




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,
)


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

lowerCAmelCase_					    = """
    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      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase=8      )     ->	Optional[int]:
  lowerCAmelCase__				:  Dict		       =     height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  lowerCAmelCase__				:  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  _lowerCAmelCase     (  _lowercase	):

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				):
        super().__init__()

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

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        if latents is None:
          lowerCAmelCase__				:  List[Any]		       =     randn_tensor(__UpperCAmelCase      ,				generator=__UpperCAmelCase      ,				device=__UpperCAmelCase      ,				dtype=__UpperCAmelCase		)
        else:
          if latents.shape != shape:
            raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""		)
          lowerCAmelCase__				:  Tuple		       =     latents.to(__UpperCAmelCase		)

        lowerCAmelCase__				:  int		       =     latents * scheduler.init_noise_sigma
        return latents

      def 	__magic_name__(   self      ,				__UpperCAmelCase=0		):
        if is_accelerate_available():
          from accelerate import cpu_offload
        else:
          raise ImportError('''Please install accelerate via `pip install accelerate`'''		)

        lowerCAmelCase__				:  Union[str, Any]		       =     torch.device(f"""cuda:{gpu_id}"""		)

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

      def 	__magic_name__(   self      ,				__UpperCAmelCase=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.'''		)

        lowerCAmelCase__				:  List[Any]		       =     torch.device(f"""cuda:{gpu_id}"""		)

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

        lowerCAmelCase__				:  Tuple		       =     None
        for cpu_offloaded_model in [self.unet, self.movq]:
          lowerCAmelCase__       ,			lowerCAmelCase__				:  List[Any]		       =     cpu_offload_with_hook(__UpperCAmelCase      ,				__UpperCAmelCase      ,				prev_module_hook=__UpperCAmelCase		)

        # We'll offload the last model manually.
        lowerCAmelCase__				:  Optional[int]		       =     hook

      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def 	__magic_name__(   self		):
        if not hasattr(self.unet      ,				'''_hf_hook'''		):
          return self.device
        for module in self.unet.modules():
          if (
              hasattr(__UpperCAmelCase      ,				'''_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(__UpperCAmelCase		)
      def __call__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase = 512      ,				__UpperCAmelCase = 512      ,				__UpperCAmelCase = 100      ,				__UpperCAmelCase = 4.0      ,				__UpperCAmelCase = 1      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = "pil"      ,				__UpperCAmelCase = True      ,				):
        lowerCAmelCase__				:  Union[str, Any]		       =     self._execution_device

        lowerCAmelCase__				:  str		       =     guidance_scale > 1.0

        if isinstance(__UpperCAmelCase      ,				__UpperCAmelCase		):
          lowerCAmelCase__				:  Tuple		       =     torch.cat(__UpperCAmelCase      ,				dim=0		)
        if isinstance(__UpperCAmelCase      ,				__UpperCAmelCase		):
          lowerCAmelCase__				:  Union[str, Any]		       =     torch.cat(__UpperCAmelCase      ,				dim=0		)
        if isinstance(__UpperCAmelCase      ,				__UpperCAmelCase		):
          lowerCAmelCase__				:  Union[str, Any]		       =     torch.cat(__UpperCAmelCase      ,				dim=0		)

        lowerCAmelCase__				:  str		       =     image_embeds.shape[0] * num_images_per_prompt

        if do_classifier_free_guidance:
          lowerCAmelCase__				:  Union[str, Any]		       =     image_embeds.repeat_interleave(__UpperCAmelCase      ,				dim=0		)
          lowerCAmelCase__				:  Dict		       =     negative_image_embeds.repeat_interleave(__UpperCAmelCase      ,				dim=0		)
          lowerCAmelCase__				:  int		       =     hint.repeat_interleave(__UpperCAmelCase      ,				dim=0		)

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

        lowerCAmelCase__				:  Optional[Any]		       =     self.movq.config.latent_channels

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

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

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

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

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

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

        if output_type == "pil":
          lowerCAmelCase__				:  Tuple		       =     self.numpy_to_pil(__UpperCAmelCase		)

        if not return_dict:
          return (image,)

        return ImagePipelineOutput(images=__UpperCAmelCase		)







lowerCAmelCase_					    = 6_55_21



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	int:
  lowerCAmelCase__				:  List[str]		       =     1
  lowerCAmelCase__				:  List[Any]		       =     0
  for plain_chr in plain_text:
    lowerCAmelCase__				:  Union[str, Any]		       =     (a + ord(UpperCamelCase      )) % MOD_ADLER
    lowerCAmelCase__				:  Optional[Any]		       =     (b + a) % MOD_ADLER
  return (b << 16) | a

