





"""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,
)


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

UpperCamelCase_									="""
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")


        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> 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\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase=8       ):
						_UpperCamelCase      : str         =   height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						_UpperCamelCase      : Optional[Any]         =   width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor






class 			_a						(				_lowerCAmelCase    ):






				def __init__(			self		:   List[str],			lowerCAmelCase__		:   UNetaDConditionModel,			lowerCAmelCase__		:   DDPMScheduler,			lowerCAmelCase__		:   VQModel,			)     ->				Dict:




										'''simple docstring'''



										super().__init__()

										self.register_modules(
										    unet=lowerCAmelCase__,			scheduler=lowerCAmelCase__,			movq=lowerCAmelCase__,			)
										_UpperCamelCase      : Optional[Any]         =   2 ** (len(self.movq.config.block_out_channels							) - 1)






				def     snake_case     (			self		:   List[str],			lowerCAmelCase__		:   int,			lowerCAmelCase__		:   Tuple,			lowerCAmelCase__		:   Optional[Any],			lowerCAmelCase__		:   Optional[Any],			lowerCAmelCase__		:   Any,			lowerCAmelCase__		:   Optional[int]							)     ->				Union[str, Any]:




										'''simple docstring'''



										if latents is None:
																_UpperCamelCase      : Any         =   randn_tensor(lowerCAmelCase__,			generator=lowerCAmelCase__,			device=lowerCAmelCase__,			dtype=lowerCAmelCase__							)
										else:
																if latents.shape != shape:
																						raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""							)
																_UpperCamelCase      : int         =   latents.to(lowerCAmelCase__							)

										_UpperCamelCase      : Optional[Any]         =   latents * scheduler.init_noise_sigma
										return latents






				def     snake_case     (			self		:   List[Any],			lowerCAmelCase__		:   str=0							)     ->				Tuple:




										'''simple docstring'''



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

										_UpperCamelCase      : str         =   torch.device(f"""cuda:{gpu_id}"""							)

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






				def     snake_case     (			self		:   Any,			lowerCAmelCase__		:   Optional[Any]=0							)     ->				Dict:




										'''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      : Optional[int]         =   torch.device(f"""cuda:{gpu_id}"""							)

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

										_UpperCamelCase      : str         =   None
										for cpu_offloaded_model in [self.unet, self.movq]:
																_UpperCamelCase       ,							_UpperCamelCase      : Union[str, Any]         =   cpu_offload_with_hook(lowerCAmelCase__,			lowerCAmelCase__,			prev_module_hook=lowerCAmelCase__							)

										# We'll offload the last model manually.
										_UpperCamelCase      : Optional[int]         =   hook






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




										'''simple docstring'''



										if not hasattr(self.unet,			'''_hf_hook'''							):
																return self.device
										for module in self.unet.modules():
																if (
																    hasattr(lowerCAmelCase__,			'''_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(lowerCAmelCase__							)
				def __call__(			self		:   Tuple,			lowerCAmelCase__		:   Union[torch.FloatTensor, List[torch.FloatTensor]],			lowerCAmelCase__		:   Union[torch.FloatTensor, List[torch.FloatTensor]],			lowerCAmelCase__		:   torch.FloatTensor,			lowerCAmelCase__		:   int = 5_1_2,			lowerCAmelCase__		:   int = 5_1_2,			lowerCAmelCase__		:   int = 1_0_0,			lowerCAmelCase__		:   float = 4.0,			lowerCAmelCase__		:   int = 1,			lowerCAmelCase__		:   Optional[Union[torch.Generator, List[torch.Generator]]] = None,			lowerCAmelCase__		:   Optional[torch.FloatTensor] = None,			lowerCAmelCase__		:   Optional[str] = "pil",			lowerCAmelCase__		:   bool = True,			)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   self._execution_device

										_UpperCamelCase      : Any         =   guidance_scale > 1.0

										if isinstance(lowerCAmelCase__,			lowerCAmelCase__							):
																_UpperCamelCase      : str         =   torch.cat(lowerCAmelCase__,			dim=0							)
										if isinstance(lowerCAmelCase__,			lowerCAmelCase__							):
																_UpperCamelCase      : Tuple         =   torch.cat(lowerCAmelCase__,			dim=0							)
										if isinstance(lowerCAmelCase__,			lowerCAmelCase__							):
																_UpperCamelCase      : Optional[int]         =   torch.cat(lowerCAmelCase__,			dim=0							)

										_UpperCamelCase      : List[Any]         =   image_embeds.shape[0] * num_images_per_prompt

										if do_classifier_free_guidance:
																_UpperCamelCase      : Union[str, Any]         =   image_embeds.repeat_interleave(lowerCAmelCase__,			dim=0							)
																_UpperCamelCase      : List[Any]         =   negative_image_embeds.repeat_interleave(lowerCAmelCase__,			dim=0							)
																_UpperCamelCase      : str         =   hint.repeat_interleave(lowerCAmelCase__,			dim=0							)

										_UpperCamelCase      : Tuple         =   torch.cat([negative_image_embeds, image_embeds],			dim=0							).to(dtype=self.unet.dtype,			device=lowerCAmelCase__							)
										_UpperCamelCase      : List[Any]         =   torch.cat([hint, hint],			dim=0							).to(dtype=self.unet.dtype,			device=lowerCAmelCase__							)
										self.scheduler.set_timesteps(lowerCAmelCase__,			device=lowerCAmelCase__							)
										_UpperCamelCase      : Union[str, Any]         =   self.scheduler.timesteps

										_UpperCamelCase      : Dict         =   self.movq.config.latent_channels

										_UpperCamelCase       ,							_UpperCamelCase      : str         =   downscale_height_and_width(lowerCAmelCase__,			lowerCAmelCase__,			self.movq_scale_factor							)

										# create initial latent
										_UpperCamelCase      : List[Any]         =   self.prepare_latents(
										    (batch_size, num_channels_latents, height, width),			image_embeds.dtype,			lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			self.scheduler,			)

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

																_UpperCamelCase      : Tuple         =   {'''image_embeds''': image_embeds, '''hint''': hint}
																_UpperCamelCase      : List[str]         =   self.unet(
																    sample=lowerCAmelCase__,			timestep=lowerCAmelCase__,			encoder_hidden_states=lowerCAmelCase__,			added_cond_kwargs=lowerCAmelCase__,			return_dict=lowerCAmelCase__,			)[0]

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

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

										if output_type == "pil":
																_UpperCamelCase      : List[str]         =   self.numpy_to_pil(lowerCAmelCase__							)

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=lowerCAmelCase__							)










"""simple docstring"""




def 	a_    (							_lowercase	,						_lowercase       ):
						if discount_rate < 0:
												raise ValueError('''Discount rate cannot be negative'''       )
						if not cash_flows:
												raise ValueError('''Cash flows list cannot be empty'''       )
						_UpperCamelCase      : Optional[int]         =   sum(
						    cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(_lowercase       )       )
						return round(_lowercase	,						ndigits=2       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



