



'''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,
)


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

snake_case_					:      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__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_=8				):
						_UpperCamelCase   :						List[Any]												=  height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						_UpperCamelCase   :						List[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__      (				_a  ):





	def __init__(					self   :							Tuple					,lowerCamelCase__   :							UNetaDConditionModel					,lowerCamelCase__   :							DDPMScheduler					,lowerCamelCase__   :							VQModel					,):







							'''simple docstring'''
							super().__init__()

							self.register_modules(
							    unet=lowerCamelCase__					,scheduler=lowerCamelCase__					,movq=lowerCamelCase__					,)
							_UpperCamelCase   :						List[Any]												=  2 ** (len(self.movq.config.block_out_channels					) - 1)





	def       UpperCamelCase_    (					self   :							Tuple					,lowerCamelCase__   :							Union[str, Any]					,lowerCamelCase__   :							str					,lowerCamelCase__   :							Tuple					,lowerCamelCase__   :							Tuple					,lowerCamelCase__   :							Optional[Any]					,lowerCamelCase__   :							str					):







							'''simple docstring'''

							if latents is None:
													_UpperCamelCase   :						int												=  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}'					)
													_UpperCamelCase   :						Tuple												=  latents.to(lowerCamelCase__					)

							_UpperCamelCase   :						int												=  latents * scheduler.init_noise_sigma
							return latents





	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							List[str]=0					):







							'''simple docstring'''

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

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

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





	def       UpperCamelCase_    (					self   :							Optional[Any]					,lowerCamelCase__   :							Optional[Any]=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.'					)

							_UpperCamelCase   :						Dict												=  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)

							_UpperCamelCase   :						Optional[Any]												=  None
							for cpu_offloaded_model in [self.unet, self.movq]:
													_UpperCamelCase							,     _UpperCamelCase   :						Optional[int]												=  cpu_offload_with_hook(lowerCamelCase__					,lowerCamelCase__					,prev_module_hook=lowerCamelCase__					)

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





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







							'''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   :							Union[str, Any]					,lowerCamelCase__   :							Union[torch.FloatTensor, List[torch.FloatTensor]]					,lowerCamelCase__   :							Union[torch.FloatTensor, List[torch.FloatTensor]]					,lowerCamelCase__   :							torch.FloatTensor					,lowerCamelCase__   :							int = 512					,lowerCamelCase__   :							int = 512					,lowerCamelCase__   :							int = 100					,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					,):







							'''simple docstring'''
							_UpperCamelCase   :						Tuple												=  self._execution_device

							_UpperCamelCase   :						int												=  guidance_scale > 1.0

							if isinstance(lowerCamelCase__					,lowerCamelCase__					):
													_UpperCamelCase   :						Optional[int]												=  torch.cat(lowerCamelCase__					,dim=0					)
							if isinstance(lowerCamelCase__					,lowerCamelCase__					):
													_UpperCamelCase   :						int												=  torch.cat(lowerCamelCase__					,dim=0					)
							if isinstance(lowerCamelCase__					,lowerCamelCase__					):
													_UpperCamelCase   :						Dict												=  torch.cat(lowerCamelCase__					,dim=0					)

							_UpperCamelCase   :						Dict												=  image_embeds.shape[0] * num_images_per_prompt

							if do_classifier_free_guidance:
													_UpperCamelCase   :						int												=  image_embeds.repeat_interleave(lowerCamelCase__					,dim=0					)
													_UpperCamelCase   :						Union[str, Any]												=  negative_image_embeds.repeat_interleave(lowerCamelCase__					,dim=0					)
													_UpperCamelCase   :						Optional[Any]												=  hint.repeat_interleave(lowerCamelCase__					,dim=0					)

							_UpperCamelCase   :						Union[str, Any]												=  torch.cat([negative_image_embeds, image_embeds]					,dim=0					).to(dtype=self.unet.dtype					,device=lowerCamelCase__					)
							_UpperCamelCase   :						str												=  torch.cat([hint, hint]					,dim=0					).to(dtype=self.unet.dtype					,device=lowerCamelCase__					)
							self.scheduler.set_timesteps(lowerCamelCase__					,device=lowerCamelCase__					)
							_UpperCamelCase   :						Any												=  self.scheduler.timesteps

							_UpperCamelCase   :						Dict												=  self.movq.config.latent_channels

							_UpperCamelCase							,     _UpperCamelCase   :						Tuple												=  downscale_height_and_width(lowerCamelCase__					,lowerCamelCase__					,self.movq_scale_factor					)

							# create initial latent
							_UpperCamelCase   :						str												=  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
													_UpperCamelCase   :						List[Any]												=  torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

													_UpperCamelCase   :						List[str]												=  {'image_embeds': image_embeds, 'hint': hint}
													_UpperCamelCase   :						List[Any]												=  self.unet(
													    sample=lowerCamelCase__					,timestep=lowerCamelCase__					,encoder_hidden_states=lowerCamelCase__					,added_cond_kwargs=lowerCamelCase__					,return_dict=lowerCamelCase__					,)[0]

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

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

							if output_type == "pil":
													_UpperCamelCase   :						str												=  self.numpy_to_pil(lowerCamelCase__					)

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=lowerCamelCase__					)






# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


A				:       Union[str, Any]      =  logging.get_logger(__name__)

A				:       Dict[Optional[str], Type[Formatter]]      =  {}
A				:       Dict[Optional[str], str]      =  {}
A				:       Dict[Optional[str], Exception]      =  {}





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase = None   ,						)    ->						str:


						'''simple docstring'''






						__snake_case				=		aliases if aliases is not None else []
						if format_type in _FORMAT_TYPES:
												logger.warning(
												    F'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''			)
						__snake_case				=		formatter_cls
						for alias in set(aliases + [format_type]			):
												if alias in _FORMAT_TYPES_ALIASES:
																		logger.warning(
																		    F'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''			)
												__snake_case				=		format_type





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase = None			)    ->						List[Any]:


						'''simple docstring'''






						__snake_case				=		aliases if aliases is not None else []
						for alias in set(aliases + [format_type]			):
												__snake_case				=		unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['python'])
_register_formatter(ArrowFormatter, 'arrow', aliases=['pa', 'pyarrow'])
_register_formatter(NumpyFormatter, 'numpy', aliases=['np'])
_register_formatter(PandasFormatter, 'pandas', aliases=['pd'])
_register_formatter(CustomFormatter, 'custom')

if config.TORCH_AVAILABLE:
	from .torch_formatter import TorchFormatter

	_register_formatter(TorchFormatter, 'torch', aliases=['pt', 'pytorch'])
else:
	A				:       Any      =  ValueError('PyTorch needs to be installed to be able to return PyTorch tensors.')
	_register_unavailable_formatter(_torch_error, 'torch', aliases=['pt', 'pytorch'])

if config.TF_AVAILABLE:
	from .tf_formatter import TFFormatter

	_register_formatter(TFFormatter, 'tensorflow', aliases=['tf'])
else:
	A				:       Tuple      =  ValueError('Tensorflow needs to be installed to be able to return Tensorflow tensors.')
	_register_unavailable_formatter(_tf_error, 'tensorflow', aliases=['tf'])

if config.JAX_AVAILABLE:
	from .jax_formatter import JaxFormatter

	_register_formatter(JaxFormatter, 'jax', aliases=[])
else:
	A				:       Optional[int]      =  ValueError('JAX needs to be installed to be able to return JAX arrays.')
	_register_unavailable_formatter(_jax_error, 'jax', aliases=[])





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						Optional[str]:


						'''simple docstring'''






						if format_type in _FORMAT_TYPES_ALIASES:
												return _FORMAT_TYPES_ALIASES[format_type]
						else:
												return format_type





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						**_lowerCAmelCase			)    ->						Formatter:


						'''simple docstring'''






						__snake_case				=		get_format_type_from_alias(_lowerCAmelCase			)
						if format_type in _FORMAT_TYPES:
												return _FORMAT_TYPES[format_type](**_lowerCAmelCase			)
						if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
												raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
						else:
												raise ValueError(
												    F'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None			)}, but got \'{format_type}\''''			)





