    timestep=A__	,    class_labels=A__					).sample

                 # perform guidance
                 if guidance_scale > 1:
                      __lowerCamelCase						:       Any				   =				noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                      __lowerCamelCase						:       Tuple				   =				torch.split(A__	,    len(A__					) // 2	,    dim=0					)

                      __lowerCamelCase						:       List[Any]				   =				uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                      __lowerCamelCase						:       Any				   =				torch.cat([half_eps, half_eps]	,    dim=0					)

                      __lowerCamelCase						:       List[str]				   =				torch.cat([eps, rest]	,    dim=1					)

                 # learned sigma
                 if self.transformer.config.out_channels // 2 == latent_channels:
                      __lowerCamelCase						:       str				   =				torch.split(A__	,    A__	,    dim=1					)
                 else:
                      __lowerCamelCase						:       Optional[int]				   =				noise_pred

                 # compute previous image: x_t -> x_t-1
                 __lowerCamelCase						:       int				   =				self.scheduler.step(A__	,    A__	,    A__					).prev_sample

            if guidance_scale > 1:
                 __lowerCamelCase						:       List[Any]				   =				latent_model_input.chunk(2	,    dim=0					)
            else:
                 __lowerCamelCase						:       int				   =				latent_model_input

            __lowerCamelCase						:       Union[str, Any]				   =				1 / self.vae.config.scaling_factor * latents
            __lowerCamelCase						:       str				   =				self.vae.decode(A__					).sample

            __lowerCamelCase						:       Tuple				   =				(samples / 2 + 0.5).clamp(0	,    1					)

            # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
            __lowerCamelCase						:       Tuple				   =				samples.cpu().permute(0	,    2	,    3	,    1					).float().numpy()

            if output_type == "pil":
                 __lowerCamelCase						:       List[str]				   =				self.numpy_to_pil(A__					)

            if not return_dict:
                 return (samples,)

            return ImagePipelineOutput(images=A__					)











'''simple docstring'''

import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







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




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


            """simple docstring"""







            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()




       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				StableDiffusionKDiffusionPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""					)
            __lowerCamelCase						:       Tuple				   =				sd_pipe.to(A__					)
            sd_pipe.set_progress_bar_config(disable=A__					)

            sd_pipe.set_scheduler("""sample_euler"""					)

            __lowerCamelCase						:       Any				   =				"""A painting of a squirrel eating a burger"""
            __lowerCamelCase						:       List[Any]				   =				torch.manual_seed(0					)
            __lowerCamelCase						:       Dict				   =				sd_pipe([prompt]	,    generator=A__	,    guidance_scale=9.0	,    num_inference_steps=20	,    output_type="""np"""					)

            __lowerCamelCase						:       Tuple				   =				output.images

            __lowerCamelCase						:       int				   =				image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            __lowerCamelCase						:       List[str]				   =				np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				StableDiffusionKDiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""					)
            __lowerCamelCase						:       int				   =				sd_pipe.to(A__					)
            sd_pipe.set_progress_bar_config(disable=A__					)

            sd_pipe.set_scheduler("""sample_euler"""					)

            __lowerCamelCase						:       List[Any]				   =				"""A painting of a squirrel eating a burger"""
            __lowerCamelCase						:       Union[str, Any]				   =				torch.manual_seed(0					)
            __lowerCamelCase						:       int				   =				sd_pipe([prompt]	,    generator=A__	,    guidance_scale=9.0	,    num_inference_steps=20	,    output_type="""np"""					)

            __lowerCamelCase						:       List[str]				   =				output.images

            __lowerCamelCase						:       Optional[int]				   =				image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            __lowerCamelCase						:       Optional[int]				   =				np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 5e-1




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				StableDiffusionKDiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""					)
            __lowerCamelCase						:       List[str]				   =				sd_pipe.to(A__					)
            sd_pipe.set_progress_bar_config(disable=A__					)

            sd_pipe.set_scheduler("""sample_dpmpp_2m"""					)

            __lowerCamelCase						:       int				   =				"""A painting of a squirrel eating a burger"""
            __lowerCamelCase						:       Tuple				   =				torch.manual_seed(0					)
            __lowerCamelCase						:       Union[str, Any]				   =				sd_pipe(
                [prompt]	,    generator=A__	,    guidance_scale=7.5	,    num_inference_steps=15	,    output_type="""np"""	,    use_karras_sigmas=A__	,    )

            __lowerCamelCase						:       int				   =				output.images

            __lowerCamelCase						:       Any				   =				image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            __lowerCamelCase						:       int				   =				np.array(
                [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2



