






'''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,
)


lowerCamelCase_				: Union[str, Any]    	=							logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCamelCase_				: List[Any]    	=							'''
    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 	__magic_name__( _A						,      _A						,      _A=8					):
						'''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 	__magic_name__( _A						,      _A=512						,      _A=512					):
						'''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(_A						,      [2, 0, 1]					)
						UpperCamelCase__         = torch.from_numpy(_A					).unsqueeze(0					)
						return image







class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			def __init__(   self							:						Tuple ,      lowercase							:						UNetaDConditionModel ,      lowercase							:						DDPMScheduler ,      lowercase							:						VQModel ,      )							->    Dict:

									'''simple docstring'''







									super().__init__()

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






			def 				A      (   self							:						List[Any] ,      lowercase							:						List[Any] ,      lowercase							:						List[str] ,      lowercase							:						Dict			)							->    Any:

									'''simple docstring'''







									UpperCamelCase__         = min(int(num_inference_steps * strength			) ,      lowercase			)

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

									return timesteps, num_inference_steps - t_start






			def 				A      (   self							:						List[str] ,      lowercase							:						List[Any] ,      lowercase							:						List[str] ,      lowercase							:						Dict ,      lowercase							:						Union[str, Any] ,      lowercase							:						str ,      lowercase							:						Optional[int] ,      lowercase							:						Tuple=None			)							->    List[str]:

									'''simple docstring'''







									if not isinstance(lowercase ,      (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(lowercase			)}"			)

									UpperCamelCase__         = image.to(device=lowercase ,      dtype=lowercase			)

									UpperCamelCase__         = batch_size * num_images_per_prompt

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

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

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

															UpperCamelCase__         = self.movq.config.scaling_factor * init_latents

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

									UpperCamelCase__         = init_latents.shape
									UpperCamelCase__         = randn_tensor(lowercase ,      generator=lowercase ,      device=lowercase ,      dtype=lowercase			)

									# get latents
									UpperCamelCase__         = self.scheduler.add_noise(lowercase ,      lowercase ,      lowercase			)

									UpperCamelCase__         = init_latents

									return latents






			def 				A      (   self							:						Tuple ,      lowercase							:						Union[str, Any]=0			)							->    Union[str, Any]:

									'''simple docstring'''







									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(lowercase ,      lowercase			)






			def 				A      (   self							:						Any ,      lowercase							:						Optional[Any]=0			)							->    List[str]:

									'''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."""			)

									UpperCamelCase__         = torch.device(f"cuda:{gpu_id}"			)

									if self.device.type != "cpu":
															self.to("""cpu""" ,      silence_dtype_warnings=lowercase			)
															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__    ,   UpperCamelCase__         = cpu_offload_with_hook(lowercase ,      lowercase ,      prev_module_hook=lowercase			)

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






			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 				A      (   self							:						str			)							->    Optional[int]:

									'''simple docstring'''







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

									'''simple docstring'''







									UpperCamelCase__         = self._execution_device

									UpperCamelCase__         = guidance_scale > 1.0

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

									if do_classifier_free_guidance:
															UpperCamelCase__         = image_embeds.repeat_interleave(lowercase ,      dim=0			)
															UpperCamelCase__         = negative_image_embeds.repeat_interleave(lowercase ,      dim=0			)

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

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

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

									UpperCamelCase__         = self.movq.encode(lowercase			)["""latents"""]
									UpperCamelCase__         = latents.repeat_interleave(lowercase ,      dim=0			)
									self.scheduler.set_timesteps(lowercase ,      device=lowercase			)
									UpperCamelCase__    ,   UpperCamelCase__         = self.get_timesteps(lowercase ,      lowercase ,      lowercase			)
									UpperCamelCase__         = timesteps[:1].repeat(batch_size * num_images_per_prompt			)
									UpperCamelCase__    ,   UpperCamelCase__         = downscale_height_and_width(lowercase ,      lowercase ,      self.movq_scale_factor			)
									UpperCamelCase__         = self.prepare_latents(
									    lowercase ,      lowercase ,      lowercase ,      lowercase ,      image_embeds.dtype ,      lowercase ,      lowercase			)
									for i, t in enumerate(self.progress_bar(lowercase			)			):
															# 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}
															UpperCamelCase__         = self.unet(
															    sample=lowercase ,      timestep=lowercase ,      encoder_hidden_states=lowercase ,      added_cond_kwargs=lowercase ,      return_dict=lowercase ,      )[0]

															if do_classifier_free_guidance:
																					UpperCamelCase__    ,   UpperCamelCase__         = noise_pred.split(latents.shape[1] ,      dim=1			)
																					UpperCamelCase__    ,   UpperCamelCase__         = noise_pred.chunk(2			)
																					UpperCamelCase__    ,   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__    ,   UpperCamelCase__         = noise_pred.split(latents.shape[1] ,      dim=1			)

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

									# post-processing
									UpperCamelCase__         = self.movq.decode(lowercase ,      force_not_quantize=lowercase			)["""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(lowercase			)

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=lowercase			)








'''simple docstring'''


import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


lowerCamelCase_				: Any    	=							{
    '''debug''': logging.DEBUG,
    '''info''': logging.INFO,
    '''warning''': logging.WARNING,
    '''error''': logging.ERROR,
    '''critical''': logging.CRITICAL,
}

lowerCamelCase_				: Optional[int]    	=							logging.WARNING







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = os.getenv("""DATASETS_VERBOSITY"""						,      _A					)
						if env_level_str:
												if env_level_str in log_levels:
																		return log_levels[env_level_str]
												else:
																		logging.getLogger().warning(
																		    f"Unknown option DATASETS_VERBOSITY={env_level_str}, "
																		    f"has to be one of: { ', '.join(log_levels.keys()					) }"					)
						return _default_log_level







def 	__magic_name__( ):
						'''simple docstring'''





						return __name__.split("""."""					)[0]







def 	__magic_name__( ):
						'''simple docstring'''





						return logging.getLogger(_get_library_name()					)







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = _get_library_root_logger()
						library_root_logger.setLevel(_get_default_logging_level()					)







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = _get_library_root_logger()
						library_root_logger.setLevel(logging.NOTSET					)







def 	__magic_name__( _A = None					):
						'''simple docstring'''





						if name is None:
												UpperCamelCase__         = _get_library_name()
						return logging.getLogger(_A					)







def 	__magic_name__( ):
						'''simple docstring'''





						return _get_library_root_logger().getEffectiveLevel()







def 	__magic_name__( _A					):
						'''simple docstring'''





						_get_library_root_logger().setLevel(_A					)







def 	__magic_name__( ):
						'''simple docstring'''





						return set_verbosity(_A					)







def 	__magic_name__( ):
						'''simple docstring'''





						return set_verbosity(_A					)







def 	__magic_name__( ):
						'''simple docstring'''





						return set_verbosity(_A					)







def 	__magic_name__( ):
						'''simple docstring'''





						return set_verbosity(_A					)







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = False







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()
class    _SCREAMING_SNAKE_CASE							:


			'''simple docstring'''





			def __init__(   self							:						int ,      *lowercase							:						Any ,      **lowercase							:						int			)							->    List[Any]:  # pylint: disable=unused-argument

									'''simple docstring'''







									UpperCamelCase__         = args[0] if args else None






			def __iter__(   self							:						str			)							->    List[str]:

									'''simple docstring'''







									return iter(self._iterator			)






			def __getattr__(   self							:						Optional[int] ,      lowercase							:						Tuple			)							->    int:

									'''simple docstring'''







									def empty_fn(*lowercase							:						List[Any] ,      **lowercase							:						Dict			):  # pylint: disable=unused-argument
															return

									return empty_fn






			def __enter__(   self							:						Dict			)							->    Tuple:

									'''simple docstring'''







									return self







			def __exit__(   self							:						Tuple ,      lowercase							:						Optional[Any] ,      lowercase							:						Any ,      lowercase							:						int			)							->    Any:

									'''simple docstring'''







									return
lowerCamelCase_				: str    	=							True
class    _SCREAMING_SNAKE_CASE							:


			'''simple docstring'''





			def __call__(   self							:						List[Any] ,      *lowercase							:						str ,      lowercase							:						Optional[int]=False ,      **lowercase							:						Optional[int]			)							->    Optional[int]:

									'''simple docstring'''







									if _tqdm_active and not disable:
															return tqdm_lib.tqdm(*lowercase ,      **lowercase			)
									else:
															return EmptyTqdm(*lowercase ,      **lowercase			)






			def 				A      (   self							:						Optional[Any] ,      *lowercase							:						Tuple ,      **lowercase							:						Any			)							->    Any:

									'''simple docstring'''







									UpperCamelCase__         = None
									if _tqdm_active:
															return tqdm_lib.tqdm.set_lock(*lowercase ,      **lowercase			)







			def 				A      (   self							:						Optional[Any]			)							->    Optional[Any]:

									'''simple docstring'''







									if _tqdm_active:
															return tqdm_lib.tqdm.get_lock()
lowerCamelCase_				: int    	=							_tqdm_cls()







def 	__magic_name__( ):
						'''simple docstring'''





						global _tqdm_active
						return bool(_tqdm_active					)







def 	__magic_name__( ):
						'''simple docstring'''





						global _tqdm_active
						UpperCamelCase__         = True







def 	__magic_name__( ):
						'''simple docstring'''





						global _tqdm_active
						UpperCamelCase__         = False
