


'''simple docstring'''







from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


A				:					List[str]    						=			logging.get_logger(__name__)  # pylint: disable=invalid-name

A				:					int    						=			'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      List[str]  ,lowerCamelCase					:      Optional[Any]=8       ):
       _A					:     Union[str, Any]     = height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       _A					:     Optional[int]     = width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[str]  ,lowerCamelCase					:      Dict=512  ,lowerCamelCase					:      int=512       ):
       _A					:     Any     = pil_image.resize((w, h)  ,resample=Image.BICUBIC  ,reducing_gap=1       )
       _A					:     Optional[Any]     = np.array(pil_image.convert('RGB'       )       )
       _A					:     Dict     = arr.astype(np.floataa       ) / 127.5 - 1
       _A					:     str     = np.transpose(lowerCAmelCase__  ,[2, 0, 1]       )
       _A					:     Optional[Any]     = torch.from_numpy(lowerCAmelCase__       ).unsqueeze(0       )
       return image


class 			__lowerCamelCase			(	_lowerCAmelCase	):
    """simple docstring"""


    def __init__(  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							):
           super().__init__()

           self.register_modules(
               unet=SCREAMING_SNAKE_CASE_				,							scheduler=SCREAMING_SNAKE_CASE_				,							movq=SCREAMING_SNAKE_CASE_				,							)
           _A					:     List[Any]     = 2 ** (len(self.movq.config.block_out_channels) - 1)


    def        A   (  self      :							str				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							Optional[int]):
           # get the original timestep using init_timestep
           _A					:     str     = min(int(num_inference_steps * strength)				,							SCREAMING_SNAKE_CASE_)

           _A					:     List[str]     = max(num_inference_steps - init_timestep				,							0)
           _A					:     List[Any]     = self.scheduler.timesteps[t_start:]

           return timesteps, num_inference_steps - t_start


    def        A   (  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							Dict=None):
           if not isinstance(SCREAMING_SNAKE_CASE_				,							(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(SCREAMING_SNAKE_CASE_)}')

           _A					:     str     = image.to(device=SCREAMING_SNAKE_CASE_				,							dtype=SCREAMING_SNAKE_CASE_)

           _A					:     List[Any]     = batch_size * num_images_per_prompt

           if image.shape[1] == 4:
                  _A					:     Optional[int]     = image

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

                  elif isinstance(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_):
                         _A					:     List[Any]     = [
                             self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(SCREAMING_SNAKE_CASE_)
                         ]
                         _A					:     Union[str, Any]     = torch.cat(SCREAMING_SNAKE_CASE_				,							dim=0)
                  else:
                         _A					:     Any     = self.movq.encode(SCREAMING_SNAKE_CASE_).latent_dist.sample(SCREAMING_SNAKE_CASE_)

                  _A					:     Dict     = self.movq.config.scaling_factor * init_latents

           _A					:     List[str]     = torch.cat([init_latents]				,							dim=0)

           _A					:     Any     = init_latents.shape
           _A					:     int     = randn_tensor(SCREAMING_SNAKE_CASE_				,							generator=SCREAMING_SNAKE_CASE_				,							device=SCREAMING_SNAKE_CASE_				,							dtype=SCREAMING_SNAKE_CASE_)

           # get latents
           _A					:     Optional[Any]     = self.scheduler.add_noise(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_)

           _A					:     str     = init_latents

           return latents


    def        A   (  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Any=0):
           if is_accelerate_available():
                  from accelerate import cpu_offload
           else:
                  raise ImportError('Please install accelerate via `pip install accelerate`')

           _A					:     Any     = torch.device(F'cuda:{gpu_id}')

           _A					:     List[Any]     = [
               self.unet,
               self.movq,
           ]
           for cpu_offloaded_model in models:
                  if cpu_offloaded_model is not None:
                         cpu_offload(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_)


    def        A   (  self      :							str				,							SCREAMING_SNAKE_CASE      :							Tuple=0):
           if is_accelerate_available() and is_accelerate_version('>='				,							'0.17.0.dev0'):
                  from accelerate import cpu_offload_with_hook
           else:
                  raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.')

           _A					:     int     = torch.device(F'cuda:{gpu_id}')

           if self.device.type != "cpu":
                  self.to('cpu'				,							silence_dtype_warnings=SCREAMING_SNAKE_CASE_)
                  torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

           _A					:     Any     = None
           for cpu_offloaded_model in [self.unet, self.movq]:
                  _A					:     Dict     = cpu_offload_with_hook(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							prev_module_hook=SCREAMING_SNAKE_CASE_)

           # We'll offload the last model manually.
           _A					:     Dict     = hook


    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def        A   (  self      :							int):
           if not hasattr(self.unet				,							'_hf_hook'):
                  return self.device
           for module in self.unet.modules():
                  if (
                      hasattr(SCREAMING_SNAKE_CASE_				,							'_hf_hook')
                      and hasattr(module._hf_hook				,							'execution_device')
                      and module._hf_hook.execution_device is not None
                  ):
                         return torch.device(module._hf_hook.execution_device)
           return self.device


    @torch.no_grad()
    @replace_example_docstring(SCREAMING_SNAKE_CASE_)
    def __call__(  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							Dict = 512				,							SCREAMING_SNAKE_CASE      :							str = 512				,							SCREAMING_SNAKE_CASE      :							Union[str, Any] = 100				,							SCREAMING_SNAKE_CASE      :							Union[str, Any] = 4.0				,							SCREAMING_SNAKE_CASE      :							List[str] = 0.3				,							SCREAMING_SNAKE_CASE      :							Union[str, Any] = 1				,							SCREAMING_SNAKE_CASE      :							Optional[int] = None				,							SCREAMING_SNAKE_CASE      :							Optional[int] = "pil"				,							SCREAMING_SNAKE_CASE      :							Dict = True				,							):
           _A					:     int     = self._execution_device

           _A					:     Any     = guidance_scale > 1.0

           if isinstance(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_):
                  _A					:     Optional[Any]     = torch.cat(SCREAMING_SNAKE_CASE_				,							dim=0)
           _A					:     int     = image_embeds.shape[0]
           if isinstance(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_):
                  _A					:     Dict     = torch.cat(SCREAMING_SNAKE_CASE_				,							dim=0)

           if do_classifier_free_guidance:
                  _A					:     int     = image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_				,							dim=0)
                  _A					:     Tuple     = negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_				,							dim=0)

           _A					:     Optional[Any]     = torch.cat([negative_image_embeds, image_embeds]				,							dim=0).to(dtype=self.unet.dtype				,							device=SCREAMING_SNAKE_CASE_)

           if not isinstance(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_):
                  _A					:     str     = [image]
           if not all(isinstance(SCREAMING_SNAKE_CASE_				,							(PIL.Image.Image, torch.Tensor)) for i in image):
                  raise ValueError(
                      F'Input is in incorrect format: {[type(SCREAMING_SNAKE_CASE_) for i in image]}. Currently, we only support  PIL image and pytorch tensor')

           _A					:     Optional[int]     = torch.cat([prepare_image(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_) for i in image]				,							dim=0)
           _A					:     List[Any]     = image.to(dtype=image_embeds.dtype				,							device=SCREAMING_SNAKE_CASE_)

           _A					:     Any     = self.movq.encode(SCREAMING_SNAKE_CASE_)["""latents"""]
           _A					:     List[Any]     = latents.repeat_interleave(SCREAMING_SNAKE_CASE_				,							dim=0)
           self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_				,							device=SCREAMING_SNAKE_CASE_)
           _A					:     str     = self.get_timesteps(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_)
           _A					:     Union[str, Any]     = timesteps[:1].repeat(batch_size * num_images_per_prompt)
           _A					:     Any     = downscale_height_and_width(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							self.movq_scale_factor)
           _A					:     int     = self.prepare_latents(
               SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							image_embeds.dtype				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_)
           for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_)):
                  # expand the latents if we are doing classifier free guidance
                  _A					:     str     = torch.cat([latents] * 2) if do_classifier_free_guidance else latents

                  _A					:     int     = {"""image_embeds""": image_embeds}
                  _A					:     Union[str, Any]     = self.unet(
                      sample=SCREAMING_SNAKE_CASE_				,							timestep=SCREAMING_SNAKE_CASE_				,							encoder_hidden_states=SCREAMING_SNAKE_CASE_				,							added_cond_kwargs=SCREAMING_SNAKE_CASE_				,							return_dict=SCREAMING_SNAKE_CASE_				,							)[0]

                  if do_classifier_free_guidance:
                         _A					:     Any     = noise_pred.split(latents.shape[1]				,							dim=1)
                         _A					:     List[str]     = noise_pred.chunk(2)
                         _A					:     List[str]     = variance_pred.chunk(2)
                         _A					:     Optional[Any]     = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                         _A					:     int     = torch.cat([noise_pred, variance_pred_text]				,							dim=1)

                  if not (
                      hasattr(self.scheduler.config				,							'variance_type')
                      and self.scheduler.config.variance_type in ["learned", "learned_range"]
                  ):
                         _A					:     Any     = noise_pred.split(latents.shape[1]				,							dim=1)

                  # compute the previous noisy sample x_t -> x_t-1
                  _A					:     Optional[int]     = self.scheduler.step(
                      SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							generator=SCREAMING_SNAKE_CASE_				,							)[0]

           # post-processing
           _A					:     List[Any]     = self.movq.decode(SCREAMING_SNAKE_CASE_				,							force_not_quantize=SCREAMING_SNAKE_CASE_)["""sample"""]

           if output_type not in ["pt", "np", "pil"]:
                  raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}')

           if output_type in ["np", "pil"]:
                  _A					:     Tuple     = image * 0.5 + 0.5
                  _A					:     Dict     = image.clamp(0				,							1)
                  _A					:     Union[str, Any]     = image.cpu().permute(0				,							2				,							3				,							1).float().numpy()

           if output_type == "pil":
                  _A					:     List[Any]     = self.numpy_to_pil(SCREAMING_SNAKE_CASE_)

           if not return_dict:
                  return (image,)

           return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_)



'''simple docstring'''







# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()


class 			__lowerCamelCase			(	a_    , a_    , a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   StableDiffusionControlNetImgaImgPipeline
    a													=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
    a													=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    a													=   IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"}	)
    a													=   IMAGE_TO_IMAGE_IMAGE_PARAMS


    def        A   (  self      :							Tuple):
           torch.manual_seed(0)
           _A					:     List[str]     = UNetaDConditionModel(
               block_out_channels=(32, 64)				,							layers_per_block=2				,							sample_size=32				,							in_channels=4				,							out_channels=4				,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')				,							cross_attention_dim=32				,							)
           torch.manual_seed(0)
           _A					:     Optional[Any]     = ControlNetModel(
               block_out_channels=(32, 64)				,							layers_per_block=2				,							in_channels=4				,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,							cross_attention_dim=32				,							conditioning_embedding_out_channels=(16, 32)				,							)
           torch.manual_seed(0)
           _A					:     Any     = DDIMScheduler(
               beta_start=0.0_0085				,							beta_end=0.012				,							beta_schedule='scaled_linear'				,							clip_sample=SCREAMING_SNAKE_CASE				,							set_alpha_to_one=SCREAMING_SNAKE_CASE				,							)
           torch.manual_seed(0)
           _A					:     List[str]     = AutoencoderKL(
               block_out_channels=[32, 64]				,							in_channels=3				,							out_channels=3				,							down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']				,							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']				,							latent_channels=4				,							)
           torch.manual_seed(0)
           _A					:     List[str]     = CLIPTextConfig(
               bos_token_id=0				,							eos_token_id=2				,							hidden_size=32				,							intermediate_size=37				,							layer_norm_eps=1e-05				,							num_attention_heads=4				,							num_hidden_layers=5				,							pad_token_id=1				,							vocab_size=1000				,							)
           _A					:     Any     = CLIPTextModel(SCREAMING_SNAKE_CASE)
           _A					:     List[Any]     = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

           _A					:     Tuple     = {
               'unet': unet,
               'controlnet': controlnet,
               'scheduler': scheduler,
               'vae': vae,
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'safety_checker': None,
               'feature_extractor': None,
           }
           return components


    def        A   (  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							Tuple=0):
           if str(SCREAMING_SNAKE_CASE).startswith('mps'):
                  _A					:     Optional[int]     = torch.manual_seed(SCREAMING_SNAKE_CASE)
           else:
                  _A					:     Tuple     = torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(SCREAMING_SNAKE_CASE)

           _A					:     Union[str, Any]     = 2
           _A					:     Tuple     = randn_tensor(
               (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,							generator=SCREAMING_SNAKE_CASE				,							device=torch.device(SCREAMING_SNAKE_CASE)				,							)
           _A					:     Tuple     = floats_tensor(control_image.shape				,							rng=random.Random(SCREAMING_SNAKE_CASE)).to(SCREAMING_SNAKE_CASE)
           _A					:     Union[str, Any]     = image.cpu().permute(0				,							2				,							3				,							1)[0]
           _A					:     Union[str, Any]     = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE)).convert('RGB').resize((64, 64))
           _A					:     Any     = {
               'prompt': 'A painting of a squirrel eating a burger',
               'generator': generator,
               'num_inference_steps': 2,
               'guidance_scale': 6.0,
               'output_type': 'numpy',
               'image': image,
               'control_image': control_image,
           }

           return inputs


    def        A   (  self      :							str):
           return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)


    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()				,							reason='XFormers attention is only available with CUDA and `xformers` installed'				,							)
    def        A   (  self      :							Tuple):
           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)


    def        A   (  self      :							int):
           self._test_inference_batch_single_identical(expected_max_diff=2e-3)




class 			__lowerCamelCase			(	a_    , a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   StableDiffusionControlNetImgaImgPipeline
    a													=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
    a													=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    a													=   frozenset([]	)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess


    def        A   (  self      :							List[str]):
           torch.manual_seed(0)
           _A					:     List[str]     = UNetaDConditionModel(
               block_out_channels=(32, 64)				,							layers_per_block=2				,							sample_size=32				,							in_channels=4				,							out_channels=4				,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')				,							cross_attention_dim=32				,							)
           torch.manual_seed(0)

           def init_weights(SCREAMING_SNAKE_CASE      :							Union[str, Any]):
                  if isinstance(SCREAMING_SNAKE_CASE				,							torch.nn.Convad):
                         torch.nn.init.normal(m.weight)
                         m.bias.data.fill_(1.0)

           _A					:     int     = ControlNetModel(
               block_out_channels=(32, 64)				,							layers_per_block=2				,							in_channels=4				,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,							cross_attention_dim=32				,							conditioning_embedding_out_channels=(16, 32)				,							)
           controlneta.controlnet_down_blocks.apply(SCREAMING_SNAKE_CASE)

           torch.manual_seed(0)
           _A					:     str     = ControlNetModel(
               block_out_channels=(32, 64)				,							layers_per_block=2				,							in_channels=4				,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,							cross_attention_dim=32				,							conditioning_embedding_out_channels=(16, 32)				,							)
           controlneta.controlnet_down_blocks.apply(SCREAMING_SNAKE_CASE)

           torch.manual_seed(0)
           _A					:     List[Any]     = DDIMScheduler(
               beta_start=0.0_0085				,							beta_end=0.012				,							beta_schedule='scaled_linear'				,							clip_sample=SCREAMING_SNAKE_CASE				,							set_alpha_to_one=SCREAMING_SNAKE_CASE				,							)
           torch.manual_seed(0)
           _A					:     Tuple     = AutoencoderKL(
               block_out_channels=[32, 64]				,							in_channels=3				,							out_channels=3				,							down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']				,							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']				,							latent_channels=4				,							)
           torch.manual_seed(0)
           _A					:     str     = CLIPTextConfig(
               bos_token_id=0				,							eos_token_id=2				,							hidden_size=32				,							intermediate_size=37				,							layer_norm_eps=1e-05				,							num_attention_heads=4				,							num_hidden_layers=5				,							pad_token_id=1				,							vocab_size=1000				,							)
           _A					:     List[Any]     = CLIPTextModel(SCREAMING_SNAKE_CASE)
           _A					:     List[Any]     = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

           _A					:     List[str]     = MultiControlNetModel([controlneta, controlneta])

           _A					:     List[str]     = {
               'unet': unet,
               'controlnet': controlnet,
               'scheduler': scheduler,
               'vae': vae,
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'safety_checker': None,
               'feature_extractor': None,
           }
           return components


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							List[str]=0):
           if str(SCREAMING_SNAKE_CASE).startswith('mps'):
                  _A					:     Optional[Any]     = torch.manual_seed(SCREAMING_SNAKE_CASE)
           else:
                  _A					:     Union[str, Any]     = torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(SCREAMING_SNAKE_CASE)

           _A					:     Union[str, Any]     = 2

           _A					:     List[str]     = [
               randn_tensor(
                   (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,							generator=SCREAMING_SNAKE_CASE				,							device=torch.device(SCREAMING_SNAKE_CASE)				,							),
               randn_tensor(
                   (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,							generator=SCREAMING_SNAKE_CASE				,							device=torch.device(SCREAMING_SNAKE_CASE)				,							),
           ]

           _A					:     str     = floats_tensor(control_image[0].shape				,							rng=random.Random(SCREAMING_SNAKE_CASE)).to(SCREAMING_SNAKE_CASE)
           _A					:     Optional[int]     = image.cpu().permute(0				,							2				,							3				,							1)[0]
           _A					:     Optional[int]     = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE)).convert('RGB').resize((64, 64))
           _A					:     Optional[Any]     = {
               'prompt': 'A painting of a squirrel eating a burger',
               'generator': generator,
               'num_inference_steps': 2,
               'guidance_scale': 6.0,
               'output_type': 'numpy',
               'image': image,
               'control_image': control_image,
           }

           return inputs


    def        A   (  self      :							Tuple):
           _A					:     List[str]     = self.get_dummy_components()
           _A					:     List[Any]     = self.pipeline_class(**SCREAMING_SNAKE_CASE)
           pipe.to(SCREAMING_SNAKE_CASE)

           _A					:     int     = 10.0
           _A					:     Union[str, Any]     = 4

           _A					:     str     = self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
           _A					:     List[Any]     = steps
           _A					:     List[str]     = scale
           _A					:     int     = pipe(**SCREAMING_SNAKE_CASE)[0]

           _A					:     Any     = self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
           _A					:     Union[str, Any]     = steps
           _A					:     Any     = scale
           _A					:     Dict     = pipe(**SCREAMING_SNAKE_CASE				,							control_guidance_start=0.1				,							control_guidance_end=0.2)[0]

           _A					:     Tuple     = self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
           _A					:     str     = steps
           _A					:     List[Any]     = scale
           _A					:     int     = pipe(**SCREAMING_SNAKE_CASE				,							control_guidance_start=[0.1, 0.3]				,							control_guidance_end=[0.2, 0.7])[0]

           _A					:     List[str]     = self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
           _A					:     Tuple     = steps
           _A					:     Tuple     = scale
           _A					:     str     = pipe(**SCREAMING_SNAKE_CASE				,							control_guidance_start=0.4				,							control_guidance_end=[0.5, 0.8])[0]

           # make sure that all outputs are different
           assert np.sum(np.abs(output_a - output_a)) > 1e-3
           assert np.sum(np.abs(output_a - output_a)) > 1e-3
           assert np.sum(np.abs(output_a - output_a)) > 1e-3


    def        A   (  self      :							Optional[Any]):
           return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)


    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()				,							reason='XFormers attention is only available with CUDA and `xformers` installed'				,							)
    def        A   (  self      :							Any):
           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)


    def        A   (  self      :							Dict):
           self._test_inference_batch_single_identical(expected_max_diff=2e-3)


    def        A   (  self      :							str):
           _A					:     Optional[int]     = self.get_dummy_components()
           _A					:     Dict     = self.pipeline_class(**SCREAMING_SNAKE_CASE)
           pipe.to(SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
           with tempfile.TemporaryDirectory() as tmpdir:
                  try:
                         # save_pretrained is not implemented for Multi-ControlNet
                         pipe.save_pretrained(SCREAMING_SNAKE_CASE)
                  except NotImplementedError:
                         pass




@slow
@require_torch_gpu
class 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""


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


    def        A   (  self      :							Any):
           _A					:     Dict     = ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny')

           _A					:     List[Any]     = StableDiffusionControlNetImgaImgPipeline.from_pretrained(
               'runwayml/stable-diffusion-v1-5'				,							safety_checker=SCREAMING_SNAKE_CASE				,							controlnet=SCREAMING_SNAKE_CASE)
           pipe.enable_model_cpu_offload()
           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

           _A					:     List[Any]     = torch.Generator(device='cpu').manual_seed(0)
           _A					:     List[Any]     = 'evil space-punk bird'
           _A					:     int     = load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png').resize((512, 512))
           _A					:     List[str]     = load_image(
               'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png').resize((512, 512))

           _A					:     Dict     = pipe(
               SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							control_image=SCREAMING_SNAKE_CASE				,							generator=SCREAMING_SNAKE_CASE				,							output_type='np'				,							num_inference_steps=50				,							strength=0.6				,							)

           _A					:     Optional[int]     = output.images[0]

           assert image.shape == (512, 512, 3)

           _A					:     int     = load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy')

           assert np.abs(expected_image - image).max() < 9e-2






