



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,
)


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

lowercase__			   =  "\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       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: List[str]  ,       _lowerCamelCase				: List[Any]=8					):
							__a		:		List[str]									=       height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							__a		:		Any									=       width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: Optional[Any]=5_1_2  ,       _lowerCamelCase				: str=5_1_2					):
							__a		:		Dict									=       pil_image.resize((w, h)  ,       resample=Image.BICUBIC  ,       reducing_gap=1					)
							__a		:		Any									=       np.array(pil_image.convert("""RGB"""					)					)
							__a		:		Any									=       arr.astype(np.floataa					) / 1_2_7.5 - 1
							__a		:		Any									=       np.transpose(_lowerCamelCase  ,       [2, 0, 1]					)
							__a		:		Dict									=       torch.from_numpy(_lowerCamelCase					).unsqueeze(0					)
							return image







class SCREAMING_SNAKE_CASE__					(		__snake_case      ):



						def __init__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     ):





													'''simple docstring'''

													super().__init__()

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



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       min(int(num_inference_steps * strength							)						,     _lowercase							)

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

													return timesteps, num_inference_steps - t_start



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase=None							):





													'''simple docstring'''

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

													__a		:		Optional[Any]									=       image.to(device=_lowercase						,     dtype=_lowercase							)

													__a		:		Optional[int]									=       batch_size * num_images_per_prompt

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

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

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

																				__a		:		Union[str, Any]									=       self.movq.config.scaling_factor * init_latents

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

													__a		:		Dict									=       init_latents.shape
													__a		:		Optional[int]									=       randn_tensor(_lowercase						,     generator=_lowercase						,     device=_lowercase						,     dtype=_lowercase							)

													# get latents
													__a		:		Any									=       self.scheduler.add_noise(_lowercase						,     _lowercase						,     _lowercase							)

													__a		:		Any									=       init_latents

													return latents



						def     lowerCAmelCase__(self						,     _lowercase=0							):





													'''simple docstring'''

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

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

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



						def     lowerCAmelCase__(self						,     _lowercase=0							):





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

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

													__a		:		Tuple									=       None
													for cpu_offloaded_model in [self.unet, self.movq]:
																				__a		:		Optional[int]									=       cpu_offload_with_hook(_lowercase						,     _lowercase						,     prev_module_hook=_lowercase							)

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



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





													'''simple docstring'''

													if not hasattr(self.unet						,     """_hf_hook"""							):
																				return self.device
													for module in self.unet.modules():
																				if (
																				    hasattr(_lowercase						,     """_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(_lowercase							)
						def __call__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase = 512						,     _lowercase = 512						,     _lowercase = 100						,     _lowercase = 4.0						,     _lowercase = 0.3						,     _lowercase = 1						,     _lowercase = None						,     _lowercase = "pil"						,     _lowercase = True						,     ):





													'''simple docstring'''

													__a		:		List[Any]									=       self._execution_device

													__a		:		List[str]									=       guidance_scale > 1.0

													if isinstance(_lowercase						,     _lowercase							):
																				__a		:		Tuple									=       torch.cat(_lowercase						,     dim=0							)
													__a		:		Union[str, Any]									=       image_embeds.shape[0]
													if isinstance(_lowercase						,     _lowercase							):
																				__a		:		Optional[int]									=       torch.cat(_lowercase						,     dim=0							)

													if do_classifier_free_guidance:
																				__a		:		int									=       image_embeds.repeat_interleave(_lowercase						,     dim=0							)
																				__a		:		Union[str, Any]									=       negative_image_embeds.repeat_interleave(_lowercase						,     dim=0							)

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

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

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

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

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

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

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

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

													if output_type == "pil":
																				__a		:		Optional[int]									=       self.numpy_to_pil(_lowercase							)

													if not return_dict:
																				return (image,)

													return ImagePipelineOutput(images=_lowercase							)






"""simple docstring"""

def       __magic_name__    (   _lowerCamelCase				: list[int]					):
							if not nums:  # Makes sure that the list is not empty
														raise ValueError("""List is empty"""					)

							__a		:		Any									=       sum(_lowerCamelCase					) / len(_lowerCamelCase					)  # Calculate the average
							return sum(abs(x - average					) for x in nums					) / len(_lowerCamelCase					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

