




import copy
import random

from transformers import CLIPTokenizer





class     lowerCamelCase__(					__lowerCamelCase):
      def __init__(				self: List[Any]    ,       *UpperCamelCase_: Optional[int]    ,       **UpperCamelCase_: List[str]   ):
             super().__init__(*UpperCamelCase_    ,       **UpperCamelCase_   )
             __lowerCamelCase   =						{}
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: Optional[int]    ,       *UpperCamelCase_: Dict    ,       **UpperCamelCase_: Tuple   ):
             __lowerCamelCase   =						super().add_tokens(UpperCamelCase_    ,       *UpperCamelCase_    ,       **UpperCamelCase_   )
             if num_added_tokens == 0:
                    raise ValueError(
                        F'The tokenizer already contains the token {placeholder_token}. Please pass a different'
                        """ `placeholder_token` that is not already in the tokenizer."""   )
      def 			lowerCAmelCase__						(				self: str    ,       UpperCamelCase_: Any    ,       *UpperCamelCase_: Any    ,       UpperCamelCase_: Optional[Any]=1    ,       **UpperCamelCase_: Optional[Any]   ):
             __lowerCamelCase   =						[]
             if num_vec_per_token == 1:
                    self.try_adding_tokens(UpperCamelCase_    ,       *UpperCamelCase_    ,       **UpperCamelCase_   )
                    output.append(UpperCamelCase_   )
             else:
                    __lowerCamelCase   =						[]
                    for i in range(UpperCamelCase_   ):
                           __lowerCamelCase   =						placeholder_token + F'_{i}'
                           self.try_adding_tokens(UpperCamelCase_    ,       *UpperCamelCase_    ,       **UpperCamelCase_   )
                           output.append(UpperCamelCase_   )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
             for token in self.token_map:
                    if token in placeholder_token:
                           raise ValueError(
                               F'The tokenizer already has placeholder token {token} that can get confused with'
                               F' {placeholder_token}keep placeholder tokens independent'   )
             __lowerCamelCase   =						output
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: str    ,       UpperCamelCase_: List[str]=False    ,       UpperCamelCase_: Optional[Any]=1.0   ):
             if isinstance(UpperCamelCase_    ,       UpperCamelCase_   ):
                    __lowerCamelCase   =						[]
                    for i in range(len(UpperCamelCase_   )   ):
                           output.append(self.replace_placeholder_tokens_in_text(text[i]    ,       vector_shuffle=UpperCamelCase_   )   )
                    return output
             for placeholder_token in self.token_map:
                    if placeholder_token in text:
                           __lowerCamelCase   =						self.token_map[placeholder_token]
                           __lowerCamelCase   =						tokens[: 1 + int(len(UpperCamelCase_   ) * prop_tokens_to_load   )]
                           if vector_shuffle:
                                  __lowerCamelCase   =						copy.copy(UpperCamelCase_   )
                                  random.shuffle(UpperCamelCase_   )
                           __lowerCamelCase   =						text.replace(UpperCamelCase_    ,       """ """.join(UpperCamelCase_   )   )
             return text
      def __call__(				self: str    ,       UpperCamelCase_: Optional[Any]    ,       *UpperCamelCase_: Optional[int]    ,       UpperCamelCase_: List[str]=False    ,       UpperCamelCase_: List[str]=1.0    ,       **UpperCamelCase_: Tuple   ):
             return super().__call__(
                 self.replace_placeholder_tokens_in_text(
                     UpperCamelCase_    ,       vector_shuffle=UpperCamelCase_    ,       prop_tokens_to_load=UpperCamelCase_   )    ,       *UpperCamelCase_    ,       **UpperCamelCase_    ,       )




      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: List[Any]    ,       *UpperCamelCase_: Optional[int]    ,       UpperCamelCase_: str=False    ,       UpperCamelCase_: int=1.0    ,       **UpperCamelCase_: List[Any]   ):
             return super().encode(
                 self.replace_placeholder_tokens_in_text(
                     UpperCamelCase_    ,       vector_shuffle=UpperCamelCase_    ,       prop_tokens_to_load=UpperCamelCase_   )    ,       *UpperCamelCase_    ,       **UpperCamelCase_    ,       )







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,
)


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

UpperCAmelCase_									=	'\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__       (						A__       :					Optional[int]  ,			A__       :					Dict  ,			A__       :					Optional[int]=8	):





       '''simple docstring'''



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





class     lowerCamelCase__(					__lowerCamelCase):
      def __init__(				self: List[Any]    ,       UpperCamelCase_: UNetaDConditionModel    ,       UpperCamelCase_: DDPMScheduler    ,       UpperCamelCase_: VQModel    ,       ):
             super().__init__()

             self.register_modules(
                 unet=UpperCamelCase_    ,       scheduler=UpperCamelCase_    ,       movq=UpperCamelCase_    ,       )
             __lowerCamelCase   =						2 ** (len(self.movq.config.block_out_channels   ) - 1)
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: Any    ,       UpperCamelCase_: Tuple    ,       UpperCamelCase_: int    ,       UpperCamelCase_: Dict    ,       UpperCamelCase_: Dict    ,       UpperCamelCase_: int   ):
             if latents is None:
                    __lowerCamelCase   =						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   =						latents.to(UpperCamelCase_   )

             __lowerCamelCase   =						latents * scheduler.init_noise_sigma
             return latents
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: str=0   ):
             if is_accelerate_available():
                    from accelerate import cpu_offload
             else:
                    raise ImportError("""Please install accelerate via `pip install accelerate`"""   )

             __lowerCamelCase   =						torch.device(F'cuda:{gpu_id}'   )

             __lowerCamelCase   =						[
                 self.unet,
                 self.movq,
             ]
             for cpu_offloaded_model in models:
                    if cpu_offloaded_model is not None:
                           cpu_offload(UpperCamelCase_    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: Optional[int]=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   =						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   =						None
             for cpu_offloaded_model in [self.unet, self.movq]:
                    __lowerCamelCase,   __lowerCamelCase   =						cpu_offload_with_hook(UpperCamelCase_    ,       UpperCamelCase_    ,       prev_module_hook=UpperCamelCase_   )

             # We'll offload the last model manually.
             __lowerCamelCase   =						hook
      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def 			lowerCAmelCase__						(				self: int   ):
             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: Tuple    ,       UpperCamelCase_: Union[torch.FloatTensor, List[torch.FloatTensor]]    ,       UpperCamelCase_: Union[torch.FloatTensor, List[torch.FloatTensor]]    ,       UpperCamelCase_: torch.FloatTensor    ,       UpperCamelCase_: int = 5_12    ,       UpperCamelCase_: int = 5_12    ,       UpperCamelCase_: int = 1_00    ,       UpperCamelCase_: float = 4.0    ,       UpperCamelCase_: int = 1    ,       UpperCamelCase_: Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,       UpperCamelCase_: Optional[torch.FloatTensor] = None    ,       UpperCamelCase_: Optional[str] = "pil"    ,       UpperCamelCase_: bool = True    ,       ):
             __lowerCamelCase   =						self._execution_device

             __lowerCamelCase   =						guidance_scale > 1.0

             if isinstance(UpperCamelCase_    ,       UpperCamelCase_   ):
                    __lowerCamelCase   =						torch.cat(UpperCamelCase_    ,       dim=0   )
             if isinstance(UpperCamelCase_    ,       UpperCamelCase_   ):
                    __lowerCamelCase   =						torch.cat(UpperCamelCase_    ,       dim=0   )
             if isinstance(UpperCamelCase_    ,       UpperCamelCase_   ):
                    __lowerCamelCase   =						torch.cat(UpperCamelCase_    ,       dim=0   )

             __lowerCamelCase   =						image_embeds.shape[0] * num_images_per_prompt

             if do_classifier_free_guidance:
                    __lowerCamelCase   =						image_embeds.repeat_interleave(UpperCamelCase_    ,       dim=0   )
                    __lowerCamelCase   =						negative_image_embeds.repeat_interleave(UpperCamelCase_    ,       dim=0   )
                    __lowerCamelCase   =						hint.repeat_interleave(UpperCamelCase_    ,       dim=0   )

             __lowerCamelCase   =						torch.cat([negative_image_embeds, image_embeds]    ,       dim=0   ).to(dtype=self.unet.dtype    ,       device=UpperCamelCase_   )
             __lowerCamelCase   =						torch.cat([hint, hint]    ,       dim=0   ).to(dtype=self.unet.dtype    ,       device=UpperCamelCase_   )
             self.scheduler.set_timesteps(UpperCamelCase_    ,       device=UpperCamelCase_   )
             __lowerCamelCase   =						self.scheduler.timesteps

             __lowerCamelCase   =						self.movq.config.latent_channels

             __lowerCamelCase,   __lowerCamelCase   =						downscale_height_and_width(UpperCamelCase_    ,       UpperCamelCase_    ,       self.movq_scale_factor   )

             # create initial latent
             __lowerCamelCase   =						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   =						torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

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

                    # compute the previous noisy sample x_t -> x_t-1
                    __lowerCamelCase   =						self.scheduler.step(
                        UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       generator=UpperCamelCase_    ,       )[0]
             # post-processing
             __lowerCamelCase   =						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   =						image * 0.5 + 0.5
                    __lowerCamelCase   =						image.clamp(0    ,       1   )
                    __lowerCamelCase   =						image.cpu().permute(0    ,       2    ,       3    ,       1   ).float().numpy()

             if output_type == "pil":
                    __lowerCamelCase   =						self.numpy_to_pil(UpperCamelCase_   )

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=UpperCamelCase_   )

