






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

UpperCAmelCase__      			=						'''
    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     a_   (__A				,       __A				,       __A=8      )				->						Optional[Any]:



					"""simple docstring"""
					__a					:						Optional[int]      =					height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					__a					:						Optional[Any]      =					width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor







def     a_   (__A				,       __A=512				,       __A=512      )				->						Optional[Any]:



					"""simple docstring"""
					__a					:						int      =					pil_image.resize((w, h)				,       resample=Image.BICUBIC				,       reducing_gap=1      )
					__a					:						Optional[Any]      =					np.array(pil_image.convert("RGB"      )      )
					__a					:						Optional[int]      =					arr.astype(np.floataa      ) / 127.5 - 1
					__a					:						Union[str, Any]      =					np.transpose(__A				,       [2, 0, 1]      )
					__a					:						Optional[Any]      =					torch.from_numpy(__A      ).unsqueeze(0      )
					return image




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						def __init__(self:					Any ,					__UpperCAmelCase:					UNetaDConditionModel ,					__UpperCAmelCase:					DDPMScheduler ,					__UpperCAmelCase:					VQModel ,					)    ->     Optional[int]:




											'''simple docstring'''



											super().__init__()

											self.register_modules(
											    unet=__UpperCAmelCase ,					scheduler=__UpperCAmelCase ,					movq=__UpperCAmelCase ,					)
											__a					:						List[str]      =					2 ** (len(self.movq.config.block_out_channels   ) - 1)



						def        UpperCAmelCase__					(self:					Tuple ,					__UpperCAmelCase:					str ,					__UpperCAmelCase:					Any ,					__UpperCAmelCase:					Dict   )    ->     str:




											'''simple docstring'''



											__a					:						List[Any]      =					min(int(num_inference_steps * strength   ) ,					__UpperCAmelCase   )

											__a					:						Any      =					max(num_inference_steps - init_timestep ,					0   )
											__a					:						Union[str, Any]      =					self.scheduler.timesteps[t_start:]

											return timesteps, num_inference_steps - t_start



						def        UpperCAmelCase__					(self:					List[str] ,					__UpperCAmelCase:					str ,					__UpperCAmelCase:					Optional[int] ,					__UpperCAmelCase:					Union[str, Any] ,					__UpperCAmelCase:					Any ,					__UpperCAmelCase:					Union[str, Any] ,					__UpperCAmelCase:					Any ,					__UpperCAmelCase:					Tuple=None   )    ->     Optional[Any]:




											'''simple docstring'''



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

											__a					:						Any      =					image.to(device=__UpperCAmelCase ,					dtype=__UpperCAmelCase   )

											__a					:						str      =					batch_size * num_images_per_prompt

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

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

																elif isinstance(__UpperCAmelCase ,					__UpperCAmelCase   ):
																					__a					:						Any      =					[
																					    self.movq.encode(image[i : i + 1]   ).latent_dist.sample(generator[i]   ) for i in range(__UpperCAmelCase   )
																					]
																					__a					:						List[str]      =					torch.cat(__UpperCAmelCase ,					dim=0   )
																else:
																					__a					:						Dict      =					self.movq.encode(__UpperCAmelCase   ).latent_dist.sample(__UpperCAmelCase   )

																__a					:						Tuple      =					self.movq.config.scaling_factor * init_latents

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

											__a					:						Any      =					init_latents.shape
											__a					:						List[str]      =					randn_tensor(__UpperCAmelCase ,					generator=__UpperCAmelCase ,					device=__UpperCAmelCase ,					dtype=__UpperCAmelCase   )

											# get latents
											__a					:						int      =					self.scheduler.add_noise(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )

											__a					:						Any      =					init_latents

											return latents



						def        UpperCAmelCase__					(self:					Optional[int] ,					__UpperCAmelCase:					Optional[Any]=0   )    ->     Any:




											'''simple docstring'''



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

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

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



						def        UpperCAmelCase__					(self:					str ,					__UpperCAmelCase:					str=0   )    ->     Optional[int]:




											'''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					:						Optional[int]      =					torch.device(f'cuda:{gpu_id}'   )

											if self.device.type != "cpu":
																self.to("cpu" ,					silence_dtype_warnings=__UpperCAmelCase   )
																torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

											__a					:						List[Any]      =					None
											for cpu_offloaded_model in [self.unet, self.movq]:
																__a      ,			__a					:						Any      =					cpu_offload_with_hook(__UpperCAmelCase ,					__UpperCAmelCase ,					prev_module_hook=__UpperCAmelCase   )

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



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




											'''simple docstring'''



											if not hasattr(self.unet ,					"_hf_hook"   ):
																return self.device
											for module in self.unet.modules():
																if (
																    hasattr(__UpperCAmelCase ,					"_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(__UpperCAmelCase   )
						def __call__(self:					Optional[int] ,					__UpperCAmelCase:					Union[torch.FloatTensor, List[torch.FloatTensor]] ,					__UpperCAmelCase:					Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]] ,					__UpperCAmelCase:					Union[torch.FloatTensor, List[torch.FloatTensor]] ,					__UpperCAmelCase:					int = 512 ,					__UpperCAmelCase:					int = 512 ,					__UpperCAmelCase:					int = 100 ,					__UpperCAmelCase:					float = 4.0 ,					__UpperCAmelCase:					float = 0.3 ,					__UpperCAmelCase:					int = 1 ,					__UpperCAmelCase:					Optional[Union[torch.Generator, List[torch.Generator]]] = None ,					__UpperCAmelCase:					Optional[str] = "pil" ,					__UpperCAmelCase:					bool = True ,					)    ->     int:




											'''simple docstring'''



											__a					:						Dict      =					self._execution_device

											__a					:						List[Any]      =					guidance_scale > 1.0

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

											if do_classifier_free_guidance:
																__a					:						Optional[Any]      =					image_embeds.repeat_interleave(__UpperCAmelCase ,					dim=0   )
																__a					:						Optional[Any]      =					negative_image_embeds.repeat_interleave(__UpperCAmelCase ,					dim=0   )

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

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

											__a					:						Optional[Any]      =					torch.cat([prepare_image(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   ) for i in image] ,					dim=0   )
											__a					:						Tuple      =					image.to(dtype=image_embeds.dtype ,					device=__UpperCAmelCase   )

											__a					:						Any      =					self.movq.encode(__UpperCAmelCase   )["latents"]
											__a					:						Dict      =					latents.repeat_interleave(__UpperCAmelCase ,					dim=0   )
											self.scheduler.set_timesteps(__UpperCAmelCase ,					device=__UpperCAmelCase   )
											__a      ,			__a					:						Union[str, Any]      =					self.get_timesteps(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )
											__a					:						Any      =					timesteps[:1].repeat(batch_size * num_images_per_prompt   )
											__a      ,			__a					:						Any      =					downscale_height_and_width(__UpperCAmelCase ,					__UpperCAmelCase ,					self.movq_scale_factor   )
											__a					:						List[str]      =					self.prepare_latents(
											    __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					image_embeds.dtype ,					__UpperCAmelCase ,					__UpperCAmelCase   )
											for i, t in enumerate(self.progress_bar(__UpperCAmelCase   )   ):
																# expand the latents if we are doing classifier free guidance
																__a					:						Any      =					torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

																__a					:						List[str]      =					{"image_embeds": image_embeds}
																__a					:						List[Any]      =					self.unet(
																    sample=__UpperCAmelCase ,					timestep=__UpperCAmelCase ,					encoder_hidden_states=__UpperCAmelCase ,					added_cond_kwargs=__UpperCAmelCase ,					return_dict=__UpperCAmelCase ,					)[0]

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

																# compute the previous noisy sample x_t -> x_t-1
																__a					:						Dict      =					self.scheduler.step(
																    __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					generator=__UpperCAmelCase ,					)[0]

											# post-processing
											__a					:						List[str]      =					self.movq.decode(__UpperCAmelCase ,					force_not_quantize=__UpperCAmelCase   )["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					:						List[str]      =					image * 0.5 + 0.5
																__a					:						Tuple      =					image.clamp(0 ,					1   )
																__a					:						Optional[int]      =					image.cpu().permute(0 ,					2 ,					3 ,					1   ).float().numpy()

											if output_type == "pil":
																__a					:						Optional[Any]      =					self.numpy_to_pil(__UpperCAmelCase   )

											if not return_dict:
																return (image,)

											return ImagePipelineOutput(images=__UpperCAmelCase   )













UpperCAmelCase__      			=						'''Input must be a string of 8 numbers plus letter'''
UpperCAmelCase__      			=						'''TRWAGMYFPDXBNJZSQVHLCKE'''







def     a_   (__A      )				->						bool:



					"""simple docstring"""

					if not isinstance(__A				,       __A      ):
										__a					:						Any      =					f'Expected string as input, found {type(__A      ).__name__}'
										raise TypeError(__A      )

					__a					:						int      =					spanish_id.replace("-"				,       ""      ).upper()
					if len(__A      ) != 9:
										raise ValueError(__A      )

					try:
										__a					:						Tuple      =					int(spanish_id_clean[0:8]      )
										__a					:						Optional[int]      =					spanish_id_clean[8]
					except ValueError as ex:
										raise ValueError(__A      ) from ex

					if letter.isdigit():
										raise ValueError(__A      )

					return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
			import doctest

			doctest.testmod()





