



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: Union[str, Any]      			=		logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowercase: Tuple      			=		"\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     a(      A       :    Union[str, Any] ,     A       :    Any ,     A       :    Optional[Any]=8       )		->       Dict:
					"""simple docstring"""





					a					     =   height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					a					     =   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       :    List[Any] ,     A       :    Optional[int]=512 ,     A       :    int=512       )		->       Optional[Any]:
					"""simple docstring"""





					a					     =   pil_image.resize((w, h) ,     resample=Image.BICUBIC ,     reducing_gap=1       )
					a					     =   np.array(pil_image.convert("RGB"       )       )
					a					     =   arr.astype(np.floataa       ) / 127.5 - 1
					a					     =   np.transpose(A ,     [2, 0, 1]       )
					a					     =   torch.from_numpy(A       ).unsqueeze(0       )
					return image







class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       ):
						"""simple docstring"""

						super().__init__()

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   min(int(num_inference_steps * strength		)		,       lowerCamelCase_		)

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

						return timesteps, num_inference_steps - t_start




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_=None		):
						"""simple docstring"""

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

						a					     =   image.to(device=lowerCamelCase_		,       dtype=lowerCamelCase_		)

						a					     =   batch_size * num_images_per_prompt

						if image.shape[1] == 4:
											a					     =   image

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

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

											a					     =   self.movq.config.scaling_factor * init_latents

						a					     =   torch.cat([init_latents]		,       dim=0		)

						a					     =   init_latents.shape
						a					     =   randn_tensor(lowerCamelCase_		,       generator=lowerCamelCase_		,       device=lowerCamelCase_		,       dtype=lowerCamelCase_		)

						# get latents
						a					     =   self.scheduler.add_noise(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)

						a					     =   init_latents

						return latents




	def      UpperCamelCase_       (self		,       lowerCamelCase_=0		):
						"""simple docstring"""

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

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

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_=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					     =   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)

						a					     =   None
						for cpu_offloaded_model in [self.unet, self.movq]:
											a     ,				a					     =   cpu_offload_with_hook(lowerCamelCase_		,       lowerCamelCase_		,       prev_module_hook=lowerCamelCase_		)

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




	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def      UpperCamelCase_       (self		):
						"""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_		,       lowerCamelCase_ = 512		,       lowerCamelCase_ = 512		,       lowerCamelCase_ = 100		,       lowerCamelCase_ = 4.0		,       lowerCamelCase_ = 0.3		,       lowerCamelCase_ = 1		,       lowerCamelCase_ = None		,       lowerCamelCase_ = "pil"		,       lowerCamelCase_ = True		,       ):
						"""simple docstring"""

						a					     =   self._execution_device

						a					     =   guidance_scale > 1.0

						if isinstance(lowerCamelCase_		,       lowerCamelCase_		):
											a					     =   torch.cat(lowerCamelCase_		,       dim=0		)
						a					     =   image_embeds.shape[0]
						if isinstance(lowerCamelCase_		,       lowerCamelCase_		):
											a					     =   torch.cat(lowerCamelCase_		,       dim=0		)

						if do_classifier_free_guidance:
											a					     =   image_embeds.repeat_interleave(lowerCamelCase_		,       dim=0		)
											a					     =   negative_image_embeds.repeat_interleave(lowerCamelCase_		,       dim=0		)

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

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

						a					     =   torch.cat([prepare_image(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		) for i in image]		,       dim=0		)
						a					     =   image.to(dtype=image_embeds.dtype		,       device=lowerCamelCase_		)

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

											a					     =   {"image_embeds": image_embeds}
											a					     =   self.unet(
											    sample=lowerCamelCase_		,       timestep=lowerCamelCase_		,       encoder_hidden_states=lowerCamelCase_		,       added_cond_kwargs=lowerCamelCase_		,       return_dict=lowerCamelCase_		,       )[0]

											if do_classifier_free_guidance:
																a     ,				a					     =   noise_pred.split(latents.shape[1]		,       dim=1		)
																a     ,				a					     =   noise_pred.chunk(2		)
																a     ,				a					     =   variance_pred.chunk(2		)
																a					     =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																a					     =   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					     =   noise_pred.split(latents.shape[1]		,       dim=1		)

											# compute the previous noisy sample x_t -> x_t-1
											a					     =   self.scheduler.step(
											    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       generator=lowerCamelCase_		,       )[0]

						# post-processing
						a					     =   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"]:
											a					     =   image * 0.5 + 0.5
											a					     =   image.clamp(0		,       1		)
											a					     =   image.cpu().permute(0		,       2		,       3		,       1		).float().numpy()

						if output_type == "pil":
											a					     =   self.numpy_to_pil(lowerCamelCase_		)

						if not return_dict:
											return (image,)

						return ImagePipelineOutput(images=lowerCamelCase_		)





from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
				import torch

				from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

_lowercase: Any      			=		logging.get_logger(__name__)



@add_end_docstrings(lowerCAmelCase	)
class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       **lowerCamelCase_		):
						"""simple docstring"""

						super().__init__(**lowerCamelCase_		)
						requires_backends(self		,       "vision"		)
						requires_backends(self		,       "torch"		)

						if self.framework != "pt":
											raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''		)

						self.check_model_type(lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   {}
						a					     =   {}
						a					     =   {}
						# preprocess args
						if "points_per_batch" in kwargs:
											a					     =   kwargs["points_per_batch"]
						if "points_per_crop" in kwargs:
											a					     =   kwargs["points_per_crop"]
						if "crops_n_layers" in kwargs:
											a					     =   kwargs["crops_n_layers"]
						if "crop_overlap_ratio" in kwargs:
											a					     =   kwargs["crop_overlap_ratio"]
						if "crop_n_points_downscale_factor" in kwargs:
											a					     =   kwargs["crop_n_points_downscale_factor"]
						# postprocess args
						if "pred_iou_thresh" in kwargs:
											a					     =   kwargs["pred_iou_thresh"]
						if "stability_score_offset" in kwargs:
											a					     =   kwargs["stability_score_offset"]
						if "mask_threshold" in kwargs:
											a					     =   kwargs["mask_threshold"]
						if "stability_score_thresh" in kwargs:
											a					     =   kwargs["stability_score_thresh"]
						if "crops_nms_thresh" in kwargs:
											a					     =   kwargs["crops_nms_thresh"]
						if "output_rle_mask" in kwargs:
											a					     =   kwargs["output_rle_mask"]
						if "output_bboxes_mask" in kwargs:
											a					     =   kwargs["output_bboxes_mask"]
						return preprocess_kwargs, forward_params, postprocess_kwargs




	def __call__(self		,       lowerCamelCase_		,       *lowerCamelCase_		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       **lowerCamelCase_		):
						"""simple docstring"""

						return super().__call__(lowerCamelCase_		,       *lowerCamelCase_		,       num_workers=lowerCamelCase_		,       batch_size=lowerCamelCase_		,       **lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=64		,       lowerCamelCase_ = 0		,       lowerCamelCase_ = 512 / 1500		,       lowerCamelCase_ = 32		,       lowerCamelCase_ = 1		,       ):
						"""simple docstring"""

						a					     =   load_image(lowerCamelCase_		)
						a					     =   self.image_processor.size["longest_edge"]
						a     ,				a     ,				a     ,				a					     =   self.image_processor.generate_crop_boxes(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)
						a					     =   self.image_processor(images=lowerCamelCase_		,       return_tensors="pt"		)

						with self.device_placement():
											if self.framework == "pt":
																a					     =   self.get_inference_context()
																with inference_context():
																					a					     =   self._ensure_tensor_on_device(lowerCamelCase_		,       device=self.device		)
																					a					     =   self.model.get_image_embeddings(model_inputs.pop("pixel_values"		)		)
																					a					     =   image_embeddings

						a					     =   grid_points.shape[1]
						a					     =   points_per_batch if points_per_batch is not None else n_points

						if points_per_batch <= 0:
											raise ValueError(
											    "Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
											    "To return all points at once, set points_per_batch to None"		)

						for i in range(0		,       lowerCamelCase_		,       lowerCamelCase_		):
											a					     =   grid_points[:, i : i + points_per_batch, :, :]
											a					     =   input_labels[:, i : i + points_per_batch]
											a					     =   i == n_points - points_per_batch
											yield {
											    "input_points": batched_points,
											    "input_labels": labels,
											    "input_boxes": crop_boxes,
											    "is_last": is_last,
											    **model_inputs,
											}




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=0.88		,       lowerCamelCase_=0.95		,       lowerCamelCase_=0		,       lowerCamelCase_=1		,       ):
						"""simple docstring"""

						a					     =   model_inputs.pop("input_boxes"		)
						a					     =   model_inputs.pop("is_last"		)
						a					     =   model_inputs.pop("original_sizes"		).tolist()
						a					     =   model_inputs.pop("reshaped_input_sizes"		).tolist()

						a					     =   self.model(**lowerCamelCase_		)

						# post processing happens here in order to avoid CPU GPU copies of ALL the masks
						a					     =   model_outputs["pred_masks"]
						a					     =   self.image_processor.post_process_masks(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       binarize=lowerCamelCase_		)
						a					     =   model_outputs["iou_scores"]
						a     ,				a     ,				a					     =   self.image_processor.filter_masks(
						    masks[0]		,       iou_scores[0]		,       original_sizes[0]		,       input_boxes[0]		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       )
						return {
						    "masks": masks,
						    "is_last": is_last,
						    "boxes": boxes,
						    "iou_scores": iou_scores,
						}




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=False		,       lowerCamelCase_=False		,       lowerCamelCase_=0.7		,       ):
						"""simple docstring"""

						a					     =   []
						a					     =   []
						a					     =   []
						for model_output in model_outputs:
											all_scores.append(model_output.pop("iou_scores"		)		)
											all_masks.extend(model_output.pop("masks"		)		)
											all_boxes.append(model_output.pop("boxes"		)		)

						a					     =   torch.cat(lowerCamelCase_		)
						a					     =   torch.cat(lowerCamelCase_		)
						a     ,				a     ,				a     ,				a					     =   self.image_processor.post_process_for_mask_generation(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)

						a					     =   defaultdict(lowerCamelCase_		)
						for output in model_outputs:
											for k, v in output.items():
																extra[k].append(lowerCamelCase_		)

						a					     =   {}
						if output_rle_mask:
											a					     =   rle_mask

						if output_bboxes_mask:
											a					     =   bounding_boxes

						return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
