



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,
)


__snake_case    :		List[str]  =logging.get_logger(__name__)  # pylint: disable=invalid-name

__snake_case    :		Optional[int]  ='\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")\n        >>> pipe_prior.to("cuda")\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")\n        >>> pipe.to("cuda")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save("cat.png")\n        ```\n'






def      lowerCAmelCase__		(   lowerCamelCase_		:		Optional[Any]	,lowerCamelCase_		:		Dict	,lowerCamelCase_		:		str=8):





				'''simple docstring'''

				lowerCAmelCase__					:    Any												=					height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				lowerCAmelCase__					:    int												=					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		,)					->      int:

						"""simple docstring"""






						super().__init__()

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



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)					->      Tuple:

						"""simple docstring"""






						if latents is None:
										lowerCAmelCase__					:    int												=					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}"""			)
										lowerCAmelCase__					:    List[Any]												=					latents.to(__lowerCamelCase			)

						lowerCAmelCase__					:    Optional[Any]												=					latents * scheduler.init_noise_sigma
						return latents



		def 		lowerCAmelCase__				(self		,__lowerCamelCase=0			)					->      Dict:

						"""simple docstring"""






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

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

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



		def 		lowerCAmelCase__				(self		,__lowerCamelCase=0			)					->      Union[str, 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.'''			)

						lowerCAmelCase__					:    str												=					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)

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

						# We'll offload the last model manually.
						lowerCAmelCase__					:    Tuple												=					hook



		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""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		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase = 5_12		,__lowerCamelCase = 5_12		,__lowerCamelCase = 1_00		,__lowerCamelCase = 4.0		,__lowerCamelCase = 1		,__lowerCamelCase = None		,__lowerCamelCase = None		,__lowerCamelCase = "pil"		,__lowerCamelCase = True		,)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					self._execution_device

						lowerCAmelCase__					:    int												=					guidance_scale > 1.0

						if isinstance(__lowerCamelCase		,__lowerCamelCase			):
										lowerCAmelCase__					:    Any												=					torch.cat(__lowerCamelCase		,dim=0			)
						lowerCAmelCase__					:    Optional[int]												=					image_embeds.shape[0] * num_images_per_prompt
						if isinstance(__lowerCamelCase		,__lowerCamelCase			):
										lowerCAmelCase__					:    Optional[int]												=					torch.cat(__lowerCamelCase		,dim=0			)

						if do_classifier_free_guidance:
										lowerCAmelCase__					:    List[Any]												=					image_embeds.repeat_interleave(__lowerCamelCase		,dim=0			)
										lowerCAmelCase__					:    Tuple												=					negative_image_embeds.repeat_interleave(__lowerCamelCase		,dim=0			)

						lowerCAmelCase__					:    Union[str, Any]												=					torch.cat([negative_image_embeds, image_embeds]		,dim=0			).to(dtype=self.unet.dtype		,device=__lowerCamelCase			)

						self.scheduler.set_timesteps(__lowerCamelCase		,device=__lowerCamelCase			)
						lowerCAmelCase__					:    List[str]												=					self.scheduler.timesteps

						lowerCAmelCase__					:    int												=					self.unet.config.in_channels

						lowerCAmelCase__						,		lowerCAmelCase__					:    Optional[Any]												=					downscale_height_and_width(__lowerCamelCase		,__lowerCamelCase		,self.movq_scale_factor			)

						# create initial latent
						lowerCAmelCase__					:    Union[str, Any]												=					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
										lowerCAmelCase__					:    List[Any]												=					torch.cat([latents] * 2			) if do_classifier_free_guidance else latents

										lowerCAmelCase__					:    Any												=					{'''image_embeds''': image_embeds}
										lowerCAmelCase__					:    Any												=					self.unet(
										    sample=__lowerCamelCase		,timestep=__lowerCamelCase		,encoder_hidden_states=__lowerCamelCase		,added_cond_kwargs=__lowerCamelCase		,return_dict=__lowerCamelCase		,)[0]

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

										# compute the previous noisy sample x_t -> x_t-1
										lowerCAmelCase__					:    int												=					self.scheduler.step(
										    __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,generator=__lowerCamelCase		,)[0]
						# post-processing
						lowerCAmelCase__					:    Tuple												=					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"]:
										lowerCAmelCase__					:    Union[str, Any]												=					image * 0.5 + 0.5
										lowerCAmelCase__					:    Optional[Any]												=					image.clamp(0		,1			)
										lowerCAmelCase__					:    List[Any]												=					image.cpu().permute(0		,2		,3		,1			).float().numpy()

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

						if not return_dict:
										return (image,)

						return ImagePipelineOutput(images=__lowerCamelCase			)






def      lowerCAmelCase__		(   lowerCamelCase_		:		str	,lowerCamelCase_		:		str):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[Any]												=					len(lowerCamelCase_)
				lowerCAmelCase__					:    str												=					len(lowerCamelCase_)
				lowerCAmelCase__					:    Union[str, Any]												=					[[False for _ in range(m + 1)] for _ in range(n + 1)]
				lowerCAmelCase__					:    List[Any]												=					True
				for i in range(lowerCamelCase_):
								for j in range(m + 1):
												if dp[i][j]:
																if j < m and a[i].upper() == b[j]:
																				lowerCAmelCase__					:    int												=					True
																if a[i].islower():
																				lowerCAmelCase__					:    int												=					True
				return dp[n][m]


if __name__ == "__main__":
							import doctest

							doctest.testmod()

