

def        __lowerCAmelCase    (       _UpperCamelCase		:  List[str]	)		->							Optional[int]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			len(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			sum(_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			[[False for x in range(s + 1	)] for y in range(n + 1	)]

 for i in range(1		,							n + 1	):
  SCREAMING_SNAKE_CASE								=			True

 for i in range(1		,							s + 1	):
  SCREAMING_SNAKE_CASE								=			False

 for i in range(1		,							n + 1	):
  for j in range(1		,							s + 1	):
   SCREAMING_SNAKE_CASE								=			dp[i][j - 1]

   if arr[i - 1] <= j:
    SCREAMING_SNAKE_CASE								=			dp[i][j] or dp[i - 1][j - arr[i - 1]]

 for j in range(int(s / 2	)		,							-1		,							-1	):
  if dp[n][j] is True:
   SCREAMING_SNAKE_CASE								=			s - 2 * j
   break

 return diff




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

a_    :						str    		=      "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"A red cartoon frog, 4k\"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to(\"cuda\")\n\n        >>> init_image = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/frog.png\"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save(\"red_frog.png\")\n        ```\n"


def        __lowerCAmelCase    (       _UpperCamelCase		:  List[str]		,							_UpperCamelCase		:  Any		,							_UpperCamelCase		:  Any=8	)		->							List[str]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			height // scale_factor**2
 if height % scale_factor**2 != 0:
  new_height += 1
 SCREAMING_SNAKE_CASE								=			width // scale_factor**2
 if width % scale_factor**2 != 0:
  new_width += 1
 return new_height * scale_factor, new_width * scale_factor


def        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[int]		,							_UpperCamelCase		:  List[Any]=5_12		,							_UpperCamelCase		:  Union[str, Any]=5_12	)		->							Union[str, Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			pil_image.resize((w, h)		,							resample=Image.BICUBIC		,							reducing_gap=1	)
 SCREAMING_SNAKE_CASE								=			np.array(pil_image.convert('RGB'	)	)
 SCREAMING_SNAKE_CASE								=			arr.astype(np.floataa	) / 1_27.5 - 1
 SCREAMING_SNAKE_CASE								=			np.transpose(_UpperCamelCase		,							[2, 0, 1]	)
 SCREAMING_SNAKE_CASE								=			torch.from_numpy(_UpperCamelCase	).unsqueeze(0	)
 return image




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):

    def __init__(			self :      int							,    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__							,    )
     SCREAMING_SNAKE_CASE								=			2 ** (len(self.movq.config.block_out_channels    ) - 1)

    def UpperCamelCase			(			self :      Any							,    snake_case__ :      int							,    snake_case__ :      List[Any]							,    snake_case__ :      List[str]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			min(int(num_inference_steps * strength    )							,    snake_case__    )

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

     return timesteps, num_inference_steps - t_start

    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      Dict							,    snake_case__ :      int							,    snake_case__ :      Optional[int]							,    snake_case__ :      Optional[Any]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      Optional[int]							,    snake_case__ :      str=None    ):

     """simple docstring"""






     if not isinstance(snake_case__							,    (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(snake_case__    )}"""    )

     SCREAMING_SNAKE_CASE								=			image.to(device=snake_case__							,    dtype=snake_case__    )

     SCREAMING_SNAKE_CASE								=			batch_size * num_images_per_prompt

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

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

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

      SCREAMING_SNAKE_CASE								=			self.movq.config.scaling_factor * init_latents

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

     SCREAMING_SNAKE_CASE								=			init_latents.shape
     SCREAMING_SNAKE_CASE								=			randn_tensor(snake_case__							,    generator=snake_case__							,    device=snake_case__							,    dtype=snake_case__    )

     # get latents
     SCREAMING_SNAKE_CASE								=			self.scheduler.add_noise(snake_case__							,    snake_case__							,    snake_case__    )

     SCREAMING_SNAKE_CASE								=			init_latents

     return latents

    def UpperCamelCase			(			self :      int							,    snake_case__ :      List[str]=0    ):

     """simple docstring"""






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

     SCREAMING_SNAKE_CASE								=			torch.device(F"""cuda:{gpu_id}"""    )

     SCREAMING_SNAKE_CASE								=			[
         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[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.'    )

     SCREAMING_SNAKE_CASE								=			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)

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

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

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

     """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 :      str							,    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__ :      int = 5_1_2							,    snake_case__ :      int = 5_1_2							,    snake_case__ :      int = 1_0_0							,    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							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self._execution_device

     SCREAMING_SNAKE_CASE								=			guidance_scale > 1.0

     if isinstance(snake_case__							,    snake_case__    ):
      SCREAMING_SNAKE_CASE								=			torch.cat(snake_case__							,    dim=0    )
     SCREAMING_SNAKE_CASE								=			image_embeds.shape[0]
     if isinstance(snake_case__							,    snake_case__    ):
      SCREAMING_SNAKE_CASE								=			torch.cat(snake_case__							,    dim=0    )

     if do_classifier_free_guidance:
      SCREAMING_SNAKE_CASE								=			image_embeds.repeat_interleave(snake_case__							,    dim=0    )
      SCREAMING_SNAKE_CASE								=			negative_image_embeds.repeat_interleave(snake_case__							,    dim=0    )

     SCREAMING_SNAKE_CASE								=			torch.cat([negative_image_embeds, image_embeds]							,    dim=0    ).to(dtype=self.unet.dtype							,    device=snake_case__    )

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

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

     SCREAMING_SNAKE_CASE								=			self.movq.encode(snake_case__    )['latents']
     SCREAMING_SNAKE_CASE								=			latents.repeat_interleave(snake_case__							,    dim=0    )
     self.scheduler.set_timesteps(snake_case__							,    device=snake_case__    )
     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.get_timesteps(snake_case__							,    snake_case__							,    snake_case__    )
     SCREAMING_SNAKE_CASE								=			timesteps[:1].repeat(batch_size * num_images_per_prompt    )
     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			downscale_height_and_width(snake_case__							,    snake_case__							,    self.movq_scale_factor    )
     SCREAMING_SNAKE_CASE								=			self.prepare_latents(
         snake_case__							,    snake_case__							,    snake_case__							,    snake_case__							,    image_embeds.dtype							,    snake_case__							,    snake_case__    )
     for i, t in enumerate(self.progress_bar(snake_case__    )    ):
      # expand the latents if we are doing classifier free guidance
      SCREAMING_SNAKE_CASE								=			torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

      SCREAMING_SNAKE_CASE								=			{'image_embeds': image_embeds}
      SCREAMING_SNAKE_CASE								=			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:
       SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			noise_pred.split(latents.shape[1]							,    dim=1    )
       SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			noise_pred.chunk(2    )
       SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			variance_pred.chunk(2    )
       SCREAMING_SNAKE_CASE								=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
       SCREAMING_SNAKE_CASE								=			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						,	SCREAMING_SNAKE_CASE								=			noise_pred.split(latents.shape[1]							,    dim=1    )

      # compute the previous noisy sample x_t -> x_t-1
      SCREAMING_SNAKE_CASE								=			self.scheduler.step(
          snake_case__							,    snake_case__							,    snake_case__							,    generator=snake_case__							,    )[0]

     # post-processing
     SCREAMING_SNAKE_CASE								=			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"]:
      SCREAMING_SNAKE_CASE								=			image * 0.5 + 0.5
      SCREAMING_SNAKE_CASE								=			image.clamp(0							,    1    )
      SCREAMING_SNAKE_CASE								=			image.cpu().permute(0							,    2							,    3							,    1    ).float().numpy()

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

     if not return_dict:
      return (image,)

     return ImagePipelineOutput(images=snake_case__    )

