
'''simple docstring'''




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,
)


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

lowercase			:				str    		=			'\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_				(   snake_case__		,				snake_case__		,				snake_case__=8						):




    '''simple docstring'''



    A       :   int					=						height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    A       :   Union[str, Any]					=						width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__=512		,				snake_case__=512						):




    '''simple docstring'''



    A       :   Optional[Any]					=						pil_image.resize((w, h)		,				resample=Image.BICUBIC		,				reducing_gap=1						)
    A       :   str					=						np.array(pil_image.convert('''RGB'''						)						)
    A       :   Union[str, Any]					=						arr.astype(np.floataa						) / 1_27.5 - 1
    A       :   Tuple					=						np.transpose(snake_case__		,				[2, 0, 1]						)
    A       :   List[str]					=						torch.from_numpy(snake_case__						).unsqueeze(0						)
    return image







class  A				(							__snake_case						):





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			) ->				Any:






         """simple docstring"""






         super().__init__()

         self.register_modules(
             unet=SCREAMING_SNAKE_CASE						,			scheduler=SCREAMING_SNAKE_CASE						,			movq=SCREAMING_SNAKE_CASE						,			)
         A       :   Dict					=						2 ** (len(self.movq.config.block_out_channels	) - 1)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   Any					=						min(int(num_inference_steps * strength	)						,			SCREAMING_SNAKE_CASE	)

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

         return timesteps, num_inference_steps - t_start





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=None	) ->				List[Any]:






         """simple docstring"""






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

         A       :   str					=						image.to(device=SCREAMING_SNAKE_CASE						,			dtype=SCREAMING_SNAKE_CASE	)

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

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

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

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

             A       :   str					=						self.movq.config.scaling_factor * init_latents

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

         A       :   Tuple					=						init_latents.shape
         A       :   str					=						randn_tensor(SCREAMING_SNAKE_CASE						,			generator=SCREAMING_SNAKE_CASE						,			device=SCREAMING_SNAKE_CASE						,			dtype=SCREAMING_SNAKE_CASE	)

         # get latents
         A       :   str					=						self.scheduler.add_noise(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         A       :   Dict					=						init_latents

         return latents





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE=0	) ->				List[Any]:






         """simple docstring"""






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

         A       :   List[Any]					=						torch.device(F'cuda:{gpu_id}'	)

         A       :   Optional[int]					=						[
             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 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE=0	) ->				Tuple:






         """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       :   Dict					=						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       :   Union[str, Any]					=						None
         for cpu_offloaded_model in [self.unet, self.movq]:
             A,  A       :   str					=						cpu_offload_with_hook(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			prev_module_hook=SCREAMING_SNAKE_CASE	)

         # We'll offload the last model manually.
         A       :   List[str]					=						hook





     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         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						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = 512						,			SCREAMING_SNAKE_CASE = 512						,			SCREAMING_SNAKE_CASE = 100						,			SCREAMING_SNAKE_CASE = 4.0						,			SCREAMING_SNAKE_CASE = 0.3						,			SCREAMING_SNAKE_CASE = 1						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = "pil"						,			SCREAMING_SNAKE_CASE = True						,			) ->				List[str]:






         """simple docstring"""






         A       :   Tuple					=						self._execution_device

         A       :   Any					=						guidance_scale > 1.0

         if isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	):
             A       :   str					=						torch.cat(SCREAMING_SNAKE_CASE						,			dim=0	)
         A       :   str					=						image_embeds.shape[0]
         if isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	):
             A       :   Dict					=						torch.cat(SCREAMING_SNAKE_CASE						,			dim=0	)

         if do_classifier_free_guidance:
             A       :   Union[str, Any]					=						image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE						,			dim=0	)
             A       :   List[str]					=						negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE						,			dim=0	)

         A       :   Any					=						torch.cat([negative_image_embeds, image_embeds]						,			dim=0	).to(dtype=self.unet.dtype						,			device=SCREAMING_SNAKE_CASE	)

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

         A       :   Tuple					=						torch.cat([prepare_image(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) for i in image]						,			dim=0	)
         A       :   Tuple					=						image.to(dtype=image_embeds.dtype						,			device=SCREAMING_SNAKE_CASE	)

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

             A       :   Dict					=						{'''image_embeds''': image_embeds}
             A       :   Tuple					=						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       :   List[str]					=						noise_pred.split(latents.shape[1]						,			dim=1	)
                 A,  A       :   List[str]					=						noise_pred.chunk(2	)
                 A,  A       :   Tuple					=						variance_pred.chunk(2	)
                 A       :   Optional[int]					=						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                 A       :   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       :   List[str]					=						noise_pred.split(latents.shape[1]						,			dim=1	)

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

         # post-processing
         A       :   Tuple					=						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       :   Union[str, Any]					=						image * 0.5 + 0.5
             A       :   Dict					=						image.clamp(0						,			1	)
             A       :   Any					=						image.cpu().permute(0						,			2						,			3						,			1	).float().numpy()

         if output_type == "pil":
             A       :   int					=						self.numpy_to_pil(SCREAMING_SNAKE_CASE	)

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE	)










"""simple docstring"""


from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


A_						=				logging.get_logger(__name__)






class 					lowercase:







 '''simple docstring'''





 lowercase__ 					=					42
 lowercase__ 					=					None


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   raise NotImplementedError


 def  UpperCamelCase_   (				self:		Tuple,      a_:		int,      a_:		int,      a_:		str,      **a_:		Dict		):




   '''simple docstring'''



   raise NotImplementedError


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		List[str]		):




   '''simple docstring'''



   raise NotImplementedError


 def  UpperCamelCase_   (				self:		Union[str, Any]		):




   '''simple docstring'''



   if not self.is_available():
     raise RuntimeError(
         f"You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}."		)


 @classmethod
 def  UpperCamelCase_   (				cls:		Tuple		):




   '''simple docstring'''



   return f"`pip install {cls.pip_package or cls.name}`"







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"optuna"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_optuna_available()


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		List[Any],      a_:		int,      a_:		str,      **a_:		List[str]		):




   '''simple docstring'''



   return run_hp_search_optuna(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		Any		):




   '''simple docstring'''



   return default_hp_space_optuna(a_		)







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"ray"
 lowercase__ 					=					"'ray[tune]'"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_ray_available()


 def  UpperCamelCase_   (				self:		int,      a_:		Optional[Any],      a_:		int,      a_:		str,      **a_:		List[Any]		):




   '''simple docstring'''



   return run_hp_search_ray(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		str,      a_:		Tuple		):




   '''simple docstring'''



   return default_hp_space_ray(a_		)







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"sigopt"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_sigopt_available()


 def  UpperCamelCase_   (				self:		Dict,      a_:		str,      a_:		int,      a_:		str,      **a_:		int		):




   '''simple docstring'''



   return run_hp_search_sigopt(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		str,      a_:		List[str]		):




   '''simple docstring'''



   return default_hp_space_sigopt(a_		)







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"wandb"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_wandb_available()


 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		str,      a_:		int,      a_:		str,      **a_:		Union[str, Any]		):




   '''simple docstring'''



   return run_hp_search_wandb(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		str,      a_:		Any		):




   '''simple docstring'''



   return default_hp_space_wandb(a_		)







A_						=				{
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		Optional[int]								=  [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
  if len(snake_case__    ) > 0:
    _snake_case							:		Any								=  available_backends[0].name
    if len(snake_case__    ) > 1:
      logger.info(
          F"{len(snake_case__    )} hyperparameter search backends available. Using {name} as the default."    )
    return name
  raise RuntimeError(
      """No hyperparameter search backend available.\n"""
      + """\n""".join(
          F" - To install {backend.name} run {backend.pip_install()}"
          for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()    )    )


