
from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
   from .pipeline_kandinsky import KandinskyPipeline
   from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
   from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
   from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
   from .text_encoder import MultilingualCLIP





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,
)


UpperCAmelCase__		:     Optional[int]     			=      logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase__		:     Dict     			=      '\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__							(					a		,		a		,		a=8      )      ->   List[Any]:
   _A:  int					=       height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   _A:  str					=       width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor






def   lowerCamelCase__							(					a		,		a=5_12		,		a=5_12      )      ->   Dict:
   _A:  Union[str, Any]					=       pil_image.resize((w, h)		,		resample=Image.BICUBIC		,		reducing_gap=1      )
   _A:  Tuple					=       np.array(pil_image.convert('''RGB'''      )      )
   _A:  List[str]					=       arr.astype(np.floataa      ) / 127.5 - 1
   _A:  Tuple					=       np.transpose(a		,		[2, 0, 1]      )
   _A:  Any					=       torch.from_numpy(a      ).unsqueeze(0      )
   return image






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''


   def __init__(					self     :							int			,			lowerCAmelCase_     :							UNetaDConditionModel			,			lowerCAmelCase_     :							DDPMScheduler			,			lowerCAmelCase_     :							VQModel			,			):

      """simple docstring"""
      super().__init__()

      self.register_modules(
          unet=lowerCAmelCase_			,			scheduler=lowerCAmelCase_			,			movq=lowerCAmelCase_			,			)
      _A:  List[Any]					=       2 ** (len(self.movq.config.block_out_channels     ) - 1)


   def  __magic_name__				(					self     :							List[str]			,			lowerCAmelCase_     :							Union[str, Any]			,			lowerCAmelCase_     :							List[Any]			,			lowerCAmelCase_     :							Union[str, Any]     ):

      """simple docstring"""
      # get the original timestep using init_timestep
      _A:  Union[str, Any]					=       min(int(num_inference_steps * strength     )			,			lowerCAmelCase_     )

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

      return timesteps, num_inference_steps - t_start


   def  __magic_name__				(					self     :							List[str]			,			lowerCAmelCase_     :							List[str]			,			lowerCAmelCase_     :							str			,			lowerCAmelCase_     :							List[Any]			,			lowerCAmelCase_     :							List[Any]			,			lowerCAmelCase_     :							Tuple			,			lowerCAmelCase_     :							Optional[int]			,			lowerCAmelCase_     :							Optional[int]=None     ):

      """simple docstring"""
      if not isinstance(lowerCAmelCase_			,			(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(lowerCAmelCase_     )}"""     )

      _A:  Optional[int]					=       image.to(device=lowerCAmelCase_			,			dtype=lowerCAmelCase_     )

      _A:  Union[str, Any]					=       batch_size * num_images_per_prompt

      if image.shape[1] == 4:
         _A:  Optional[int]					=       image

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

         elif isinstance(lowerCAmelCase_			,			lowerCAmelCase_     ):
            _A:  List[Any]					=       [
                self.movq.encode(image[i : i + 1]     ).latent_dist.sample(generator[i]     ) for i in range(lowerCAmelCase_     )
            ]
            _A:  Optional[Any]					=       torch.cat(lowerCAmelCase_			,			dim=0     )
         else:
            _A:  Optional[int]					=       self.movq.encode(lowerCAmelCase_     ).latent_dist.sample(lowerCAmelCase_     )

         _A:  int					=       self.movq.config.scaling_factor * init_latents

      _A:  Optional[Any]					=       torch.cat([init_latents]			,			dim=0     )

      _A:  Any					=       init_latents.shape
      _A:  Optional[Any]					=       randn_tensor(lowerCAmelCase_			,			generator=lowerCAmelCase_			,			device=lowerCAmelCase_			,			dtype=lowerCAmelCase_     )

      # get latents
      _A:  Union[str, Any]					=       self.scheduler.add_noise(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_     )

      _A:  List[str]					=       init_latents

      return latents


   def  __magic_name__				(					self     :							Optional[int]			,			lowerCAmelCase_     :							Optional[int]=0     ):

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

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

      _A:  int					=       [
          self.unet,
          self.movq,
      ]
      for cpu_offloaded_model in models:
         if cpu_offloaded_model is not None:
            cpu_offload(lowerCAmelCase_			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Any			,			lowerCAmelCase_     :							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.'''     )

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

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

      _A:  int					=       None
      for cpu_offloaded_model in [self.unet, self.movq]:
         _A			,  _A:  List[Any]					=       cpu_offload_with_hook(lowerCAmelCase_			,			lowerCAmelCase_			,			prev_module_hook=lowerCAmelCase_     )

      # We'll offload the last model manually.
      _A:  Tuple					=       hook


   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      if not hasattr(self.unet			,			'''_hf_hook'''     ):
         return self.device
      for module in self.unet.modules():
         if (
             hasattr(lowerCAmelCase_			,			'''_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(lowerCAmelCase_     )
   def __call__(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Union[torch.FloatTensor, List[torch.FloatTensor]]			,			lowerCAmelCase_     :							Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]			,			lowerCAmelCase_     :							Union[torch.FloatTensor, List[torch.FloatTensor]]			,			lowerCAmelCase_     :							int = 5_1_2			,			lowerCAmelCase_     :							int = 5_1_2			,			lowerCAmelCase_     :							int = 1_0_0			,			lowerCAmelCase_     :							float = 4.0			,			lowerCAmelCase_     :							float = 0.3			,			lowerCAmelCase_     :							int = 1			,			lowerCAmelCase_     :							Optional[Union[torch.Generator, List[torch.Generator]]] = None			,			lowerCAmelCase_     :							Optional[str] = "pil"			,			lowerCAmelCase_     :							bool = True			,			):

      """simple docstring"""
      _A:  Any					=       self._execution_device

      _A:  Any					=       guidance_scale > 1.0

      if isinstance(lowerCAmelCase_			,			lowerCAmelCase_     ):
         _A:  Any					=       torch.cat(lowerCAmelCase_			,			dim=0     )
      _A:  int					=       image_embeds.shape[0]
      if isinstance(lowerCAmelCase_			,			lowerCAmelCase_     ):
         _A:  Dict					=       torch.cat(lowerCAmelCase_			,			dim=0     )

      if do_classifier_free_guidance:
         _A:  Any					=       image_embeds.repeat_interleave(lowerCAmelCase_			,			dim=0     )
         _A:  str					=       negative_image_embeds.repeat_interleave(lowerCAmelCase_			,			dim=0     )

      _A:  Dict					=       torch.cat([negative_image_embeds, image_embeds]			,			dim=0     ).to(dtype=self.unet.dtype			,			device=lowerCAmelCase_     )

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

      _A:  List[str]					=       torch.cat([prepare_image(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_     ) for i in image]			,			dim=0     )
      _A:  Tuple					=       image.to(dtype=image_embeds.dtype			,			device=lowerCAmelCase_     )

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

         _A:  str					=       {'''image_embeds''': image_embeds}
         _A:  Optional[int]					=       self.unet(
             sample=lowerCAmelCase_			,			timestep=lowerCAmelCase_			,			encoder_hidden_states=lowerCAmelCase_			,			added_cond_kwargs=lowerCAmelCase_			,			return_dict=lowerCAmelCase_			,			)[0]

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

         # compute the previous noisy sample x_t -> x_t-1
         _A:  Any					=       self.scheduler.step(
             lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			generator=lowerCAmelCase_			,			)[0]

      # post-processing
      _A:  Tuple					=       self.movq.decode(lowerCAmelCase_			,			force_not_quantize=lowerCAmelCase_     )['''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:  int					=       image * 0.5 + 0.5
         _A:  Any					=       image.clamp(0			,			1     )
         _A:  Any					=       image.cpu().permute(0			,			2			,			3			,			1     ).float().numpy()

      if output_type == "pil":
         _A:  Union[str, Any]					=       self.numpy_to_pil(lowerCAmelCase_     )

      if not return_dict:
         return (image,)

      return ImagePipelineOutput(images=lowerCAmelCase_     )



