





"""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__				:Optional[int]				   =       logging.get_logger(__name__)  # pylint: disable=invalid-name

lowercase__				:int				   =       '\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_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=8  )						->Union[str, Any]:





  """simple docstring"""


  __UpperCAmelCase    :						List[str]       =						height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  __UpperCAmelCase    :						Union[str, Any]       =						width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor







class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      def __init__(							self       :      Dict						,				__lowercase       :      UNetaDConditionModel						,				__lowercase       :      DDPMScheduler						,				__lowercase       :      VQModel						,				):


        '''simple docstring'''




        super().__init__()

        self.register_modules(
            unet=__lowercase						,				scheduler=__lowercase						,				movq=__lowercase						,				)
        __UpperCAmelCase    :						int       =						2 ** (len(self.movq.config.block_out_channels ) - 1)

      def       A_					(							self       :      Optional[Any]						,				__lowercase       :      List[str]						,				__lowercase       :      List[str]						,				__lowercase       :      List[Any]						,				__lowercase       :      Any						,				__lowercase       :      Optional[Any]						,				__lowercase       :      List[str] ):


        '''simple docstring'''




        if latents is None:
          __UpperCAmelCase    :						str       =						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}''' )
          __UpperCAmelCase    :						int       =						latents.to(__lowercase )

        __UpperCAmelCase    :						Union[str, Any]       =						latents * scheduler.init_noise_sigma
        return latents

      def       A_					(							self       :      str						,				__lowercase       :      List[str]=0 ):


        '''simple docstring'''




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

        __UpperCAmelCase    :						int       =						torch.device(f'''cuda:{gpu_id}''' )

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

      def       A_					(							self       :      Union[str, Any]						,				__lowercase       :      int=0 ):


        '''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    :						int       =						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)

        __UpperCAmelCase    :						Dict       =						None
        for cpu_offloaded_model in [self.unet, self.movq]:
          __UpperCAmelCase       ,     __UpperCAmelCase    :						Tuple       =						cpu_offload_with_hook(__lowercase						,				__lowercase						,				prev_module_hook=__lowercase )

        # We'll offload the last model manually.
        __UpperCAmelCase    :						Tuple       =						hook

      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def       A_					(							self       :      int ):


        '''simple docstring'''




        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       :      Tuple						,				__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						,				):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						self._execution_device

        __UpperCAmelCase    :						Optional[int]       =						guidance_scale > 1.0

        if isinstance(__lowercase						,				__lowercase ):
          __UpperCAmelCase    :						Optional[int]       =						torch.cat(__lowercase						,				dim=0 )
        if isinstance(__lowercase						,				__lowercase ):
          __UpperCAmelCase    :						str       =						torch.cat(__lowercase						,				dim=0 )
        if isinstance(__lowercase						,				__lowercase ):
          __UpperCAmelCase    :						List[Any]       =						torch.cat(__lowercase						,				dim=0 )

        __UpperCAmelCase    :						Optional[int]       =						image_embeds.shape[0] * num_images_per_prompt

        if do_classifier_free_guidance:
          __UpperCAmelCase    :						List[str]       =						image_embeds.repeat_interleave(__lowercase						,				dim=0 )
          __UpperCAmelCase    :						int       =						negative_image_embeds.repeat_interleave(__lowercase						,				dim=0 )
          __UpperCAmelCase    :						Optional[Any]       =						hint.repeat_interleave(__lowercase						,				dim=0 )

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

        __UpperCAmelCase    :						Optional[int]       =						self.movq.config.latent_channels

        __UpperCAmelCase       ,     __UpperCAmelCase    :						Dict       =						downscale_height_and_width(__lowercase						,				__lowercase						,				self.movq_scale_factor )

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

          __UpperCAmelCase    :						Any       =						{'''image_embeds''': image_embeds, '''hint''': hint}
          __UpperCAmelCase    :						str       =						self.unet(
              sample=__lowercase						,				timestep=__lowercase						,				encoder_hidden_states=__lowercase						,				added_cond_kwargs=__lowercase						,				return_dict=__lowercase						,				)[0]

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

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

        if output_type == "pil":
          __UpperCAmelCase    :						int       =						self.numpy_to_pil(__lowercase )

        if not return_dict:
          return (image,)

        return ImagePipelineOutput(images=__lowercase )






"""simple docstring"""





from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

lowercase__				:int				   =       TypeVar('T')




class 							snake_case							(			Generic[T] ):

      '''simple docstring'''



      def __init__(							self       :      Optional[Any]						,				__lowercase       :      list[T]						,				__lowercase       :      Callable[[T, T], T] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any | T       =						None

        __UpperCAmelCase    :						int       =						len(__lowercase )
        __UpperCAmelCase    :						list[T]       =						[any_type for _ in range(self.N )] + arr
        __UpperCAmelCase    :						List[Any]       =						fnc
        self.build()

      def       A_					(							self       :      str ):


        '''simple docstring'''




        for p in range(self.N - 1						,				0						,				-1 ):
          __UpperCAmelCase    :						Any       =						self.fn(self.st[p * 2]						,				self.st[p * 2 + 1] )

      def       A_					(							self       :      Union[str, Any]						,				__lowercase       :      int						,				__lowercase       :      T ):


        '''simple docstring'''




        p += self.N
        __UpperCAmelCase    :						Tuple       =						v
        while p > 1:
          __UpperCAmelCase    :						Union[str, Any]       =						p // 2
          __UpperCAmelCase    :						Any       =						self.fn(self.st[p * 2]						,				self.st[p * 2 + 1] )







      def       A_					(							self       :      str						,				__lowercase       :      int						,				__lowercase       :      int ):  # noqa: E741


        '''simple docstring'''




        __UpperCAmelCase       ,     __UpperCAmelCase    :						int       =						l + self.N, r + self.N

        __UpperCAmelCase    :						T | None       =						None
        while l <= r:
          if l % 2 == 1:
            __UpperCAmelCase    :						Any       =						self.st[l] if res is None else self.fn(__lowercase						,				self.st[l] )
          if r % 2 == 0:
            __UpperCAmelCase    :						Any       =						self.st[r] if res is None else self.fn(__lowercase						,				self.st[r] )
          __UpperCAmelCase       ,     __UpperCAmelCase    :						Any       =						(l + 1) // 2, (r - 1) // 2
        return res


if __name__ == "__main__":
  from functools import reduce

  lowercase__				:Any				   =       [1, 1_0, -2, 9, -3, 8, 4, -7, 5, 6, 1_1, -1_2]

  lowercase__				:List[Any]				   =       {
      0: 7,
      1: 2,
      2: 6,
      3: -1_4,
      4: 5,
      5: 4,
      6: 7,
      7: -1_0,
      8: 9,
      9: 1_0,
      1_0: 1_2,
      1_1: 1,
  }

  lowercase__				:List[str]				   =       SegmentTree(test_array, min)
  lowercase__				:List[str]				   =       SegmentTree(test_array, max)
  lowercase__				:Tuple				   =       SegmentTree(test_array, lambda a, b: a + b)


  def       lowerCamelCase_       (   )						->None:





    """simple docstring"""


    for i in range(len(UpperCAmelCase_  )  ):
      for j in range(UpperCAmelCase_  ,			len(UpperCAmelCase_  )  ):
        __UpperCAmelCase    :						Optional[Any]       =						reduce(UpperCAmelCase_  ,			test_array[i : j + 1]  )
        __UpperCAmelCase    :						Optional[int]       =						reduce(UpperCAmelCase_  ,			test_array[i : j + 1]  )
        __UpperCAmelCase    :						Union[str, Any]       =						reduce(lambda UpperCAmelCase_  ,			UpperCAmelCase_  : a + b  ,			test_array[i : j + 1]  )
        assert min_range == min_segment_tree.query(UpperCAmelCase_  ,			UpperCAmelCase_  )
        assert max_range == max_segment_tree.query(UpperCAmelCase_  ,			UpperCAmelCase_  )
        assert sum_range == sum_segment_tree.query(UpperCAmelCase_  ,			UpperCAmelCase_  )

  test_all_segments()

  for index, value in test_updates.items():
    lowercase__				:Optional[Any]				   =       value
    min_segment_tree.update(index, value)
    max_segment_tree.update(index, value)
    sum_segment_tree.update(index, value)
    test_all_segments()