

"""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,
)


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

_UpperCamelCase							=      '\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 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Any    ,						SCREAMING_SNAKE_CASE       :							Tuple    ,						SCREAMING_SNAKE_CASE       :							Tuple=8   ):


							'''simple docstring'''







							__lowerCamelCase      :      Tuple         =height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							__lowerCamelCase      :      Tuple         =width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""




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

											self.register_modules(
											    unet=__lowercase						,    scheduler=__lowercase						,    movq=__lowercase						,    )
											__lowerCamelCase      :      Any         =2 ** (len(self.movq.config.block_out_channels			) - 1)




				def 					__lowercase  (     self  :int						,    __lowercase  :Tuple						,    __lowercase  :Dict						,    __lowercase  :str						,    __lowercase  :Dict						,    __lowercase  :Union[str, Any]						,    __lowercase  :Dict			):
											if latents is None:
																		__lowerCamelCase      :      str         =randn_tensor(__lowercase						,    generator=__lowercase						,    device=__lowercase						,    dtype=__lowercase			)
											else:
																		if latents.shape != shape:
																									raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'			)
																		__lowerCamelCase      :      str         =latents.to(__lowercase			)

											__lowerCamelCase      :      Any         =latents * scheduler.init_noise_sigma
											return latents




				def 					__lowercase  (     self  :Dict						,    __lowercase  :Any=0			):
											if is_accelerate_available():
																		from accelerate import cpu_offload
											else:
																		raise ImportError('''Please install accelerate via `pip install accelerate`'''			)

											__lowerCamelCase      :      Any         =torch.device(f'cuda:{gpu_id}'			)

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




				def 					__lowercase  (     self  :Optional[int]						,    __lowercase  :List[Any]=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.'''			)

											__lowerCamelCase      :      str         =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)

											__lowerCamelCase      :      Optional[Any]         =None
											for cpu_offloaded_model in [self.unet, self.movq]:
																		__lowerCamelCase      :      Union[str, Any]         =cpu_offload_with_hook(__lowercase						,    __lowercase						,    prev_module_hook=__lowercase			)

											# We'll offload the last model manually.
											__lowerCamelCase      :      Dict         =hook




				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def 					__lowercase  (     self  :Tuple			):
											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  :Union[str, Any]						,    __lowercase  :Union[torch.FloatTensor, List[torch.FloatTensor]]						,    __lowercase  :Union[torch.FloatTensor, List[torch.FloatTensor]]						,    __lowercase  :torch.FloatTensor						,    __lowercase  :int = 512						,    __lowercase  :int = 512						,    __lowercase  :int = 100						,    __lowercase  :float = 4.0						,    __lowercase  :int = 1						,    __lowercase  :Optional[Union[torch.Generator, List[torch.Generator]]] = None						,    __lowercase  :Optional[torch.FloatTensor] = None						,    __lowercase  :Optional[str] = "pil"						,    __lowercase  :bool = True						,    ):
											__lowerCamelCase      :      Any         =self._execution_device

											__lowerCamelCase      :      Dict         =guidance_scale > 1.0

											if isinstance(__lowercase						,    __lowercase			):
																		__lowerCamelCase      :      Any         =torch.cat(__lowercase						,    dim=0			)
											if isinstance(__lowercase						,    __lowercase			):
																		__lowerCamelCase      :      Dict         =torch.cat(__lowercase						,    dim=0			)
											if isinstance(__lowercase						,    __lowercase			):
																		__lowerCamelCase      :      Dict         =torch.cat(__lowercase						,    dim=0			)

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

											if do_classifier_free_guidance:
																		__lowerCamelCase      :      Optional[int]         =image_embeds.repeat_interleave(__lowercase						,    dim=0			)
																		__lowerCamelCase      :      Any         =negative_image_embeds.repeat_interleave(__lowercase						,    dim=0			)
																		__lowerCamelCase      :      List[Any]         =hint.repeat_interleave(__lowercase						,    dim=0			)

											__lowerCamelCase      :      List[str]         =torch.cat([negative_image_embeds, image_embeds]						,    dim=0			).to(dtype=self.unet.dtype						,    device=__lowercase			)
											__lowerCamelCase      :      List[str]         =torch.cat([hint, hint]						,    dim=0			).to(dtype=self.unet.dtype						,    device=__lowercase			)
											self.scheduler.set_timesteps(__lowercase						,    device=__lowercase			)
											__lowerCamelCase      :      str         =self.scheduler.timesteps

											__lowerCamelCase      :      Tuple         =self.movq.config.latent_channels

											__lowerCamelCase      :      List[str]         =downscale_height_and_width(__lowercase						,    __lowercase						,    self.movq_scale_factor			)

											# create initial latent
											__lowerCamelCase      :      Optional[int]         =self.prepare_latents(
											    (batch_size, num_channels_latents, height, width)						,    image_embeds.dtype						,    __lowercase						,    __lowercase						,    __lowercase						,    self.scheduler						,    )

											for i, t in enumerate(self.progress_bar(__lowercase			)			):
																		# expand the latents if we are doing classifier free guidance
																		__lowerCamelCase      :      int         =torch.cat([latents] * 2			) if do_classifier_free_guidance else latents

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

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

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

											if output_type == "pil":
																		__lowerCamelCase      :      Optional[Any]         =self.numpy_to_pil(__lowercase			)

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=__lowercase			)











"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


_UpperCamelCase							=      logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       List[str]	 =		["""pixel_values"""]




				def __init__(     self  :List[str]						,    __lowercase  :bool = True						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :PILImageResampling = PIL.Image.BICUBIC						,    __lowercase  :bool = True						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :Union[int, float] = 1 / 255						,    __lowercase  :bool = True						,    __lowercase  :bool = True						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    **__lowercase  :Union[str, Any]						,    ):
											super().__init__(**__lowercase			)
											__lowerCamelCase      :      Dict         =size if size is not None else {'''height''': 256, '''width''': 256}
											__lowerCamelCase      :      Union[str, Any]         =get_size_dict(__lowercase			)
											__lowerCamelCase      :      Tuple         =crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
											__lowerCamelCase      :      int         =get_size_dict(__lowercase						,    param_name='''crop_size'''			)

											__lowerCamelCase      :      Tuple         =do_resize
											__lowerCamelCase      :      int         =size
											__lowerCamelCase      :      Union[str, Any]         =resample
											__lowerCamelCase      :      Dict         =do_center_crop
											__lowerCamelCase      :      Any         =crop_size
											__lowerCamelCase      :      Dict         =do_rescale
											__lowerCamelCase      :      List[Any]         =rescale_factor
											__lowerCamelCase      :      Any         =do_normalize
											__lowerCamelCase      :      int         =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__lowerCamelCase      :      List[str]         =image_std if image_std is not None else IMAGENET_STANDARD_STD




				def 					__lowercase  (     self  :Union[str, Any]						,    __lowercase  :np.ndarray						,    __lowercase  :Dict[str, int]						,    __lowercase  :PILImageResampling = PIL.Image.BICUBIC						,    __lowercase  :Optional[Union[str, ChannelDimension]] = None						,    **__lowercase  :Optional[int]						,    ):
											__lowerCamelCase      :      Optional[int]         =get_size_dict(__lowercase			)
											if "height" not in size or "width" not in size:
																		raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'			)
											return resize(
											    __lowercase						,    size=(size['''height'''], size['''width'''])						,    resample=__lowercase						,    data_format=__lowercase						,    **__lowercase			)




				def 					__lowercase  (     self  :Any						,    __lowercase  :np.ndarray						,    __lowercase  :Dict[str, int]						,    __lowercase  :Optional[Union[str, ChannelDimension]] = None						,    **__lowercase  :Optional[Any]						,    ):
											__lowerCamelCase      :      Dict         =get_size_dict(__lowercase			)
											if "height" not in size or "width" not in size:
																		raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'			)
											return center_crop(__lowercase						,    size=(size['''height'''], size['''width'''])						,    data_format=__lowercase						,    **__lowercase			)




				def 					__lowercase  (     self  :int						,    __lowercase  :np.ndarray						,    __lowercase  :Union[int, float]						,    __lowercase  :Optional[Union[str, ChannelDimension]] = None						,    **__lowercase  :List[Any]						,    ):
											return rescale(__lowercase						,    scale=__lowercase						,    data_format=__lowercase						,    **__lowercase			)




				def 					__lowercase  (     self  :Dict						,    __lowercase  :np.ndarray						,    __lowercase  :Union[float, List[float]]						,    __lowercase  :Union[float, List[float]]						,    __lowercase  :Optional[Union[str, ChannelDimension]] = None						,    **__lowercase  :int						,    ):
											return normalize(__lowercase						,    mean=__lowercase						,    std=__lowercase						,    data_format=__lowercase						,    **__lowercase			)




				def 					__lowercase  (     self  :Any						,    __lowercase  :ImageInput						,    __lowercase  :bool = None						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :Optional[Any]=None						,    __lowercase  :bool = None						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :bool = None						,    __lowercase  :float = None						,    __lowercase  :bool = None						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    __lowercase  :Optional[Union[str, TensorType]] = None						,    __lowercase  :ChannelDimension = ChannelDimension.FIRST						,    **__lowercase  :Any						,    ):
											__lowerCamelCase      :      Optional[Any]         =do_resize if do_resize is not None else self.do_resize
											__lowerCamelCase      :      List[Any]         =resample if resample is not None else self.resample
											__lowerCamelCase      :      Optional[int]         =do_center_crop if do_center_crop is not None else self.do_center_crop
											__lowerCamelCase      :      List[str]         =do_rescale if do_rescale is not None else self.do_rescale
											__lowerCamelCase      :      Optional[Any]         =rescale_factor if rescale_factor is not None else self.rescale_factor
											__lowerCamelCase      :      Optional[int]         =do_normalize if do_normalize is not None else self.do_normalize
											__lowerCamelCase      :      Dict         =image_mean if image_mean is not None else self.image_mean
											__lowerCamelCase      :      Union[str, Any]         =image_std if image_std is not None else self.image_std

											__lowerCamelCase      :      Dict         =size if size is not None else self.size
											__lowerCamelCase      :      int         =get_size_dict(__lowercase			)
											__lowerCamelCase      :      List[Any]         =crop_size if crop_size is not None else self.crop_size
											__lowerCamelCase      :      Optional[int]         =get_size_dict(__lowercase						,    param_name='''crop_size'''			)

											__lowerCamelCase      :      int         =make_list_of_images(__lowercase			)

											if not valid_images(__lowercase			):
																		raise ValueError(
																		    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																		    '''torch.Tensor, tf.Tensor or jax.ndarray.'''			)

											if do_resize and size is None or resample is None:
																		raise ValueError('''Size and resample must be specified if do_resize is True.'''			)

											if do_center_crop and crop_size is None:
																		raise ValueError('''Crop size must be specified if do_center_crop is True.'''			)

											if do_rescale and rescale_factor is None:
																		raise ValueError('''Rescale factor must be specified if do_rescale is True.'''			)

											if do_normalize and (image_mean is None or image_std is None):
																		raise ValueError('''Image mean and std must be specified if do_normalize is True.'''			)

											# All transformations expect numpy arrays.
											__lowerCamelCase      :      str         =[to_numpy_array(__lowercase			) for image in images]

											if do_resize:
																		__lowerCamelCase      :      Union[str, Any]         =[self.resize(image=__lowercase						,    size=__lowercase						,    resample=__lowercase			) for image in images]

											if do_center_crop:
																		__lowerCamelCase      :      Optional[Any]         =[self.center_crop(image=__lowercase						,    size=__lowercase			) for image in images]

											if do_rescale:
																		__lowerCamelCase      :      str         =[self.rescale(image=__lowercase						,    scale=__lowercase			) for image in images]

											if do_normalize:
																		__lowerCamelCase      :      int         =[self.normalize(image=__lowercase						,    mean=__lowercase						,    std=__lowercase			) for image in images]

											__lowerCamelCase      :      Tuple         =[to_channel_dimension_format(__lowercase						,    __lowercase			) for image in images]

											__lowerCamelCase      :      Any         ={'''pixel_values''': images}
											return BatchFeature(data=__lowercase						,    tensor_type=__lowercase			)




