
"""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,
)


UpperCAmelCase__   :					str												=				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 lowercase_						(     _snake_case     ,_snake_case     ,_snake_case=8		):
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor

def lowercase_						(     _snake_case     ,_snake_case=512     ,_snake_case=512		):
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     pil_image.resize((w, h)     ,resample=Image.BICUBIC     ,reducing_gap=1		)
							SCREAMING_SNAKE_CASE__		: Tuple     =     np.array(pil_image.convert("""RGB"""		)		)
							SCREAMING_SNAKE_CASE__		: List[str]     =     arr.astype(np.floataa		) / 127.5 - 1
							SCREAMING_SNAKE_CASE__		: int     =     np.transpose(__lowerCAmelCase     ,[2, 0, 1]		)
							SCREAMING_SNAKE_CASE__		: str     =     torch.from_numpy(__lowerCAmelCase		).unsqueeze(0		)
							return image



class 			lowerCAmelCase_		(UpperCAmelCase__						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , )      ->	Union[str, Any]:



											"""simple docstring"""




											super().__init__()

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



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: int     =     min(int(num_inference_steps * strength					)     , lowerCamelCase__					)

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     max(num_inference_steps - init_timestep     , 0					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     self.scheduler.timesteps[t_start:]

											return timesteps, num_inference_steps - t_start



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=None					)      ->	Any:



											"""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__					)}'''					)

											SCREAMING_SNAKE_CASE__		: str     =     image.to(device=lowerCamelCase__     , dtype=lowerCamelCase__					)

											SCREAMING_SNAKE_CASE__		: Tuple     =     batch_size * num_images_per_prompt

											if image.shape[1] == 4:
																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     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__					):
																									SCREAMING_SNAKE_CASE__		: List[Any]     =     [
																									    self.movq.encode(image[i : i + 1]					).latent_dist.sample(generator[i]					) for i in range(lowerCamelCase__					)
																									]
																									SCREAMING_SNAKE_CASE__		: str     =     torch.cat(lowerCamelCase__     , dim=0					)
																		else:
																									SCREAMING_SNAKE_CASE__		: List[str]     =     self.movq.encode(lowerCamelCase__					).latent_dist.sample(lowerCamelCase__					)

																		SCREAMING_SNAKE_CASE__		: List[str]     =     self.movq.config.scaling_factor * init_latents

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

											SCREAMING_SNAKE_CASE__		: int     =     init_latents.shape
											SCREAMING_SNAKE_CASE__		: List[Any]     =     randn_tensor(lowerCamelCase__     , generator=lowerCamelCase__     , device=lowerCamelCase__     , dtype=lowerCamelCase__					)

											# get latents
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.scheduler.add_noise(lowerCamelCase__     , lowerCamelCase__     , lowerCamelCase__					)

											SCREAMING_SNAKE_CASE__		: Dict     =     init_latents

											return latents



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__=0					)      ->	str:



											"""simple docstring"""




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

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

											SCREAMING_SNAKE_CASE__		: Tuple     =     [
											    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     , SCREAMING_SNAKE_CASE__=0					)      ->	List[Any]:



											"""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."""					)

											SCREAMING_SNAKE_CASE__		: Dict     =     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)

											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     None
											for cpu_offloaded_model in [self.unet, self.movq]:
																		SCREAMING_SNAKE_CASE__		: Tuple     =     cpu_offload_with_hook(lowerCamelCase__     , lowerCamelCase__     , prev_module_hook=lowerCamelCase__					)

											# We'll offload the last model manually.
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     hook



				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def 			__magic_name__					(self					)      ->	Optional[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     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = 5_12     , SCREAMING_SNAKE_CASE__ = 5_12     , SCREAMING_SNAKE_CASE__ = 1_00     , 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     , )      ->	str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     self._execution_device

											SCREAMING_SNAKE_CASE__		: str     =     guidance_scale > 1.0

											if isinstance(lowerCamelCase__     , lowerCamelCase__					):
																		SCREAMING_SNAKE_CASE__		: List[str]     =     torch.cat(lowerCamelCase__     , dim=0					)
											SCREAMING_SNAKE_CASE__		: Any     =     image_embeds.shape[0]
											if isinstance(lowerCamelCase__     , lowerCamelCase__					):
																		SCREAMING_SNAKE_CASE__		: Optional[Any]     =     torch.cat(lowerCamelCase__     , dim=0					)

											if do_classifier_free_guidance:
																		SCREAMING_SNAKE_CASE__		: str     =     image_embeds.repeat_interleave(lowerCamelCase__     , dim=0					)
																		SCREAMING_SNAKE_CASE__		: Optional[int]     =     negative_image_embeds.repeat_interleave(lowerCamelCase__     , dim=0					)

											SCREAMING_SNAKE_CASE__		: str     =     torch.cat([negative_image_embeds, image_embeds]     , dim=0					).to(dtype=self.unet.dtype     , device=lowerCamelCase__					)

											if not isinstance(lowerCamelCase__     , lowerCamelCase__					):
																		SCREAMING_SNAKE_CASE__		: List[Any]     =     [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'''					)

											SCREAMING_SNAKE_CASE__		: Dict     =     torch.cat([prepare_image(lowerCamelCase__     , lowerCamelCase__     , lowerCamelCase__					) for i in image]     , dim=0					)
											SCREAMING_SNAKE_CASE__		: int     =     image.to(dtype=image_embeds.dtype     , device=lowerCamelCase__					)

											SCREAMING_SNAKE_CASE__		: Dict     =     self.movq.encode(lowerCamelCase__					)["latents"]
											SCREAMING_SNAKE_CASE__		: List[str]     =     latents.repeat_interleave(lowerCamelCase__     , dim=0					)
											self.scheduler.set_timesteps(lowerCamelCase__     , device=lowerCamelCase__					)
											SCREAMING_SNAKE_CASE__		: Any     =     self.get_timesteps(lowerCamelCase__     , lowerCamelCase__     , lowerCamelCase__					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     timesteps[:1].repeat(batch_size * num_images_per_prompt					)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     downscale_height_and_width(lowerCamelCase__     , lowerCamelCase__     , self.movq_scale_factor					)
											SCREAMING_SNAKE_CASE__		: List[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
																		SCREAMING_SNAKE_CASE__		: Optional[Any]     =     torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     {"image_embeds": image_embeds}
																		SCREAMING_SNAKE_CASE__		: Any     =     self.unet(
																		    sample=lowerCamelCase__     , timestep=lowerCamelCase__     , encoder_hidden_states=lowerCamelCase__     , added_cond_kwargs=lowerCamelCase__     , return_dict=lowerCamelCase__     , )[0]

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

																		# compute the previous noisy sample x_t -> x_t-1
																		SCREAMING_SNAKE_CASE__		: int     =     self.scheduler.step(
																		    lowerCamelCase__     , lowerCamelCase__     , lowerCamelCase__     , generator=lowerCamelCase__     , )[0]

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

											if output_type == "pil":
																		SCREAMING_SNAKE_CASE__		: Tuple     =     self.numpy_to_pil(lowerCamelCase__					)

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=lowerCamelCase__					)




"""simple docstring"""




from typing import TYPE_CHECKING

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


UpperCAmelCase__   :					Tuple												=				{
    'configuration_mask2former': [
        'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Mask2FormerConfig',
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__   :					str												=				['Mask2FormerImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__   :					Any												=				[
					    'MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'Mask2FormerForUniversalSegmentation',
					    'Mask2FormerModel',
					    'Mask2FormerPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

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




