



'''simple docstring'''

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,
)


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

a							   =				'\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				,							__UpperCAmelCase				,							__UpperCAmelCase=8					)				->					str:





			"""simple docstring"""







			snake_case:    Optional[int]      							=height // scale_factor**2
			if height % scale_factor**2 != 0:
						new_height += 1
			snake_case:    int      							=width // scale_factor**2
			if width % scale_factor**2 != 0:
						new_width += 1
			return new_height * scale_factor, new_width * scale_factor







class 						a_		(     snake_case    ):


							def __init__(				self   :						List[str]      ,						a_   :						UNetaDConditionModel      ,						a_   :						DDPMScheduler      ,						a_   :						VQModel      ,						)			->					str:
										super().__init__()

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


							def    UpperCamelCase     (				self   :						List[str]      ,						a_   :						Optional[int]      ,						a_   :						Optional[int]      ,						a_   :						List[str]      ,						a_   :						Any      ,						a_   :						Optional[int]      ,						a_   :						int			)			->					Optional[int]:
										if latents is None:
													snake_case:    List[Any]      							=randn_tensor(a_      ,						generator=a_      ,						device=a_      ,						dtype=a_			)
										else:
													if latents.shape != shape:
																raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''			)
													snake_case:    Optional[int]      							=latents.to(a_			)

										snake_case:    str      							=latents * scheduler.init_noise_sigma
										return latents


							def    UpperCamelCase     (				self   :						Any      ,						a_   :						Union[str, Any]=0			)			->					Dict:
										if is_accelerate_available():
													from accelerate import cpu_offload
										else:
													raise ImportError('Please install accelerate via `pip install accelerate`'			)

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

										snake_case:    Union[str, Any]      							=[
										    self.unet,
										    self.movq,
										]
										for cpu_offloaded_model in models:
													if cpu_offloaded_model is not None:
																cpu_offload(a_      ,						a_			)


							def    UpperCamelCase     (				self   :						Any      ,						a_   :						Optional[Any]=0			)			->					Any:
										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.'			)

										snake_case:    Optional[Any]      							=torch.device(F'''cuda:{gpu_id}'''			)

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

										snake_case:    List[Any]      							=None
										for cpu_offloaded_model in [self.unet, self.movq]:
													snake_case							,  snake_case:    Optional[Any]      							=cpu_offload_with_hook(a_      ,						a_      ,						prev_module_hook=a_			)

										# We'll offload the last model manually.
										snake_case:    List[str]      							=hook


							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def    UpperCamelCase     (				self   :						List[Any]			)			->					Any:
										if not hasattr(self.unet      ,						'_hf_hook'			):
													return self.device
										for module in self.unet.modules():
													if (
													    hasattr(a_      ,						'_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(a_			)
							def __call__(				self   :						List[str]      ,						a_   :						Union[torch.FloatTensor, List[torch.FloatTensor]]      ,						a_   :						Union[torch.FloatTensor, List[torch.FloatTensor]]      ,						a_   :						torch.FloatTensor      ,						a_   :						int = 5_1_2      ,						a_   :						int = 5_1_2      ,						a_   :						int = 1_0_0      ,						a_   :						float = 4.0      ,						a_   :						int = 1      ,						a_   :						Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,						a_   :						Optional[torch.FloatTensor] = None      ,						a_   :						Optional[str] = "pil"      ,						a_   :						bool = True      ,						)			->					str:
										snake_case:    List[Any]      							=self._execution_device

										snake_case:    Optional[Any]      							=guidance_scale > 1.0

										if isinstance(a_      ,						a_			):
													snake_case:    Any      							=torch.cat(a_      ,						dim=0			)
										if isinstance(a_      ,						a_			):
													snake_case:    Optional[Any]      							=torch.cat(a_      ,						dim=0			)
										if isinstance(a_      ,						a_			):
													snake_case:    Dict      							=torch.cat(a_      ,						dim=0			)

										snake_case:    int      							=image_embeds.shape[0] * num_images_per_prompt

										if do_classifier_free_guidance:
													snake_case:    Optional[int]      							=image_embeds.repeat_interleave(a_      ,						dim=0			)
													snake_case:    int      							=negative_image_embeds.repeat_interleave(a_      ,						dim=0			)
													snake_case:    Dict      							=hint.repeat_interleave(a_      ,						dim=0			)

										snake_case:    Optional[int]      							=torch.cat([negative_image_embeds, image_embeds]      ,						dim=0			).to(dtype=self.unet.dtype      ,						device=a_			)
										snake_case:    str      							=torch.cat([hint, hint]      ,						dim=0			).to(dtype=self.unet.dtype      ,						device=a_			)
										self.scheduler.set_timesteps(a_      ,						device=a_			)
										snake_case:    Optional[int]      							=self.scheduler.timesteps

										snake_case:    Optional[Any]      							=self.movq.config.latent_channels

										snake_case							,  snake_case:    Dict      							=downscale_height_and_width(a_      ,						a_      ,						self.movq_scale_factor			)

										# create initial latent
										snake_case:    Any      							=self.prepare_latents(
										    (batch_size, num_channels_latents, height, width)      ,						image_embeds.dtype      ,						a_      ,						a_      ,						a_      ,						self.scheduler      ,						)

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

													snake_case:    Dict      							={'image_embeds': image_embeds, 'hint': hint}
													snake_case:    Any      							=self.unet(
													    sample=a_      ,						timestep=a_      ,						encoder_hidden_states=a_      ,						added_cond_kwargs=a_      ,						return_dict=a_      ,						)[0]

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

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

										if output_type == "pil":
													snake_case:    Optional[int]      							=self.numpy_to_pil(a_			)

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=a_			)









'''simple docstring'''

import math

import tensorflow as tf
from packaging import version







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    str      							=tf.convert_to_tensor(__UpperCAmelCase					)
			snake_case:    int      							=0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0					)				,							x.dtype					)					))

			return x * cdf







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			snake_case:    Optional[int]      							=tf.convert_to_tensor(__UpperCAmelCase					)
			snake_case:    Tuple      							=tf.cast(math.pi				,							x.dtype					)
			snake_case:    Dict      							=tf.cast(0.04_4715				,							x.dtype					)
			snake_case:    List[str]      							=0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi					) * (x + coeff * tf.pow(__UpperCAmelCase				,							3					))					))

			return x * cdf







def a_     (    __UpperCAmelCase					)				->					Tuple:





			"""simple docstring"""







			snake_case:    Optional[int]      							=tf.convert_to_tensor(__UpperCAmelCase					)

			return x * tf.tanh(tf.math.softplus(__UpperCAmelCase					)					)







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			snake_case:    List[str]      							=tf.convert_to_tensor(__UpperCAmelCase					)
			snake_case:    Dict      							=tf.cast(0.04_4715				,							x.dtype					)
			snake_case:    List[str]      							=tf.cast(0.79_7884_5608				,							x.dtype					)

			return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)					))







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    Union[str, Any]      							=tf.convert_to_tensor(__UpperCAmelCase					)
			snake_case:    Tuple      							=tf.cast(1.702				,							x.dtype					)
			return x * tf.math.sigmoid(coeff * x					)







def a_     (    __UpperCAmelCase					)				->					int:





			"""simple docstring"""







			return tf.clip_by_value(_gelu(__UpperCAmelCase					)				,							-10				,							10					)







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase=-1					)				->					List[str]:





			"""simple docstring"""







			snake_case							,  snake_case:    int      							=tf.split(__UpperCAmelCase				,							2				,							axis=__UpperCAmelCase					)
			return a * tf.math.sigmoid(__UpperCAmelCase					)


if version.parse(tf.version.VERSION) >= version.parse('2.4'):







	def a_     (    __UpperCAmelCase					)				->					int:





				"""simple docstring"""







				return tf.keras.activations.gelu(__UpperCAmelCase				,							approximate=__UpperCAmelCase					)

	a							   =				tf.keras.activations.gelu







	a							   =				approximate_gelu_wrap
else:
	a							   =				_gelu
	a							   =				_gelu_new


a							   =				{
    'gelu': gelu,
    'gelu_10': gelu_aa,
    'gelu_fast': gelu_fast,
    'gelu_new': gelu_new,
    'glu': glu,
    'mish': mish,
    'quick_gelu': quick_gelu,
    'relu': tf.keras.activations.relu,
    'sigmoid': tf.keras.activations.sigmoid,
    'silu': tf.keras.activations.swish,
    'swish': tf.keras.activations.swish,
    'tanh': tf.keras.activations.tanh,
}







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			if activation_string in ACTaFN:
						return ACTaFN[activation_string]
			else:
						raise KeyError(f'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()					)}'''					)




