


"""simple docstring"""




from typing import List, Optional, Union

import torch

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 KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
        >>> pipe_prior.to("cuda")
        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
        >>> pipe.to("cuda")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save("cat.png")
        ```
'''


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


class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       )     ->							List[Any]:
								super().__init__()

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







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							int:
								if latents is None:
														SCREAMING_SNAKE_CASE													=					randn_tensor(lowerCAmelCase__						,       generator=lowerCAmelCase__						,       device=lowerCAmelCase__						,       dtype=lowerCAmelCase__				)
								else:
														if latents.shape != shape:
																				raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'				)
														SCREAMING_SNAKE_CASE													=					latents.to(lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					latents * scheduler.init_noise_sigma
								return latents







		def 							__A						(	self						,       lowerCAmelCase__=0				)     ->							Tuple:
								if is_accelerate_available():
														from accelerate import cpu_offload
								else:
														raise ImportError('Please install accelerate via `pip install accelerate`'				)

								SCREAMING_SNAKE_CASE													=					torch.device(F'cuda:{gpu_id}'				)

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







		def 							__A						(	self						,       lowerCAmelCase__=0				)     ->							Any:
								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													=					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													=					None
								for cpu_offloaded_model in [self.unet, self.movq]:
														SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					cpu_offload_with_hook(lowerCAmelCase__						,       lowerCAmelCase__						,       prev_module_hook=lowerCAmelCase__				)

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







		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def 							__A						(	self				)     ->							int:
								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						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__ = 512						,       lowerCAmelCase__ = 512						,       lowerCAmelCase__ = 100						,       lowerCAmelCase__ = 4.0						,       lowerCAmelCase__ = 1						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = "pil"						,       lowerCAmelCase__ = True						,       )     ->							str:
								SCREAMING_SNAKE_CASE													=					self._execution_device

								SCREAMING_SNAKE_CASE													=					guidance_scale > 1.0

								if isinstance(lowerCAmelCase__						,       lowerCAmelCase__				):
														SCREAMING_SNAKE_CASE													=					torch.cat(lowerCAmelCase__						,       dim=0				)
								SCREAMING_SNAKE_CASE													=					image_embeds.shape[0] * num_images_per_prompt
								if isinstance(lowerCAmelCase__						,       lowerCAmelCase__				):
														SCREAMING_SNAKE_CASE													=					torch.cat(lowerCAmelCase__						,       dim=0				)

								if do_classifier_free_guidance:
														SCREAMING_SNAKE_CASE													=					image_embeds.repeat_interleave(lowerCAmelCase__						,       dim=0				)
														SCREAMING_SNAKE_CASE													=					negative_image_embeds.repeat_interleave(lowerCAmelCase__						,       dim=0				)

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

								self.scheduler.set_timesteps(lowerCAmelCase__						,       device=lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					self.scheduler.timesteps

								SCREAMING_SNAKE_CASE													=					self.unet.config.in_channels

								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					downscale_height_and_width(lowerCAmelCase__						,       lowerCAmelCase__						,       self.movq_scale_factor				)

								# create initial latent
								SCREAMING_SNAKE_CASE													=					self.prepare_latents(
								    (batch_size, num_channels_latents, height, width)						,       image_embeds.dtype						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       self.scheduler						,       )

								for i, t in enumerate(self.progress_bar(lowerCAmelCase__				)				):
														# expand the latents if we are doing classifier free guidance
														SCREAMING_SNAKE_CASE													=					torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

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

														# compute the previous noisy sample x_t -> x_t-1
														SCREAMING_SNAKE_CASE													=					self.scheduler.step(
														    lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       generator=lowerCAmelCase__						,       )[0]
								# post-processing
								SCREAMING_SNAKE_CASE													=					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													=					image * 0.5 + 0.5
														SCREAMING_SNAKE_CASE													=					image.clamp(0						,       1				)
														SCREAMING_SNAKE_CASE													=					image.cpu().permute(0						,       2						,       3						,       1				).float().numpy()

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

								if not return_dict:
														return (image,)

								return ImagePipelineOutput(images=lowerCAmelCase__				)






"""simple docstring"""




# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


__UpperCamelCase						=      open  # noqa: we just need to have a builtin inside this module to test it properly


