
"""simple docstring"""






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,
)


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

lowerCamelCase__ :       Optional[int]						     =			"\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"





def __A						(   a_ :							Any		,		a_ :							Optional[Any]		,		a_ :							List[str]=8     )->							Optional[int]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     int				= height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		SCREAMING_SNAKE_CASE       :     Any				= width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	def __init__(						self    :List[str]		, lowerCamelCase_    :UNetaDConditionModel		, lowerCamelCase_    :DDPMScheduler		, lowerCamelCase_    :VQModel		, )							->			str:







			'''simple docstring'''




			super().__init__()

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





	def   __lowerCAmelCase (						self    :Optional[int]		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Any		, lowerCamelCase_    :str		, lowerCamelCase_    :int		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :int					)							->			List[Any]:







			'''simple docstring'''




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

			SCREAMING_SNAKE_CASE       :     List[str]				= latents * scheduler.init_noise_sigma
			return latents





	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :Any=0					)							->			List[Any]:







			'''simple docstring'''




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

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

			SCREAMING_SNAKE_CASE       :     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    :str		, lowerCamelCase_    :Union[str, Any]=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.'''					)

			SCREAMING_SNAKE_CASE       :     Optional[int]				= 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)

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

			# We'll offload the last model manually.
			SCREAMING_SNAKE_CASE       :     int				= hook





	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def   __lowerCAmelCase (						self    :int					)							->			Optional[int]:







			'''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    :str		, lowerCamelCase_    :Union[torch.FloatTensor, List[torch.FloatTensor]]		, lowerCamelCase_    :Union[torch.FloatTensor, List[torch.FloatTensor]]		, lowerCamelCase_    :torch.FloatTensor		, lowerCamelCase_    :int = 5_12		, lowerCamelCase_    :int = 5_12		, lowerCamelCase_    :int = 1_00		, lowerCamelCase_    :float = 4.0		, lowerCamelCase_    :int = 1		, lowerCamelCase_    :Optional[Union[torch.Generator, List[torch.Generator]]] = None		, lowerCamelCase_    :Optional[torch.FloatTensor] = None		, lowerCamelCase_    :Optional[str] = "pil"		, lowerCamelCase_    :bool = True		, )							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= self._execution_device

			SCREAMING_SNAKE_CASE       :     List[Any]				= guidance_scale > 1.0

			if isinstance(lowerCamelCase_		, lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     str				= torch.cat(lowerCamelCase_		, dim=0					)
			if isinstance(lowerCamelCase_		, lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= torch.cat(lowerCamelCase_		, dim=0					)
			if isinstance(lowerCamelCase_		, lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= torch.cat(lowerCamelCase_		, dim=0					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= image_embeds.shape[0] * num_images_per_prompt

			if do_classifier_free_guidance:
					SCREAMING_SNAKE_CASE       :     Tuple				= image_embeds.repeat_interleave(lowerCamelCase_		, dim=0					)
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= negative_image_embeds.repeat_interleave(lowerCamelCase_		, dim=0					)
					SCREAMING_SNAKE_CASE       :     Tuple				= hint.repeat_interleave(lowerCamelCase_		, dim=0					)

			SCREAMING_SNAKE_CASE       :     Any				= torch.cat([negative_image_embeds, image_embeds]		, dim=0					).to(dtype=self.unet.dtype		, device=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= torch.cat([hint, hint]		, dim=0					).to(dtype=self.unet.dtype		, device=lowerCamelCase_					)
			self.scheduler.set_timesteps(lowerCamelCase_		, device=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Dict				= self.scheduler.timesteps

			SCREAMING_SNAKE_CASE       :     List[str]				= self.movq.config.latent_channels

			SCREAMING_SNAKE_CASE       :     Any				= downscale_height_and_width(lowerCamelCase_		, lowerCamelCase_		, self.movq_scale_factor					)

			# create initial latent
			SCREAMING_SNAKE_CASE       :     int				= 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
					SCREAMING_SNAKE_CASE       :     Dict				= torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

					SCREAMING_SNAKE_CASE       :     Any				= {'''image_embeds''': image_embeds, '''hint''': hint}
					SCREAMING_SNAKE_CASE       :     Dict				= self.unet(
					    sample=lowerCamelCase_		, timestep=lowerCamelCase_		, encoder_hidden_states=lowerCamelCase_		, added_cond_kwargs=lowerCamelCase_		, return_dict=lowerCamelCase_		, )[0]

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

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

			if output_type == "pil":
					SCREAMING_SNAKE_CASE       :     Optional[int]				= self.numpy_to_pil(lowerCamelCase_					)

			if not return_dict:
					return (image,)

			return ImagePipelineOutput(images=lowerCamelCase_					)







"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase__ :       int						     =			logging.get_logger(__name__)




class 				lowercase__(				_UpperCAmelCase		,						_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """maskformer-swin"""

	UpperCamelCase				=  {
	    """num_attention_heads""": """num_heads""",
	    """num_hidden_layers""": """num_layers""",
	}





	def __init__(						self    :Optional[int]		, lowerCamelCase_    :List[Any]=2_24		, lowerCamelCase_    :Tuple=4		, lowerCamelCase_    :Optional[Any]=3		, lowerCamelCase_    :List[str]=96		, lowerCamelCase_    :int=[2, 2, 6, 2]		, lowerCamelCase_    :Union[str, Any]=[3, 6, 12, 24]		, lowerCamelCase_    :Optional[int]=7		, lowerCamelCase_    :Tuple=4.0		, lowerCamelCase_    :Tuple=True		, lowerCamelCase_    :Dict=0.0		, lowerCamelCase_    :Any=0.0		, lowerCamelCase_    :List[Any]=0.1		, lowerCamelCase_    :Dict="gelu"		, lowerCamelCase_    :Optional[int]=False		, lowerCamelCase_    :List[str]=0.0_2		, lowerCamelCase_    :Any=1E-5		, lowerCamelCase_    :Union[str, Any]=None		, lowerCamelCase_    :List[str]=None		, **lowerCamelCase_    :Union[str, Any]		, )							->			Dict:







			'''simple docstring'''




			super().__init__(**lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     int				= image_size
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= patch_size
			SCREAMING_SNAKE_CASE       :     str				= num_channels
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= embed_dim
			SCREAMING_SNAKE_CASE       :     List[Any]				= depths
			SCREAMING_SNAKE_CASE       :     List[str]				= len(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     str				= num_heads
			SCREAMING_SNAKE_CASE       :     Any				= window_size
			SCREAMING_SNAKE_CASE       :     List[str]				= mlp_ratio
			SCREAMING_SNAKE_CASE       :     str				= qkv_bias
			SCREAMING_SNAKE_CASE       :     Tuple				= hidden_dropout_prob
			SCREAMING_SNAKE_CASE       :     int				= attention_probs_dropout_prob
			SCREAMING_SNAKE_CASE       :     int				= drop_path_rate
			SCREAMING_SNAKE_CASE       :     Tuple				= hidden_act
			SCREAMING_SNAKE_CASE       :     Any				= use_absolute_embeddings
			SCREAMING_SNAKE_CASE       :     List[str]				= layer_norm_eps
			SCREAMING_SNAKE_CASE       :     List[str]				= initializer_range
			# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
			# this indicates the channel dimension after the last stage of the model
			SCREAMING_SNAKE_CASE       :     int				= int(embed_dim * 2 ** (len(lowerCamelCase_					) - 1)					)
			SCREAMING_SNAKE_CASE       :     Dict				= ['''stem'''] + [f"stage{idx}" for idx in range(1		, len(lowerCamelCase_					) + 1					)]
			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     int				= get_aligned_output_features_output_indices(
			    out_features=lowerCamelCase_		, out_indices=lowerCamelCase_		, stage_names=self.stage_names					)





