
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,
)


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

lowerCamelCase__						=				'\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(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase=8			)		->					Any:

		"""simple docstring"""






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







class 							lowerCAmelCase__   ( __lowercase						):

						def __init__(      self							,				a							,				a							,				a							,				)       ->   List[str]:


								'''simple docstring'''

								super().__init__()

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

						def 			A_					(      self							,				a							,				a							,				a							,				a							,				a							,				a					)       ->   Union[str, Any]:


								'''simple docstring'''

								if latents is None:
										_UpperCamelCase         =						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}'					)
										_UpperCamelCase         =						latents.to(A_					)

								_UpperCamelCase         =						latents * scheduler.init_noise_sigma
								return latents

						def 			A_					(      self							,				a=0					)       ->   Tuple:


								'''simple docstring'''

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

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

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

						def 			A_					(      self							,				a=0					)       ->   str:


								'''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."""					)

								_UpperCamelCase         =						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)

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

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

						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def 			A_					(      self					)       ->   Optional[int]:


								'''simple docstring'''

								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							,				a							,				a							,				a							,				a = 5_12							,				a = 5_12							,				a = 1_00							,				a = 4.0							,				a = 1							,				a = None							,				a = None							,				a = "pil"							,				a = True							,				)       ->   List[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						self._execution_device

								_UpperCamelCase         =						guidance_scale > 1.0

								if isinstance(A_							,				A_					):
										_UpperCamelCase         =						torch.cat(A_							,				dim=0					)
								if isinstance(A_							,				A_					):
										_UpperCamelCase         =						torch.cat(A_							,				dim=0					)
								if isinstance(A_							,				A_					):
										_UpperCamelCase         =						torch.cat(A_							,				dim=0					)

								_UpperCamelCase         =						image_embeds.shape[0] * num_images_per_prompt

								if do_classifier_free_guidance:
										_UpperCamelCase         =						image_embeds.repeat_interleave(A_							,				dim=0					)
										_UpperCamelCase         =						negative_image_embeds.repeat_interleave(A_							,				dim=0					)
										_UpperCamelCase         =						hint.repeat_interleave(A_							,				dim=0					)

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

								_UpperCamelCase         =						self.movq.config.latent_channels

								_UpperCamelCase     ,						_UpperCamelCase         =						downscale_height_and_width(A_							,				A_							,				self.movq_scale_factor					)

								# create initial latent
								_UpperCamelCase         =						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
										_UpperCamelCase         =						torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

										_UpperCamelCase         =						{"""image_embeds""": image_embeds, """hint""": hint}
										_UpperCamelCase         =						self.unet(
										    sample=A_							,				timestep=A_							,				encoder_hidden_states=A_							,				added_cond_kwargs=A_							,				return_dict=A_							,				)[0]

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

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

								if output_type == "pil":
										_UpperCamelCase         =						self.numpy_to_pil(A_					)

								if not return_dict:
										return (image,)

								return ImagePipelineOutput(images=A_					)







"""simple docstring"""




from collections.abc import Callable





def       a_ (	lowercase__ :Callable[[float], float],			lowercase__ :float,			lowercase__ :float     ):
			__lowerCamelCase          =      a
			__lowerCamelCase          =      b
			if function(lowercase__     ) == 0:  # one of the a or b is a root for the function
						return a
			elif function(lowercase__     ) == 0:
						return b
			elif (
			    function(lowercase__     ) * function(lowercase__     ) > 0
			):  # if none of these are root and they are both positive or negative,
						# then this algorithm can't find the root
						raise ValueError("""could not find root in given interval."""     )
			else:
						__lowerCamelCase          =      start + (end - start) / 2.0
						while abs(start - mid     ) > 10**-7:  # until precisely equals to 10^-7
									if function(lowercase__     ) == 0:
												return mid
									elif function(lowercase__     ) * function(lowercase__     ) < 0:
												__lowerCamelCase          =      mid
									else:
												__lowerCamelCase          =      mid
									__lowerCamelCase          =      start + (end - start) / 2.0
						return mid





def       a_ (	lowercase__ :float     ):
			return x**3 - 2 * x - 5


if __name__ == "__main__":
					print(bisection(f, 1, 1_0_0_0))

					import doctest

					doctest.testmod()




