


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

UpperCAmelCase_      : Optional[Any]  					=       '\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        SCREAMING_SNAKE_CASE_						(			__A :			str  ,    __A :			List[Any]  ,    __A :			int=8				)	->		Optional[Any]:

       """simple docstring"""

       a_					:		int							=  height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       a_					:		Optional[int]							=  width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      def __init__(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       UNetaDConditionModel     ,							SCREAMING_SNAKE_CASE__				:       DDPMScheduler     ,							SCREAMING_SNAKE_CASE__				:       VQModel     ,							)		->			Optional[int]:
             super().__init__()

             self.register_modules(
                 unet=SCREAMING_SNAKE_CASE__     ,							scheduler=SCREAMING_SNAKE_CASE__     ,							movq=SCREAMING_SNAKE_CASE__     ,							)
             a_					:		Optional[int]							=  2 ** (len(self.movq.config.block_out_channels     ) - 1)
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       str     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			Optional[Any]:
             if latents is None:
                    a_					:		Tuple							=  randn_tensor(SCREAMING_SNAKE_CASE__     ,							generator=SCREAMING_SNAKE_CASE__     ,							device=SCREAMING_SNAKE_CASE__     ,							dtype=SCREAMING_SNAKE_CASE__     )
             else:
                    if latents.shape != shape:
                           raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""     )
                    a_					:		Tuple							=  latents.to(SCREAMING_SNAKE_CASE__     )

             a_					:		int							=  latents * scheduler.init_noise_sigma
             return latents
      def        SCREAMING_SNAKE_CASE							(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=0     )		->			Any:
             if is_accelerate_available():
                    from accelerate import cpu_offload
             else:
                    raise ImportError('Please install accelerate via `pip install accelerate`'     )

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

             a_					:		Tuple							=  [
                 self.unet,
                 self.movq,
             ]
             for cpu_offloaded_model in models:
                    if cpu_offloaded_model is not None:
                           cpu_offload(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=0     )		->			List[str]:
             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.'     )

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

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

             a_					:		Any							=  None
             for cpu_offloaded_model in [self.unet, self.movq]:
                    a_							,		a_					:		Optional[int]							=  cpu_offload_with_hook(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							prev_module_hook=SCREAMING_SNAKE_CASE__     )

             # We'll offload the last model manually.
             a_					:		Union[str, Any]							=  hook
      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			List[str]:
             if not hasattr(self.unet     ,							'_hf_hook'     ):
                    return self.device
             for module in self.unet.modules():
                    if (
                        hasattr(SCREAMING_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(SCREAMING_SNAKE_CASE__     )
      def __call__(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Union[torch.FloatTensor, List[torch.FloatTensor]]     ,							SCREAMING_SNAKE_CASE__				:       Union[torch.FloatTensor, List[torch.FloatTensor]]     ,							SCREAMING_SNAKE_CASE__				:       torch.FloatTensor     ,							SCREAMING_SNAKE_CASE__				:       int = 5_1_2     ,							SCREAMING_SNAKE_CASE__				:       int = 5_1_2     ,							SCREAMING_SNAKE_CASE__				:       int = 1_0_0     ,							SCREAMING_SNAKE_CASE__				:       float = 4.0     ,							SCREAMING_SNAKE_CASE__				:       int = 1     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[torch.FloatTensor] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[str] = "pil"     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							)		->			Optional[int]:
             a_					:		Union[str, Any]							=  self._execution_device

             a_					:		int							=  guidance_scale > 1.0

             if isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ):
                    a_					:		List[Any]							=  torch.cat(SCREAMING_SNAKE_CASE__     ,							dim=0     )
             if isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ):
                    a_					:		str							=  torch.cat(SCREAMING_SNAKE_CASE__     ,							dim=0     )
             if isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ):
                    a_					:		List[str]							=  torch.cat(SCREAMING_SNAKE_CASE__     ,							dim=0     )

             a_					:		Union[str, Any]							=  image_embeds.shape[0] * num_images_per_prompt

             if do_classifier_free_guidance:
                    a_					:		int							=  image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__     ,							dim=0     )
                    a_					:		Optional[Any]							=  negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__     ,							dim=0     )
                    a_					:		Optional[Any]							=  hint.repeat_interleave(SCREAMING_SNAKE_CASE__     ,							dim=0     )

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

             a_					:		str							=  self.movq.config.latent_channels

             a_							,		a_					:		Optional[Any]							=  downscale_height_and_width(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							self.movq_scale_factor     )

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

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

                    a_					:		Dict							=  {'image_embeds': image_embeds, 'hint': hint}
                    a_					:		List[Any]							=  self.unet(
                        sample=SCREAMING_SNAKE_CASE__     ,							timestep=SCREAMING_SNAKE_CASE__     ,							encoder_hidden_states=SCREAMING_SNAKE_CASE__     ,							added_cond_kwargs=SCREAMING_SNAKE_CASE__     ,							return_dict=SCREAMING_SNAKE_CASE__     ,							)[0]

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

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

             if output_type == "pil":
                    a_					:		List[Any]							=  self.numpy_to_pil(SCREAMING_SNAKE_CASE__     )

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__     )






import flax.linen as nn
import jax
import jax.numpy as jnp
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int
      snake_case__				:     jnp.dtype        =						jnp.floataa
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			int:
             a_					:		Dict							=  nn.Conv(
                 self.out_channels     ,							kernel_size=(3, 3)     ,							strides=(1, 1)     ,							padding=((1, 1), (1, 1))     ,							dtype=self.dtype     ,							)

      def __call__(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     )		->			Optional[int]:
             a_							,		a_							,		a_							,		a_					:		Union[str, Any]							=  hidden_states.shape
             a_					:		List[str]							=  jax.image.resize(
                 SCREAMING_SNAKE_CASE__     ,							shape=(batch, height * 2, width * 2, channels)     ,							method='nearest'     ,							)
             a_					:		Any							=  self.conv(SCREAMING_SNAKE_CASE__     )
             return hidden_states
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int
      snake_case__				:     jnp.dtype        =						jnp.floataa
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Optional[int]:
             a_					:		Optional[int]							=  nn.Conv(
                 self.out_channels     ,							kernel_size=(3, 3)     ,							strides=(2, 2)     ,							padding=((1, 1), (1, 1))     ,							dtype=self.dtype     ,							)

      def __call__(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       int     )		->			Optional[int]:
             # pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
             # hidden_states = jnp.pad(hidden_states, pad_width=pad)
             a_					:		str							=  self.conv(SCREAMING_SNAKE_CASE__     )
             return hidden_states
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int
      snake_case__				:     int        =						None
      snake_case__				:     float        =						0.0
      snake_case__				:     bool        =						None
      snake_case__				:     jnp.dtype        =						jnp.floataa
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Dict:
             a_					:		List[str]							=  self.in_channels if self.out_channels is None else self.out_channels

             a_					:		Optional[int]							=  nn.GroupNorm(num_groups=3_2     ,							epsilon=1E-5     )
             a_					:		Any							=  nn.Conv(
                 SCREAMING_SNAKE_CASE__     ,							kernel_size=(3, 3)     ,							strides=(1, 1)     ,							padding=((1, 1), (1, 1))     ,							dtype=self.dtype     ,							)

             a_					:		Optional[int]							=  nn.Dense(SCREAMING_SNAKE_CASE__     ,							dtype=self.dtype     )

             a_					:		Union[str, Any]							=  nn.GroupNorm(num_groups=3_2     ,							epsilon=1E-5     )
             a_					:		int							=  nn.Dropout(self.dropout_prob     )
             a_					:		Optional[Any]							=  nn.Conv(
                 SCREAMING_SNAKE_CASE__     ,							kernel_size=(3, 3)     ,							strides=(1, 1)     ,							padding=((1, 1), (1, 1))     ,							dtype=self.dtype     ,							)

             a_					:		List[str]							=  self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

             a_					:		List[Any]							=  None
             if use_nin_shortcut:
                    a_					:		Union[str, Any]							=  nn.Conv(
                        SCREAMING_SNAKE_CASE__     ,							kernel_size=(1, 1)     ,							strides=(1, 1)     ,							padding='VALID'     ,							dtype=self.dtype     ,							)

      def __call__(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=True     )		->			int:
             a_					:		List[Any]							=  hidden_states
             a_					:		Any							=  self.norma(SCREAMING_SNAKE_CASE__     )
             a_					:		Any							=  nn.swish(SCREAMING_SNAKE_CASE__     )
             a_					:		Union[str, Any]							=  self.conva(SCREAMING_SNAKE_CASE__     )

             a_					:		int							=  self.time_emb_proj(nn.swish(SCREAMING_SNAKE_CASE__     )     )
             a_					:		List[str]							=  jnp.expand_dims(jnp.expand_dims(SCREAMING_SNAKE_CASE__     ,							1     )     ,							1     )
             a_					:		Optional[int]							=  hidden_states + temb

             a_					:		List[str]							=  self.norma(SCREAMING_SNAKE_CASE__     )
             a_					:		Tuple							=  nn.swish(SCREAMING_SNAKE_CASE__     )
             a_					:		Optional[int]							=  self.dropout(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             a_					:		List[Any]							=  self.conva(SCREAMING_SNAKE_CASE__     )

             if self.conv_shortcut is not None:
                    a_					:		List[str]							=  self.conv_shortcut(SCREAMING_SNAKE_CASE__     )

             return hidden_states + residual


