



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

lowerCAmelCase__					  =  '''
    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  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__=8						):

				"""simple docstring"""


				lowercase__					:   str													=					height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				lowercase__					:   Tuple													=					width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__=512					,			lowerCamelCase__=512						):

				"""simple docstring"""


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

class 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""






					def __init__(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     UNetaDConditionModel				,       SCREAMING_SNAKE_CASE :     DDPMScheduler				,       SCREAMING_SNAKE_CASE :     VQModel				,       ):
									super().__init__()

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







					def 			snake_case					(							self :     Dict				,       SCREAMING_SNAKE_CASE :     List[Any]				,       SCREAMING_SNAKE_CASE :     List[Any]				,       SCREAMING_SNAKE_CASE :     List[str]					):
									# get the original timestep using init_timestep
									lowercase__					:   int													=					min(int(num_inference_steps * strength					)				,       SCREAMING_SNAKE_CASE					)

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

									return timesteps, num_inference_steps - t_start







					def 			snake_case					(							self :     Any				,       SCREAMING_SNAKE_CASE :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     int				,       SCREAMING_SNAKE_CASE :     List[Any]				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     Any=None					):
									if not isinstance(SCREAMING_SNAKE_CASE				,       (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(SCREAMING_SNAKE_CASE					)}"""					)

									lowercase__					:   Any													=					image.to(device=SCREAMING_SNAKE_CASE				,       dtype=SCREAMING_SNAKE_CASE					)

									lowercase__					:   Dict													=					batch_size * num_images_per_prompt

									if image.shape[1] == 4:
													lowercase__					:   List[str]													=					image

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

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

													lowercase__					:   List[Any]													=					self.movq.config.scaling_factor * init_latents

									lowercase__					:   Tuple													=					torch.cat([init_latents]				,       dim=0					)

									lowercase__					:   int													=					init_latents.shape
									lowercase__					:   str													=					randn_tensor(SCREAMING_SNAKE_CASE				,       generator=SCREAMING_SNAKE_CASE				,       device=SCREAMING_SNAKE_CASE				,       dtype=SCREAMING_SNAKE_CASE					)

									# get latents
									lowercase__					:   Any													=					self.scheduler.add_noise(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)

									lowercase__					:   Union[str, Any]													=					init_latents

									return latents







					def 			snake_case					(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     Tuple=0					):
									if is_accelerate_available():
													from accelerate import cpu_offload
									else:
													raise ImportError("Please install accelerate via `pip install accelerate`"					)

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

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







					def 			snake_case					(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     Optional[int]=0					):
									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."					)

									lowercase__					:   Optional[Any]													=					torch.device(f"""cuda:{gpu_id}"""					)

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

									lowercase__					:   str													=					None
									for cpu_offloaded_model in [self.unet, self.movq]:
													lowercase__							,							lowercase__					:   List[Any]													=					cpu_offload_with_hook(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       prev_module_hook=SCREAMING_SNAKE_CASE					)

									# We'll offload the last model manually.
									lowercase__					:   Union[str, Any]													=					hook







					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def 			snake_case					(							self :     Dict					):
									if not hasattr(self.unet				,       "_hf_hook"					):
													return self.device
									for module in self.unet.modules():
													if (
													    hasattr(SCREAMING_SNAKE_CASE				,       "_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(SCREAMING_SNAKE_CASE					)
					def __call__(							self :     str				,       SCREAMING_SNAKE_CASE :     Union[torch.FloatTensor, List[torch.FloatTensor]]				,       SCREAMING_SNAKE_CASE :     Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]				,       SCREAMING_SNAKE_CASE :     Union[torch.FloatTensor, List[torch.FloatTensor]]				,       SCREAMING_SNAKE_CASE :     int = 512				,       SCREAMING_SNAKE_CASE :     int = 512				,       SCREAMING_SNAKE_CASE :     int = 100				,       SCREAMING_SNAKE_CASE :     float = 4.0				,       SCREAMING_SNAKE_CASE :     float = 0.3				,       SCREAMING_SNAKE_CASE :     int = 1				,       SCREAMING_SNAKE_CASE :     Optional[Union[torch.Generator, List[torch.Generator]]] = None				,       SCREAMING_SNAKE_CASE :     Optional[str] = "pil"				,       SCREAMING_SNAKE_CASE :     bool = True				,       ):
									lowercase__					:   Tuple													=					self._execution_device

									lowercase__					:   Tuple													=					guidance_scale > 1.0

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

									if do_classifier_free_guidance:
													lowercase__					:   Optional[int]													=					image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE				,       dim=0					)
													lowercase__					:   Dict													=					negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE				,       dim=0					)

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

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

									lowercase__					:   str													=					torch.cat([prepare_image(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					) for i in image]				,       dim=0					)
									lowercase__					:   int													=					image.to(dtype=image_embeds.dtype				,       device=SCREAMING_SNAKE_CASE					)

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

													lowercase__					:   Tuple													=					{"image_embeds": image_embeds}
													lowercase__					:   Any													=					self.unet(
													    sample=SCREAMING_SNAKE_CASE				,       timestep=SCREAMING_SNAKE_CASE				,       encoder_hidden_states=SCREAMING_SNAKE_CASE				,       added_cond_kwargs=SCREAMING_SNAKE_CASE				,       return_dict=SCREAMING_SNAKE_CASE				,       )[0]

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

													# compute the previous noisy sample x_t -> x_t-1
													lowercase__					:   Tuple													=					self.scheduler.step(
													    SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       generator=SCREAMING_SNAKE_CASE				,       )[0]

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

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

									if not return_dict:
													return (image,)

									return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE					)









from typing import Union

import fire
import torch
from tqdm import tqdm


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__ = "cpu"					,			lowerCamelCase__ = None						):

				"""simple docstring"""


				lowercase__					:   Any													=					torch.load(lowerCamelCase__					,			map_location=lowerCamelCase__						)
				for k, v in tqdm(state_dict.items()						):
								if not isinstance(lowerCamelCase__					,			torch.Tensor						):
												raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"						)
								lowercase__					:   int													=					v.half()
				if save_path is None:  # overwrite src_path
								lowercase__					:   Optional[Any]													=					src_path
				torch.save(lowerCamelCase__					,			lowerCamelCase__						)


if __name__ == "__main__":
				fire.Fire(convert)




