




from typing import List, Optional, Union

import torch

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,
)


UpperCAmelCase__					  =			logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase__					  =			"\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"







def 		_A(							UpperCamelCase__				:      List[Any]					,							UpperCamelCase__				:      Optional[int]					,							UpperCamelCase__				:      List[str]=8					)     ->			Dict:
							'''simple docstring'''


							__lowercase			  =     height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							__lowercase			  =     width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor







class a			(  __SCREAMING_SNAKE_CASE	):
						"""simple docstring"""

						def __init__(			self			:  Optional[int]					,				lowerCamelCase__			:  Optional[int]					,				lowerCamelCase__			:  List[str]					,				lowerCamelCase__			:  Optional[int]					,				)     ->    Union[str, Any]:
													"""simple docstring"""


													super().__init__()

													self.register_modules(
													    unet=UpperCAmelCase_					,				scheduler=UpperCAmelCase_					,				movq=UpperCAmelCase_					,				)
													__lowercase			  =     2 ** (len(self.movq.config.block_out_channels					) - 1)






						def      UpperCAmelCase_   (			self			:  int					,				lowerCamelCase__			:  int					,				lowerCamelCase__			:  int					,				lowerCamelCase__			:  List[str]					,				lowerCamelCase__			:  Optional[Any]					,				lowerCamelCase__			:  int					,				lowerCamelCase__			:  int					)     ->    Union[str, Any]:
													"""simple docstring"""


													if latents is None:
																				__lowercase			  =     randn_tensor(UpperCAmelCase_					,				generator=UpperCAmelCase_					,				device=UpperCAmelCase_					,				dtype=UpperCAmelCase_					)
													else:
																				if latents.shape != shape:
																											raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'					)
																				__lowercase			  =     latents.to(UpperCAmelCase_					)

													__lowercase			  =     latents * scheduler.init_noise_sigma
													return latents






						def      UpperCAmelCase_   (			self			:  int					,				lowerCamelCase__			:  Any=0					)     ->    int:
													"""simple docstring"""


													if is_accelerate_available():
																				from accelerate import cpu_offload
													else:
																				raise ImportError('''Please install accelerate via `pip install accelerate`'''					)

													__lowercase			  =     torch.device(f'cuda:{gpu_id}'					)

													__lowercase			  =     [
													    self.unet,
													    self.movq,
													]
													for cpu_offloaded_model in models:
																				if cpu_offloaded_model is not None:
																											cpu_offload(UpperCAmelCase_					,				UpperCAmelCase_					)






						def      UpperCAmelCase_   (			self			:  Union[str, Any]					,				lowerCamelCase__			:  str=0					)     ->    Optional[Any]:
													"""simple docstring"""


													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.'''					)

													__lowercase			  =     torch.device(f'cuda:{gpu_id}'					)

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

													__lowercase			  =     None
													for cpu_offloaded_model in [self.unet, self.movq]:
																				__lowercase  ,  __lowercase			  =     cpu_offload_with_hook(UpperCAmelCase_					,				UpperCAmelCase_					,				prev_module_hook=UpperCAmelCase_					)

													# We'll offload the last model manually.
													__lowercase			  =     hook






						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def      UpperCAmelCase_   (			self			:  Optional[Any]					)     ->    int:
													"""simple docstring"""


													if not hasattr(self.unet					,				'''_hf_hook'''					):
																				return self.device
													for module in self.unet.modules():
																				if (
																				    hasattr(UpperCAmelCase_					,				'''_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(UpperCAmelCase_					)
						def __call__(			self			:  Union[str, Any]					,				lowerCamelCase__			:  List[Any]					,				lowerCamelCase__			:  List[Any]					,				lowerCamelCase__			:  Optional[Any]					,				lowerCamelCase__			:  Union[str, Any] = 512					,				lowerCamelCase__			:  List[Any] = 512					,				lowerCamelCase__			:  Dict = 100					,				lowerCamelCase__			:  int = 4.0					,				lowerCamelCase__			:  Tuple = 1					,				lowerCamelCase__			:  Union[str, Any] = None					,				lowerCamelCase__			:  Optional[int] = None					,				lowerCamelCase__			:  Optional[int] = "pil"					,				lowerCamelCase__			:  List[str] = True					,				)     ->    int:
													"""simple docstring"""


													__lowercase			  =     self._execution_device

													__lowercase			  =     guidance_scale > 1.0

													if isinstance(UpperCAmelCase_					,				UpperCAmelCase_					):
																				__lowercase			  =     torch.cat(UpperCAmelCase_					,				dim=0					)
													if isinstance(UpperCAmelCase_					,				UpperCAmelCase_					):
																				__lowercase			  =     torch.cat(UpperCAmelCase_					,				dim=0					)
													if isinstance(UpperCAmelCase_					,				UpperCAmelCase_					):
																				__lowercase			  =     torch.cat(UpperCAmelCase_					,				dim=0					)

													__lowercase			  =     image_embeds.shape[0] * num_images_per_prompt

													if do_classifier_free_guidance:
																				__lowercase			  =     image_embeds.repeat_interleave(UpperCAmelCase_					,				dim=0					)
																				__lowercase			  =     negative_image_embeds.repeat_interleave(UpperCAmelCase_					,				dim=0					)
																				__lowercase			  =     hint.repeat_interleave(UpperCAmelCase_					,				dim=0					)

													__lowercase			  =     torch.cat([negative_image_embeds, image_embeds]					,				dim=0					).to(dtype=self.unet.dtype					,				device=UpperCAmelCase_					)
													__lowercase			  =     torch.cat([hint, hint]					,				dim=0					).to(dtype=self.unet.dtype					,				device=UpperCAmelCase_					)
													self.scheduler.set_timesteps(UpperCAmelCase_					,				device=UpperCAmelCase_					)
													__lowercase			  =     self.scheduler.timesteps

													__lowercase			  =     self.movq.config.latent_channels

													__lowercase  ,  __lowercase			  =     downscale_height_and_width(UpperCAmelCase_					,				UpperCAmelCase_					,				self.movq_scale_factor					)

													# create initial latent
													__lowercase			  =     self.prepare_latents(
													    (batch_size, num_channels_latents, height, width)					,				image_embeds.dtype					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				self.scheduler					,				)

													for i, t in enumerate(self.progress_bar(UpperCAmelCase_					)					):
																				# expand the latents if we are doing classifier free guidance
																				__lowercase			  =     torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

																				__lowercase			  =     {'''image_embeds''': image_embeds, '''hint''': hint}
																				__lowercase			  =     self.unet(
																				    sample=UpperCAmelCase_					,				timestep=UpperCAmelCase_					,				encoder_hidden_states=UpperCAmelCase_					,				added_cond_kwargs=UpperCAmelCase_					,				return_dict=UpperCAmelCase_					,				)[0]

																				if do_classifier_free_guidance:
																											__lowercase  ,  __lowercase			  =     noise_pred.split(latents.shape[1]					,				dim=1					)
																											__lowercase  ,  __lowercase			  =     noise_pred.chunk(2					)
																											__lowercase  ,  __lowercase			  =     variance_pred.chunk(2					)
																											__lowercase			  =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																											__lowercase			  =     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"]
																				):
																											__lowercase  ,  __lowercase			  =     noise_pred.split(latents.shape[1]					,				dim=1					)

																				# compute the previous noisy sample x_t -> x_t-1
																				__lowercase			  =     self.scheduler.step(
																				    UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				generator=UpperCAmelCase_					,				)[0]
													# post-processing
													__lowercase			  =     self.movq.decode(UpperCAmelCase_					,				force_not_quantize=UpperCAmelCase_					)['''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"]:
																				__lowercase			  =     image * 0.5 + 0.5
																				__lowercase			  =     image.clamp(0					,				1					)
																				__lowercase			  =     image.cpu().permute(0					,				2					,				3					,				1					).float().numpy()

													if output_type == "pil":
																				__lowercase			  =     self.numpy_to_pil(UpperCAmelCase_					)

													if not return_dict:
																				return (image,)

													return ImagePipelineOutput(images=UpperCAmelCase_					)











'''simple docstring'''





def 							lowerCamelCase	(					_snake_case	:    str							):





    '''simple docstring'''


    return " ".join(
        "".join(word[::-1]							) if len(_snake_case							) > 4 else word for word in sentence.split()							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(reverse_long_words("Hey wollef sroirraw"))




