
'''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,
)


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

SCREAMING_SNAKE_CASE     =       """
    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 snake_case_     (					lowercase__   ,       lowercase__   ,       lowercase__=8		):
  UpperCAmelCase__      :					int				       =							height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  UpperCAmelCase__      :					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_							(    A				):

 '''simple docstring'''







 def __init__(					self		:  Any			,				snake_case__		:  UNetaDConditionModel			,				snake_case__		:  DDPMScheduler			,				snake_case__		:  VQModel			,				):



   '''simple docstring'''




   super().__init__()

   self.register_modules(
       unet=snake_case__			,				scheduler=snake_case__			,				movq=snake_case__			,				)
   UpperCAmelCase__      :					Tuple				       =							2 ** (len(self.movq.config.block_out_channels ) - 1)







 def 							UpperCamelCase  (					self		:  Union[str, Any]			,				snake_case__		:  Union[str, Any]			,				snake_case__		:  Dict			,				snake_case__		:  Tuple			,				snake_case__		:  List[Any]			,				snake_case__		:  Union[str, Any]			,				snake_case__		:  List[str] ):



   '''simple docstring'''




   if latents is None:
     UpperCAmelCase__      :					str				       =							randn_tensor(snake_case__			,				generator=snake_case__			,				device=snake_case__			,				dtype=snake_case__ )
   else:
     if latents.shape != shape:
       raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
     UpperCAmelCase__      :					List[Any]				       =							latents.to(snake_case__ )

   UpperCAmelCase__      :					Any				       =							latents * scheduler.init_noise_sigma
   return latents







 def 							UpperCamelCase  (					self		:  Dict			,				snake_case__		:  Dict=0 ):



   '''simple docstring'''




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

   UpperCAmelCase__      :					List[str]				       =							torch.device(F"""cuda:{gpu_id}""" )

   UpperCAmelCase__      :					Any				       =							[
       self.unet,
       self.movq,
   ]
   for cpu_offloaded_model in models:
     if cpu_offloaded_model is not None:
       cpu_offload(snake_case__			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  Union[str, Any]			,				snake_case__		:  Optional[Any]=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__      :					str				       =							torch.device(F"""cuda:{gpu_id}""" )

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

   UpperCAmelCase__      :					Optional[int]				       =							None
   for cpu_offloaded_model in [self.unet, self.movq]:
     UpperCAmelCase__       ,			UpperCAmelCase__      :					List[str]				       =							cpu_offload_with_hook(snake_case__			,				snake_case__			,				prev_module_hook=snake_case__ )

   # We'll offload the last model manually.
   UpperCAmelCase__      :					List[Any]				       =							hook







 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 							UpperCamelCase  (					self		:  Optional[int] ):



   '''simple docstring'''




   if not hasattr(self.unet			,				"_hf_hook" ):
     return self.device
   for module in self.unet.modules():
     if (
         hasattr(snake_case__			,				"_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(snake_case__ )
 def __call__(					self		:  List[str]			,				snake_case__		:  Union[torch.FloatTensor, List[torch.FloatTensor]]			,				snake_case__		:  Union[torch.FloatTensor, List[torch.FloatTensor]]			,				snake_case__		:  torch.FloatTensor			,				snake_case__		:  int = 5_12			,				snake_case__		:  int = 5_12			,				snake_case__		:  int = 1_00			,				snake_case__		:  float = 4.0			,				snake_case__		:  int = 1			,				snake_case__		:  Optional[Union[torch.Generator, List[torch.Generator]]] = None			,				snake_case__		:  Optional[torch.FloatTensor] = None			,				snake_case__		:  Optional[str] = "pil"			,				snake_case__		:  bool = True			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					str				       =							self._execution_device

   UpperCAmelCase__      :					Dict				       =							guidance_scale > 1.0

   if isinstance(snake_case__			,				snake_case__ ):
     UpperCAmelCase__      :					List[str]				       =							torch.cat(snake_case__			,				dim=0 )
   if isinstance(snake_case__			,				snake_case__ ):
     UpperCAmelCase__      :					List[Any]				       =							torch.cat(snake_case__			,				dim=0 )
   if isinstance(snake_case__			,				snake_case__ ):
     UpperCAmelCase__      :					Tuple				       =							torch.cat(snake_case__			,				dim=0 )

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

   if do_classifier_free_guidance:
     UpperCAmelCase__      :					Dict				       =							image_embeds.repeat_interleave(snake_case__			,				dim=0 )
     UpperCAmelCase__      :					Tuple				       =							negative_image_embeds.repeat_interleave(snake_case__			,				dim=0 )
     UpperCAmelCase__      :					str				       =							hint.repeat_interleave(snake_case__			,				dim=0 )

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

   UpperCAmelCase__      :					Tuple				       =							self.movq.config.latent_channels

   UpperCAmelCase__       ,			UpperCAmelCase__      :					int				       =							downscale_height_and_width(snake_case__			,				snake_case__			,				self.movq_scale_factor )

   # create initial latent
   UpperCAmelCase__      :					Tuple				       =							self.prepare_latents(
       (batch_size, num_channels_latents, height, width)			,				image_embeds.dtype			,				snake_case__			,				snake_case__			,				snake_case__			,				self.scheduler			,				)

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

     UpperCAmelCase__      :					Optional[int]				       =							{"image_embeds": image_embeds, "hint": hint}
     UpperCAmelCase__      :					Dict				       =							self.unet(
         sample=snake_case__			,				timestep=snake_case__			,				encoder_hidden_states=snake_case__			,				added_cond_kwargs=snake_case__			,				return_dict=snake_case__			,				)[0]

     if do_classifier_free_guidance:
       UpperCAmelCase__       ,			UpperCAmelCase__      :					Any				       =							noise_pred.split(latents.shape[1]			,				dim=1 )
       UpperCAmelCase__       ,			UpperCAmelCase__      :					List[Any]				       =							noise_pred.chunk(2 )
       UpperCAmelCase__       ,			UpperCAmelCase__      :					Dict				       =							variance_pred.chunk(2 )
       UpperCAmelCase__      :					int				       =							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__      :					Any				       =							noise_pred.split(latents.shape[1]			,				dim=1 )

     # compute the previous noisy sample x_t -> x_t-1
     UpperCAmelCase__      :					List[str]				       =							self.scheduler.step(
         snake_case__			,				snake_case__			,				snake_case__			,				generator=snake_case__			,				)[0]
   # post-processing
   UpperCAmelCase__      :					Any				       =							self.movq.decode(snake_case__			,				force_not_quantize=snake_case__ )["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__      :					Optional[int]				       =							image.clamp(0			,				1 )
     UpperCAmelCase__      :					List[Any]				       =							image.cpu().permute(0			,				2			,				3			,				1 ).float().numpy()

   if output_type == "pil":
     UpperCAmelCase__      :					List[Any]				       =							self.numpy_to_pil(snake_case__ )

   if not return_dict:
     return (image,)

   return ImagePipelineOutput(images=snake_case__ )





'''simple docstring'''





from math import factorial

SCREAMING_SNAKE_CASE     =       {str(digit): factorial(digit) for digit in range(1_0)}







def snake_case_     (					lowercase__		):
  if not isinstance(lowercase__   ,       lowercase__		):
    raise TypeError("Parameter number must be int"		)

  if number < 0:
    raise ValueError("Parameter number must be greater than or equal to 0"		)

  # Converts number in string to iterate on its digits and adds its factorial.
  return sum(DIGIT_FACTORIAL[digit] for digit in str(lowercase__		)		)







def snake_case_     (					lowercase__ = 6_0   ,       lowercase__ = 1_0_0_0_0_0_0		):

  if not isinstance(lowercase__   ,       lowercase__		) or not isinstance(lowercase__   ,       lowercase__		):
    raise TypeError("Parameters chain_length and number_limit must be int"		)

  if chain_length <= 0 or number_limit <= 0:
    raise ValueError(
        "Parameters chain_length and number_limit must be greater than 0"		)

  # the counter for the chains with the exact desired length
  UpperCAmelCase__      :					Union[str, Any]				       =							0
  # the cached sizes of the previous chains
  UpperCAmelCase__      :					dict[int, int]				       =							{}

  for start_chain_element in range(1   ,       lowercase__		):
    # The temporary set will contain the elements of the chain
    UpperCAmelCase__      :					Any				       =							set()
    UpperCAmelCase__      :					int				       =							0

    # Stop computing the chain when you find a cached size, a repeating item or the
    # length is greater then the desired one.
    UpperCAmelCase__      :					List[str]				       =							start_chain_element
    while (
        chain_element not in chain_sets_lengths
        and chain_element not in chain_set
        and chain_set_length <= chain_length
    ):
      chain_set.add(lowercase__		)
      chain_set_length += 1
      UpperCAmelCase__      :					List[str]				       =							digit_factorial_sum(lowercase__		)

    if chain_element in chain_sets_lengths:
      chain_set_length += chain_sets_lengths[chain_element]

    UpperCAmelCase__      :					List[str]				       =							chain_set_length

    # If chain contains the exact amount of elements increase the counter
    if chain_set_length == chain_length:
      chains_counter += 1

  return chains_counter


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(F'{solution()}')



