




from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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 KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\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 = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\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\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        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\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     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_=8					)				->		List[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




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_=5_12    ,   lowercase_=5_12					)				->		List[str]:

    """simple docstring"""


    __UpperCamelCase     						=  pil_image.resize((w, h)    ,   resample=Image.BICUBIC    ,   reducing_gap=1					)
    __UpperCamelCase     						=  np.array(pil_image.convert('''RGB'''					)					)
    __UpperCamelCase     						=  arr.astype(np.floataa					) / 127.5 - 1
    __UpperCamelCase     						=  np.transpose(lowercase_    ,   [2, 0, 1]					)
    __UpperCamelCase     						=  torch.from_numpy(lowercase_					).unsqueeze(0					)
    return image


class        _lowerCamelCase  (	__UpperCAmelCase      ):

      """simple docstring"""

      def __init__(   self    :       Any       ,				snake_case    :       UNetaDConditionModel       ,				snake_case    :       DDPMScheduler       ,				snake_case    :       VQModel       ,				):
          super().__init__()

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







      def 					snake_case		(   self    :       Optional[Any]       ,				snake_case    :       List[str]       ,				snake_case    :       str       ,				snake_case    :       List[str] ):
          # get the original timestep using init_timestep
          __UpperCamelCase     						=  min(int(num_inference_steps * strength )       ,				_lowerCamelCase )

          __UpperCamelCase     						=  max(num_inference_steps - init_timestep       ,				0 )
          __UpperCamelCase     						=  self.scheduler.timesteps[t_start:]

          return timesteps, num_inference_steps - t_start







      def 					snake_case		(   self    :       int       ,				snake_case    :       Union[str, Any]       ,				snake_case    :       Union[str, Any]       ,				snake_case    :       int       ,				snake_case    :       Dict       ,				snake_case    :       List[str]       ,				snake_case    :       Optional[Any]       ,				snake_case    :       Union[str, Any]=None ):
          if not isinstance(_lowerCamelCase       ,				(torch.Tensor, PIL.Image.Image, list) ):
              raise ValueError(
                  F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_lowerCamelCase )}" )

          __UpperCamelCase     						=  image.to(device=_lowerCamelCase       ,				dtype=_lowerCamelCase )

          __UpperCamelCase     						=  batch_size * num_images_per_prompt

          if image.shape[1] == 4:
              __UpperCamelCase     						=  image

          else:
              if isinstance(_lowerCamelCase       ,				_lowerCamelCase ) and len(_lowerCamelCase ) != batch_size:
                  raise ValueError(
                      F"You have passed a list of generators of length {len(_lowerCamelCase )}, but requested an effective batch"
                      F" size of {batch_size}. Make sure the batch size matches the length of the generators." )

              elif isinstance(_lowerCamelCase       ,				_lowerCamelCase ):
                  __UpperCamelCase     						=  [
                      self.movq.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(_lowerCamelCase )
                  ]
                  __UpperCamelCase     						=  torch.cat(_lowerCamelCase       ,				dim=0 )
              else:
                  __UpperCamelCase     						=  self.movq.encode(_lowerCamelCase ).latent_dist.sample(_lowerCamelCase )

              __UpperCamelCase     						=  self.movq.config.scaling_factor * init_latents

          __UpperCamelCase     						=  torch.cat([init_latents]       ,				dim=0 )

          __UpperCamelCase     						=  init_latents.shape
          __UpperCamelCase     						=  randn_tensor(_lowerCamelCase       ,				generator=_lowerCamelCase       ,				device=_lowerCamelCase       ,				dtype=_lowerCamelCase )

          # get latents
          __UpperCamelCase     						=  self.scheduler.add_noise(_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase )

          __UpperCamelCase     						=  init_latents

          return latents







      def 					snake_case		(   self    :       int       ,				snake_case    :       Tuple=0 ):
          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(_lowerCamelCase       ,				_lowerCamelCase )







      def 					snake_case		(   self    :       str       ,				snake_case    :       Dict=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.''' )

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

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







      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def 					snake_case		(   self    :       List[str] ):
          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    :       Dict       ,				snake_case    :       Union[torch.FloatTensor, List[torch.FloatTensor]]       ,				snake_case    :       Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]       ,				snake_case    :       Union[torch.FloatTensor, List[torch.FloatTensor]]       ,				snake_case    :       torch.FloatTensor       ,				snake_case    :       int = 512       ,				snake_case    :       int = 512       ,				snake_case    :       int = 100       ,				snake_case    :       float = 4.0       ,				snake_case    :       float = 0.3       ,				snake_case    :       int = 1       ,				snake_case    :       Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,				snake_case    :       Optional[str] = "pil"       ,				snake_case    :       bool = True       ,				):
          __UpperCamelCase     						=  self._execution_device

          __UpperCamelCase     						=  guidance_scale > 1.0

          if isinstance(_lowerCamelCase       ,				_lowerCamelCase ):
              __UpperCamelCase     						=  torch.cat(_lowerCamelCase       ,				dim=0 )
          if isinstance(_lowerCamelCase       ,				_lowerCamelCase ):
              __UpperCamelCase     						=  torch.cat(_lowerCamelCase       ,				dim=0 )
          if isinstance(_lowerCamelCase       ,				_lowerCamelCase ):
              __UpperCamelCase     						=  torch.cat(_lowerCamelCase       ,				dim=0 )

          __UpperCamelCase     						=  image_embeds.shape[0]

          if do_classifier_free_guidance:
              __UpperCamelCase     						=  image_embeds.repeat_interleave(_lowerCamelCase       ,				dim=0 )
              __UpperCamelCase     						=  negative_image_embeds.repeat_interleave(_lowerCamelCase       ,				dim=0 )
              __UpperCamelCase     						=  hint.repeat_interleave(_lowerCamelCase       ,				dim=0 )

          __UpperCamelCase     						=  torch.cat([negative_image_embeds, image_embeds]       ,				dim=0 ).to(dtype=self.unet.dtype       ,				device=_lowerCamelCase )
          __UpperCamelCase     						=  torch.cat([hint, hint]       ,				dim=0 ).to(dtype=self.unet.dtype       ,				device=_lowerCamelCase )

          if not isinstance(_lowerCamelCase       ,				_lowerCamelCase ):
              __UpperCamelCase     						=  [image]
          if not all(isinstance(_lowerCamelCase       ,				(PIL.Image.Image, torch.Tensor) ) for i in image ):
              raise ValueError(
                  F"Input is in incorrect format: {[type(_lowerCamelCase ) for i in image]}. Currently, we only support  PIL image and pytorch tensor" )

          __UpperCamelCase     						=  torch.cat([prepare_image(_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase ) for i in image]       ,				dim=0 )
          __UpperCamelCase     						=  image.to(dtype=image_embeds.dtype       ,				device=_lowerCamelCase )

          __UpperCamelCase     						=  self.movq.encode(_lowerCamelCase )['''latents''']
          __UpperCamelCase     						=  latents.repeat_interleave(_lowerCamelCase       ,				dim=0 )
          self.scheduler.set_timesteps(_lowerCamelCase       ,				device=_lowerCamelCase )
          __UpperCamelCase     						=  self.get_timesteps(_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase )
          __UpperCamelCase     						=  timesteps[:1].repeat(batch_size * num_images_per_prompt )
          __UpperCamelCase     						=  downscale_height_and_width(_lowerCamelCase       ,				_lowerCamelCase       ,				self.movq_scale_factor )
          __UpperCamelCase     						=  self.prepare_latents(
              _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				image_embeds.dtype       ,				_lowerCamelCase       ,				_lowerCamelCase )
          for i, t in enumerate(self.progress_bar(_lowerCamelCase ) ):
              # 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=_lowerCamelCase       ,				timestep=_lowerCamelCase       ,				encoder_hidden_states=_lowerCamelCase       ,				added_cond_kwargs=_lowerCamelCase       ,				return_dict=_lowerCamelCase       ,				)[0]

              if do_classifier_free_guidance:
                  __UpperCamelCase     						=  noise_pred.split(latents.shape[1]       ,				dim=1 )
                  __UpperCamelCase     						=  noise_pred.chunk(2 )
                  __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     						=  noise_pred.split(latents.shape[1]       ,				dim=1 )

              # compute the previous noisy sample x_t -> x_t-1

              __UpperCamelCase     						=  self.scheduler.step(
                  _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				generator=_lowerCamelCase       ,				)[0]

          # post-processing
          __UpperCamelCase     						=  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     						=  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(_lowerCamelCase )

          if not return_dict:
              return (image,)

          return ImagePipelineOutput(images=_lowerCamelCase )













"""simple docstring"""





import math




def 		lowerCAmelCase_	(  lowercase_   :							int		):

 '''simple docstring'''






 if 1 < number < 4:
  # 2 and 3 are primes
  return True
 elif number < 2 or number % 2 == 0 or number % 3 == 0:
  # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
  return False

 # All primes number are in format of 6k +/- 1
 for i in range(5 ,      int(math.sqrt(lowercase_		) + 1		) ,      6		):
  if number % i == 0 or number % (i + 2) == 0:
   return False
 return True




def 		lowerCAmelCase_	(  lowercase_   :							int = 6008_5147_5143		):

 '''simple docstring'''






 try:
  __SCREAMING_SNAKE_CASE :			Any												=       int(lowercase_		)
 except (TypeError, ValueError):
  raise TypeError('''Parameter n must be int or castable to int.'''		)
 if n <= 0:
  raise ValueError('''Parameter n must be greater than or equal to one.'''		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       0
 if is_prime(lowercase_		):
  return n
 while n % 2 == 0:
  n //= 2
 if is_prime(lowercase_		):
  return n
 for i in range(3 ,      int(math.sqrt(lowercase_		)		) + 1 ,      2		):
  if n % i == 0:
   if is_prime(n // i		):
    __SCREAMING_SNAKE_CASE :			Optional[int]												=       n // i
    break
   elif is_prime(lowercase_		):
    __SCREAMING_SNAKE_CASE :			Any												=       i
 return max_number


if __name__ == "__main__":
    print(f'{solution() = }')

