





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,
)


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

a__							:							List[str]			='''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> 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 = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.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)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
'''
def     lowercase__       (       __lowercase						:  int    ,  __lowercase						:  List[Any]    ,  __lowercase						:  Tuple=8					)			->				Any:



       """simple docstring"""






       __UpperCamelCase	=  height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       __UpperCamelCase	=  width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor
def     lowercase__       (       __lowercase						:  List[str]    ,  __lowercase						:  Optional[Any]=512    ,  __lowercase						:  Any=512					)			->				Optional[Any]:



       """simple docstring"""






       __UpperCamelCase	=  pil_image.resize((w, h)    ,  resample=Image.BICUBIC    ,  reducing_gap=1					)
       __UpperCamelCase	=  np.array(pil_image.convert('RGB'					)					)
       __UpperCamelCase	=  arr.astype(np.floataa					) / 1_2_7.5 - 1
       __UpperCamelCase	=  np.transpose(__lowercase    ,  [2, 0, 1]					)
       __UpperCamelCase	=  torch.from_numpy(__lowercase					).unsqueeze(0					)
       return image


class 					snake_case					(   lowerCAmelCase__	):




 """simple docstring"""



 def __init__(	self	:							List[str]							,     __A	:							UNetaDConditionModel							,     __A	:							DDPMScheduler							,     __A	:							VQModel							,     ):
        super().__init__()

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






 def 	_lowerCamelCase (	self	:							List[str]							,     __A	:							Dict							,     __A	:							int							,     __A	:							Tuple     ):
        __UpperCamelCase	=  min(int(num_inference_steps * strength     )							,     _A     )

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

        return timesteps, num_inference_steps - t_start






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




        if not isinstance(_A							,     (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(_A     )}'''     )

        __UpperCamelCase	=  image.to(device=_A							,     dtype=_A     )

        __UpperCamelCase	=  batch_size * num_images_per_prompt

        if image.shape[1] == 4:
               __UpperCamelCase	=  image

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

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

               __UpperCamelCase	=  self.movq.config.scaling_factor * init_latents

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

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

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

        __UpperCamelCase	=  init_latents

        return latents






 def 	_lowerCamelCase (	self	:							Union[str, Any]							,     __A	:							str=0     ):




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

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

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






 def 	_lowerCamelCase (	self	:							Optional[int]							,     __A	:							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.'     )

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

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

        __UpperCamelCase	=  None
        for cpu_offloaded_model in [self.unet, self.movq]:
               __UpperCamelCase	=  cpu_offload_with_hook(_A							,     _A							,     prev_module_hook=_A     )

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






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




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

        __UpperCamelCase	=  guidance_scale > 1.0

        if isinstance(_A							,     _A     ):
               __UpperCamelCase	=  torch.cat(_A							,     dim=0     )
        if isinstance(_A							,     _A     ):
               __UpperCamelCase	=  torch.cat(_A							,     dim=0     )
        if isinstance(_A							,     _A     ):
               __UpperCamelCase	=  torch.cat(_A							,     dim=0     )

        __UpperCamelCase	=  image_embeds.shape[0]

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

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

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

        __UpperCamelCase	=  torch.cat([prepare_image(_A							,     _A							,     _A     ) for i in image]							,     dim=0     )
        __UpperCamelCase	=  image.to(dtype=image_embeds.dtype							,     device=_A     )

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

               __UpperCamelCase	=  {'''image_embeds''': image_embeds, '''hint''': hint}
               __UpperCamelCase	=  self.unet(
                   sample=_A							,     timestep=_A							,     encoder_hidden_states=_A							,     added_cond_kwargs=_A							,     return_dict=_A							,     )[0]

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

               # compute the previous noisy sample x_t -> x_t-1

               __UpperCamelCase	=  self.scheduler.step(
                   _A							,     _A							,     _A							,     generator=_A							,     )[0]

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

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

        if not return_dict:
               return (image,)

        return ImagePipelineOutput(images=_A     )







'''simple docstring'''
a__							:							int			='''
# Transformers installation
! pip install transformers datasets evaluate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

a__							:							List[Any]			=[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
a__							:							Dict			={
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}
