



"""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,
)


lowercase__					:						Union[str, Any]												=			logging.get_logger(__name__)  # pylint: disable=invalid-name

lowercase__					:						Tuple												=			'''
    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 __lowercase    (							_a	,    _a	,    _a=8					):
    snake_case_				:  Tuple       =			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  _UpperCAmelCase (    lowerCAmelCase__):

       def __init__(  self	: Tuple						,  lowercase_	: UNetaDConditionModel						,  lowercase_	: DDPMScheduler						,  lowercase_	: VQModel						,  ):
           super().__init__()

           self.register_modules(
               unet=lowercase_						,  scheduler=lowercase_						,  movq=lowercase_						,  )
           snake_case_				:  Dict       =			2 ** (len(self.movq.config.block_out_channels       ) - 1)

       def 		_snake_case      (  self	: Union[str, Any]						,  lowercase_	: List[str]						,  lowercase_	: Tuple						,  lowercase_	: Any						,  lowercase_	: Dict						,  lowercase_	: str						,  lowercase_	: Tuple       ):
           if latents is None:
               snake_case_				:  int       =			randn_tensor(lowercase_						,  generator=lowercase_						,  device=lowercase_						,  dtype=lowercase_       )
           else:
               if latents.shape != shape:
                   raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"       )
               snake_case_				:  Optional[int]       =			latents.to(lowercase_       )

           snake_case_				:  Any       =			latents * scheduler.init_noise_sigma
           return latents

       def 		_snake_case      (  self	: Optional[int]						,  lowercase_	: Optional[int]=0       ):
           if is_accelerate_available():
               from accelerate import cpu_offload
           else:
               raise ImportError('''Please install accelerate via `pip install accelerate`'''       )

           snake_case_				:  int       =			torch.device(f"cuda:{gpu_id}"       )

           snake_case_				:  List[str]       =			[
               self.unet,
               self.movq,
           ]
           for cpu_offloaded_model in models:
               if cpu_offloaded_model is not None:
                   cpu_offload(lowercase_						,  lowercase_       )

       def 		_snake_case      (  self	: Any						,  lowercase_	: List[str]=0       ):
           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_				:  str       =			torch.device(f"cuda:{gpu_id}"       )

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

           snake_case_				:  str       =			None
           for cpu_offloaded_model in [self.unet, self.movq]:
               snake_case_				:  List[Any]       =			cpu_offload_with_hook(lowercase_						,  lowercase_						,  prev_module_hook=lowercase_       )

           # We'll offload the last model manually.
           snake_case_				:  int       =			hook

       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 		_snake_case      (  self	: Optional[Any]       ):
           if not hasattr(self.unet						,  '''_hf_hook'''       ):
               return self.device
           for module in self.unet.modules():
               if (
                   hasattr(lowercase_						,  '''_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(lowercase_       )
       def __call__(  self	: Any						,  lowercase_	: Union[torch.FloatTensor, List[torch.FloatTensor]]						,  lowercase_	: Union[torch.FloatTensor, List[torch.FloatTensor]]						,  lowercase_	: torch.FloatTensor						,  lowercase_	: int = 512						,  lowercase_	: int = 512						,  lowercase_	: int = 100						,  lowercase_	: float = 4.0						,  lowercase_	: int = 1						,  lowercase_	: Optional[Union[torch.Generator, List[torch.Generator]]] = None						,  lowercase_	: Optional[torch.FloatTensor] = None						,  lowercase_	: Optional[str] = "pil"						,  lowercase_	: bool = True						,  ):
           snake_case_				:  List[str]       =			self._execution_device

           snake_case_				:  str       =			guidance_scale > 1.0

           if isinstance(lowercase_						,  lowercase_       ):
               snake_case_				:  Any       =			torch.cat(lowercase_						,  dim=0       )
           if isinstance(lowercase_						,  lowercase_       ):
               snake_case_				:  Union[str, Any]       =			torch.cat(lowercase_						,  dim=0       )
           if isinstance(lowercase_						,  lowercase_       ):
               snake_case_				:  Union[str, Any]       =			torch.cat(lowercase_						,  dim=0       )

           snake_case_				:  Union[str, Any]       =			image_embeds.shape[0] * num_images_per_prompt

           if do_classifier_free_guidance:
               snake_case_				:  List[Any]       =			image_embeds.repeat_interleave(lowercase_						,  dim=0       )
               snake_case_				:  Tuple       =			negative_image_embeds.repeat_interleave(lowercase_						,  dim=0       )
               snake_case_				:  str       =			hint.repeat_interleave(lowercase_						,  dim=0       )

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

           snake_case_				:  int       =			self.movq.config.latent_channels

           snake_case_				:  Dict       =			downscale_height_and_width(lowercase_						,  lowercase_						,  self.movq_scale_factor       )

           # create initial latent
           snake_case_				:  Dict       =			self.prepare_latents(
               (batch_size, num_channels_latents, height, width)						,  image_embeds.dtype						,  lowercase_						,  lowercase_						,  lowercase_						,  self.scheduler						,  )

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

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

               if do_classifier_free_guidance:
                   snake_case_				:  Tuple       =			noise_pred.split(latents.shape[1]						,  dim=1       )
                   snake_case_				:  Union[str, Any]       =			noise_pred.chunk(2       )
                   snake_case_				:  List[str]       =			variance_pred.chunk(2       )
                   snake_case_				:  Optional[int]       =			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_				:  Optional[Any]       =			noise_pred.split(latents.shape[1]						,  dim=1       )

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

           if output_type == "pil":
               snake_case_				:  Union[str, Any]       =			self.numpy_to_pil(lowercase_       )

           if not return_dict:
               return (image,)

           return ImagePipelineOutput(images=lowercase_       )










"""simple docstring"""

from statistics import mean, stdev




def __lowercase    (							_a	,    _a = 3					):
    snake_case_				:  Optional[int]       =			min(_a					)
    snake_case_				:  str       =			max(_a					)
    # normalize data
    return [round((x - x_min) / (x_max - x_min)	,    _a					) for x in data]






def __lowercase    (							_a	,    _a = 3					):
    snake_case_				:  Any       =			mean(_a					)
    snake_case_				:  str       =			stdev(_a					)
    # standardize data
    return [round((x - mu) / (sigma)	,    _a					) for x in data]





