



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

UpperCAmelCase_		 =					"""
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to(\"cuda\")

        >>> prompt = \"A red cartoon frog, 4k\"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16
        ... )
        >>> pipe.to(\"cuda\")

        >>> init_image = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/frog.png\"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save(\"red_frog.png\")
        ```
"""

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Dict	,   _snake_case	:List[Any]	,   _snake_case	:str=8				)  ->     str:
   _A      =       height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   _A      =       width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Dict	,   _snake_case	:Tuple=512	,   _snake_case	:Any=512				)  ->     Optional[Any]:
   _A      =       pil_image.resize((w, h)	,   resample=Image.BICUBIC	,   reducing_gap=1				)
   _A      =       np.array(pil_image.convert('''RGB'''				)				)
   _A      =       arr.astype(np.floataa				) / 127.5 - 1
   _A      =       np.transpose(_snake_case	,   [2, 0, 1]				)
   _A      =       torch.from_numpy(_snake_case				).unsqueeze(0				)
   return image


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					Optional[Any]			,						__lowerCAmelCase					:					UNetaDConditionModel			,						__lowerCAmelCase					:					DDPMScheduler			,						__lowerCAmelCase					:					VQModel			,						)   ->	Tuple:
    super().__init__()

    self.register_modules(
        unet=__lowerCAmelCase			,						scheduler=__lowerCAmelCase			,						movq=__lowerCAmelCase			,						)
    _A      =       2 ** (len(self.movq.config.block_out_channels      ) - 1)

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					List[str]			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					List[str]      )   ->	Dict:
    # get the original timestep using init_timestep
    _A      =       min(int(num_inference_steps * strength      )			,						__lowerCAmelCase      )

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

    return timesteps, num_inference_steps - t_start

 def     snake_case_      (    self					:					int			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					Any			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Tuple			,						__lowerCAmelCase					:					Optional[Any]			,						__lowerCAmelCase					:					Optional[int]			,						__lowerCAmelCase					:					List[str]=None      )   ->	List[Any]:
    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      =       image.to(device=__lowerCAmelCase			,						dtype=__lowerCAmelCase      )

    _A      =       batch_size * num_images_per_prompt

    if image.shape[1] == 4:
       _A      =       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      =       [
              self.movq.encode(image[i : i + 1]      ).latent_dist.sample(generator[i]      ) for i in range(__lowerCAmelCase      )
          ]
          _A      =       torch.cat(__lowerCAmelCase			,						dim=0      )
       else:
          _A      =       self.movq.encode(__lowerCAmelCase      ).latent_dist.sample(__lowerCAmelCase      )

       _A      =       self.movq.config.scaling_factor * init_latents

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

    _A      =       init_latents.shape
    _A      =       randn_tensor(__lowerCAmelCase			,						generator=__lowerCAmelCase			,						device=__lowerCAmelCase			,						dtype=__lowerCAmelCase      )

    # get latents
    _A      =       self.scheduler.add_noise(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase      )

    _A      =       init_latents

    return latents

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					str=0      )   ->	Tuple:
    if is_accelerate_available():
       from accelerate import cpu_offload
    else:
       raise ImportError('''Please install accelerate via `pip install accelerate`'''      )

    _A      =       torch.device(f'''cuda:{gpu_id}'''      )

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

 def     snake_case_      (    self					:					int			,						__lowerCAmelCase					:					str=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      =       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      =       None
    for cpu_offloaded_model in [self.unet, self.movq]:
       _A  ,    _A      =       cpu_offload_with_hook(__lowerCAmelCase			,						__lowerCAmelCase			,						prev_module_hook=__lowerCAmelCase      )

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

 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def     snake_case_      (    self					:					Union[str, Any]      )   ->	Any:
    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					:					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_12			,						__lowerCAmelCase					:					int = 5_12			,						__lowerCAmelCase					:					int = 1_00			,						__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			,						)   ->	List[Any]:
    _A      =       self._execution_device

    _A      =       guidance_scale > 1.0

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

    if do_classifier_free_guidance:
       _A      =       image_embeds.repeat_interleave(__lowerCAmelCase			,						dim=0      )
       _A      =       negative_image_embeds.repeat_interleave(__lowerCAmelCase			,						dim=0      )

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

    if not isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ):
       _A      =       [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      =       torch.cat([prepare_image(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase      ) for i in image]			,						dim=0      )
    _A      =       image.to(dtype=image_embeds.dtype			,						device=__lowerCAmelCase      )

    _A      =       self.movq.encode(__lowerCAmelCase      )['''latents''']
    _A      =       latents.repeat_interleave(__lowerCAmelCase			,						dim=0      )
    self.scheduler.set_timesteps(__lowerCAmelCase			,						device=__lowerCAmelCase      )
    _A  ,    _A      =       self.get_timesteps(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase      )
    _A      =       timesteps[:1].repeat(batch_size * num_images_per_prompt      )
    _A  ,    _A      =       downscale_height_and_width(__lowerCAmelCase			,						__lowerCAmelCase			,						self.movq_scale_factor      )
    _A      =       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      =       torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents

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

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

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

    if output_type == "pil":
       _A      =       self.numpy_to_pil(__lowerCAmelCase      )

    if not return_dict:
       return (image,)

    return ImagePipelineOutput(images=__lowerCAmelCase      )







from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A				:       Optional[Any]      =  {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A				:       Optional[Any]      =  [
	    'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'VanForImageClassification',
	    'VanModel',
	    'VanPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_van import (
		    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    VanForImageClassification,
		    VanModel,
		    VanPreTrainedModel,
		)

else:
	import sys

	A				:       Union[str, Any]      =  _LazyModule(__name__, globals()['__file__'], _import_structure)





