
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  lowerCamelCase__ (					_a							,			_a							,			_a=8):
	SCREAMING_SNAKE_CASE   :      int				     =       height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       width // scale_factor**2
	if width % scale_factor**2 != 0:
		new_width += 1
	return new_height * scale_factor, new_width * scale_factor






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			def __init__(			self							: Any      ,      a							: UNetaDConditionModel      ,      a							: DDPMScheduler      ,      a							: VQModel      ,      )							->       List[Any]:

				"""simple docstring"""

				super().__init__()

				self.register_modules(
				    unet=a      ,      scheduler=a      ,      movq=a      ,      )
				SCREAMING_SNAKE_CASE   :      List[str]				     =       2 ** (len(self.movq.config.block_out_channels	) - 1)
			def 							__UpperCamelCase (			self							: int      ,      a							: Any      ,      a							: Optional[Any]      ,      a							: int      ,      a							: List[str]      ,      a							: List[str]      ,      a							: str	)							->       Optional[int]:

				"""simple docstring"""

				if latents is None:
					SCREAMING_SNAKE_CASE   :      Optional[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}"	)
					SCREAMING_SNAKE_CASE   :      Dict				     =       latents.to(a	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       latents * scheduler.init_noise_sigma
				return latents
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: Union[str, Any]=0	)							->       List[Any]:

				"""simple docstring"""

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

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

				SCREAMING_SNAKE_CASE   :      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							: List[str]      ,      a							: Tuple=0	)							->       Union[str, 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."	)

				SCREAMING_SNAKE_CASE   :      List[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)

				SCREAMING_SNAKE_CASE   :      int				     =       None
				for cpu_offloaded_model in [self.unet, self.movq]:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       cpu_offload_with_hook(a      ,      a      ,      prev_module_hook=a	)

				# We'll offload the last model manually.
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       hook
			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 							__UpperCamelCase (			self							: Dict	)							->       Union[str, Any]:

				"""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							: Tuple      ,      a							: Union[torch.FloatTensor, List[torch.FloatTensor]]      ,      a							: Union[torch.FloatTensor, List[torch.FloatTensor]]      ,      a							: torch.FloatTensor      ,      a							: int = 512      ,      a							: int = 512      ,      a							: int = 100      ,      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      ,      )							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       self._execution_device

				SCREAMING_SNAKE_CASE   :      Dict				     =       guidance_scale > 1.0

				if isinstance(a      ,      a	):
					SCREAMING_SNAKE_CASE   :      int				     =       torch.cat(a      ,      dim=0	)
				if isinstance(a      ,      a	):
					SCREAMING_SNAKE_CASE   :      List[str]				     =       torch.cat(a      ,      dim=0	)
				if isinstance(a      ,      a	):
					SCREAMING_SNAKE_CASE   :      List[str]				     =       torch.cat(a      ,      dim=0	)

				SCREAMING_SNAKE_CASE   :      Dict				     =       image_embeds.shape[0] * num_images_per_prompt

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

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

				SCREAMING_SNAKE_CASE   :      int				     =       self.movq.config.latent_channels

				SCREAMING_SNAKE_CASE   :      Dict				     =       downscale_height_and_width(a      ,      a      ,      self.movq_scale_factor	)

				# create initial latent
				SCREAMING_SNAKE_CASE   :      List[str]				     =       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
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.cat([latents] * 2	) if do_classifier_free_guidance else latents

					SCREAMING_SNAKE_CASE   :      int				     =       {"image_embeds": image_embeds, "hint": hint}
					SCREAMING_SNAKE_CASE   :      str				     =       self.unet(
					    sample=a      ,      timestep=a      ,      encoder_hidden_states=a      ,      added_cond_kwargs=a      ,      return_dict=a      ,      )[0]

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

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

				if output_type == "pil":
					SCREAMING_SNAKE_CASE   :      str				     =       self.numpy_to_pil(a	)

				if not return_dict:
					return (image,)

				return ImagePipelineOutput(images=a	)


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin






@flax.struct.dataclass
class _UpperCamelCase							:
			'''simple docstring'''







			lowerCamelCase__           =None
			lowerCamelCase__           =None
			lowerCamelCase__           =None  # sigma(t_i)
			@classmethod
			def 							__UpperCamelCase (			cls							: Tuple	)							->       int:

				"""simple docstring"""

				return cls()






@dataclass
class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           =42
			lowerCamelCase__           =42
			lowerCamelCase__           =42






class _UpperCamelCase							(							__A							,	__A   ):
			'''simple docstring'''







			@property
			def 							__UpperCamelCase (			self							: List[str]	)							->       Optional[int]:

				"""simple docstring"""

				return True
			@register_to_config
			def __init__(			self							: List[str]      ,      a							: float = 0.02      ,      a							: float = 100      ,      a							: float = 1.007      ,      a							: float = 80      ,      a							: float = 0.05      ,      a							: float = 50      ,      )							->       Optional[Any]:

				"""simple docstring"""

				pass
			def 							__UpperCamelCase (			self							: Dict	)							->       str:

				"""simple docstring"""

				return KarrasVeSchedulerState.create()
			def 							__UpperCamelCase (			self							: Tuple      ,      a							: KarrasVeSchedulerState      ,      a							: int      ,      a							: Tuple = ()	)							->       KarrasVeSchedulerState:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       jnp.arange(0      ,      a	)[::-1].copy()
				SCREAMING_SNAKE_CASE   :      int				     =       [
				    (
				        self.config.sigma_max**2
				        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
				    )
				    for i in timesteps
				]

				return state.replace(
				    num_inference_steps=a      ,      schedule=jnp.array(a      ,      dtype=jnp.floataa	)      ,      timesteps=a      ,      )
			def 							__UpperCamelCase (			self							: str      ,      a							: KarrasVeSchedulerState      ,      a							: jnp.ndarray      ,      a							: float      ,      a							: random.KeyArray      ,      )							->       Tuple[jnp.ndarray, float]:

				"""simple docstring"""

				if self.config.s_min <= sigma <= self.config.s_max:
					SCREAMING_SNAKE_CASE   :      Dict				     =       min(self.config.s_churn / state.num_inference_steps      ,      2**0.5 - 1	)
				else:
					SCREAMING_SNAKE_CASE   :      Any				     =       0

				# sample eps ~ N(0, S_noise^2 * I)
				SCREAMING_SNAKE_CASE   :      Any				     =       random.split(a      ,      num=1	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.config.s_noise * random.normal(key=a      ,      shape=sample.shape	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       sigma + gamma * sigma
				SCREAMING_SNAKE_CASE   :      str				     =       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

				return sample_hat, sigma_hat
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: KarrasVeSchedulerState      ,      a							: jnp.ndarray      ,      a							: float      ,      a							: float      ,      a							: jnp.ndarray      ,      a							: bool = True      ,      )							->       Union[FlaxKarrasVeOutput, Tuple]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       sample_hat + sigma_hat * model_output
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (sample_hat - pred_original_sample) / sigma_hat
				SCREAMING_SNAKE_CASE   :      str				     =       sample_hat + (sigma_prev - sigma_hat) * derivative

				if not return_dict:
					return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=a      ,      derivative=a      ,      state=a	)
			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: KarrasVeSchedulerState      ,      a							: jnp.ndarray      ,      a							: float      ,      a							: float      ,      a							: jnp.ndarray      ,      a							: jnp.ndarray      ,      a							: jnp.ndarray      ,      a							: bool = True      ,      )							->       Union[FlaxKarrasVeOutput, Tuple]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       sample_prev + sigma_prev * model_output
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       (sample_prev - pred_original_sample) / sigma_prev
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

				if not return_dict:
					return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=a      ,      derivative=a      ,      state=a	)






			def 							__UpperCamelCase (			self							: int      ,      a							: KarrasVeSchedulerState      ,      a							: Optional[Any]      ,      a							: int      ,      a							: Dict	)							->       List[str]:

				"""simple docstring"""

				raise NotImplementedError()