





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

__lowerCamelCase : Any											=			"\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 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_=8							):

				"""simple docstring"""






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


class       UpperCAmelCase (						_lowercase  ):




		def __init__(self      :	str							,			A__      :	UNetaDConditionModel							,			A__      :	DDPMScheduler							,			A__      :	VQModel							,			)					-> Optional[int]:
						super().__init__()

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




		def 				UpperCAmelCase__   (self      :	str							,			A__      :	int							,			A__      :	Dict							,			A__      :	str							,			A__      :	List[str]							,			A__      :	List[Any]							,			A__      :	str				)					-> Optional[int]:
						if latents is None:
										lowercase         = randn_tensor(A__							,			generator=A__							,			device=A__							,			dtype=A__				)
						else:
										if latents.shape != shape:
														raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'				)
										lowercase         = latents.to(A__				)

						lowercase         = latents * scheduler.init_noise_sigma
						return latents




		def 				UpperCAmelCase__   (self      :	Dict							,			A__      :	Optional[int]=0				)					-> Any:
						if is_accelerate_available():
										from accelerate import cpu_offload
						else:
										raise ImportError("Please install accelerate via `pip install accelerate`"				)

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

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




		def 				UpperCAmelCase__   (self      :	Tuple							,			A__      :	str=0				)					-> str:
						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."				)

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

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

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

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




		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> List[Any]:
						if not hasattr(self.unet							,			"_hf_hook"				):
										return self.device
						for module in self.unet.modules():
										if (
										    hasattr(A__							,			"_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(A__				)
		def __call__(self      :	Union[str, Any]							,			A__      :	Union[torch.FloatTensor, List[torch.FloatTensor]]							,			A__      :	Union[torch.FloatTensor, List[torch.FloatTensor]]							,			A__      :	torch.FloatTensor							,			A__      :	int = 5_1_2							,			A__      :	int = 5_1_2							,			A__      :	int = 1_0_0							,			A__      :	float = 4.0							,			A__      :	int = 1							,			A__      :	Optional[Union[torch.Generator, List[torch.Generator]]] = None							,			A__      :	Optional[torch.FloatTensor] = None							,			A__      :	Optional[str] = "pil"							,			A__      :	bool = True							,			)					-> int:
						lowercase         = self._execution_device

						lowercase         = guidance_scale > 1.0

						if isinstance(A__							,			A__				):
										lowercase         = torch.cat(A__							,			dim=0				)
						if isinstance(A__							,			A__				):
										lowercase         = torch.cat(A__							,			dim=0				)
						if isinstance(A__							,			A__				):
										lowercase         = torch.cat(A__							,			dim=0				)

						lowercase         = image_embeds.shape[0] * num_images_per_prompt

						if do_classifier_free_guidance:
										lowercase         = image_embeds.repeat_interleave(A__							,			dim=0				)
										lowercase         = negative_image_embeds.repeat_interleave(A__							,			dim=0				)
										lowercase         = hint.repeat_interleave(A__							,			dim=0				)

						lowercase         = torch.cat([negative_image_embeds, image_embeds]							,			dim=0				).to(dtype=self.unet.dtype							,			device=A__				)
						lowercase         = torch.cat([hint, hint]							,			dim=0				).to(dtype=self.unet.dtype							,			device=A__				)
						self.scheduler.set_timesteps(A__							,			device=A__				)
						lowercase         = self.scheduler.timesteps

						lowercase         = self.movq.config.latent_channels

						lowercase   ,							lowercase         = downscale_height_and_width(A__							,			A__							,			self.movq_scale_factor				)

						# create initial latent
						lowercase         = self.prepare_latents(
						    (batch_size, num_channels_latents, height, width)							,			image_embeds.dtype							,			A__							,			A__							,			A__							,			self.scheduler							,			)

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

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

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

										# compute the previous noisy sample x_t -> x_t-1
										lowercase         = self.scheduler.step(
										    A__							,			A__							,			A__							,			generator=A__							,			)[0]
						# post-processing
						lowercase         = self.movq.decode(A__							,			force_not_quantize=A__				)["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"]:
										lowercase         = image * 0.5 + 0.5
										lowercase         = image.clamp(0							,			1				)
										lowercase         = image.cpu().permute(0							,			2							,			3							,			1				).float().numpy()

						if output_type == "pil":
										lowercase         = self.numpy_to_pil(A__				)

						if not return_dict:
										return (image,)

						return ImagePipelineOutput(images=A__				)











'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


__lowerCamelCase : List[str]											=			logging.get_logger(__name__)

class       UpperCAmelCase (						_lowercase  ):




		def __init__(self      :	Any							,			A__      :	int							,			A__      :	int							,			A__      :	float							,			**A__      :	int				)					-> List[str]:
						lowercase         = feature_size
						lowercase         = sampling_rate
						lowercase         = padding_value

						lowercase         = kwargs.pop("padding_side"							,			"right"				)
						lowercase         = kwargs.pop("return_attention_mask"							,			A__				)

						super().__init__(**A__				)




		def 				UpperCAmelCase__   (self      :	Tuple							,			A__      :	Union[
		        BatchFeature,
		        List[BatchFeature],
		        Dict[str, BatchFeature],
		        Dict[str, List[BatchFeature]],
		        List[Dict[str, BatchFeature]],
		    ]							,			A__      :	Union[bool, str, PaddingStrategy] = True							,			A__      :	Optional[int] = None							,			A__      :	bool = False							,			A__      :	Optional[int] = None							,			A__      :	Optional[bool] = None							,			A__      :	Optional[Union[str, TensorType]] = None							,			)					-> BatchFeature:
						# If we have a list of dicts, let's convert it in a dict of lists
						# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
						if isinstance(A__							,			(list, tuple)				) and isinstance(processed_features[0]							,			(dict, BatchFeature)				):
										lowercase         = {
										    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
										}

						# The model's main input name, usually `input_values`, has be passed for padding
						if self.model_input_names[0] not in processed_features:
										raise ValueError(
										    "You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
										    f' to this method that includes {self.model_input_names[0]}, but you provided'
										    f' {list(processed_features.keys()				)}'				)

						lowercase         = processed_features[self.model_input_names[0]]
						lowercase         = (
						    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
						)

						if len(A__				) == 0:
										if return_attention_mask:
														lowercase         = []
										return processed_features

						# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
						# and rebuild them afterwards if no return_tensors is specified
						# Note that we lose the specific device the tensor may be on for PyTorch

						lowercase         = required_input[0]
						if isinstance(A__							,			(list, tuple)				):
										# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
										lowercase         = 0
										while len(required_input[index]				) == 0:
														index += 1
										if index < len(A__				):
														lowercase         = required_input[index][0]

						if return_tensors is None:
										if is_tf_tensor(A__				):
														lowercase         = "tf"
										elif is_torch_tensor(A__				):
														lowercase         = "pt"
										elif isinstance(A__							,			(int, float, list, tuple, np.ndarray)				):
														lowercase         = "np"
										else:
														raise ValueError(
														    f'type of {first_element} unknown: {type(A__				)}. '
														    "Should be one of a python, numpy, pytorch or tensorflow object."				)

						for key, value in processed_features.items():
										if isinstance(value[0]							,			(int, float)				):
														lowercase         = to_numpy(A__				)
										else:
														lowercase         = [to_numpy(A__				) for v in value]

        # Convert padding_strategy in PaddingStrategy
						lowercase         = self._get_padding_strategies(padding=A__							,			max_length=A__				)

						lowercase         = processed_features[self.model_input_names[0]]

						lowercase         = len(A__				)
						if not all(len(A__				) == batch_size for v in processed_features.values()				):
										raise ValueError("Some items in the output dictionary have a different batch size than others."				)

						lowercase         = []
						for i in range(A__				):
										lowercase         = {k: v[i] for k, v in processed_features.items()}
										# truncation
										lowercase         = self._truncate(
										    A__							,			max_length=A__							,			pad_to_multiple_of=A__							,			truncation=A__							,			)
										truncated_inputs.append(A__				)

						if padding_strategy == PaddingStrategy.LONGEST:
										# make sure that `max_length` cannot be longer than the longest truncated length
										lowercase         = max(len(input_slice[self.model_input_names[0]]				) for input_slice in truncated_inputs				)
										lowercase         = PaddingStrategy.MAX_LENGTH

						lowercase         = {}
						for i in range(A__				):
										# padding
										lowercase         = self._pad(
										    truncated_inputs[i]							,			max_length=A__							,			padding_strategy=A__							,			pad_to_multiple_of=A__							,			return_attention_mask=A__							,			)

										for key, value in outputs.items():
														if key not in batch_outputs:
																		lowercase         = []
														if value.dtype is np.dtype(np.floataa				):
																		lowercase         = value.astype(np.floataa				)
														batch_outputs[key].append(A__				)

						return BatchFeature(A__							,			tensor_type=A__				)




		def 				UpperCAmelCase__   (self      :	Union[str, Any]							,			A__      :	Union[Dict[str, np.ndarray], BatchFeature]							,			A__      :	Optional[int] = None							,			A__      :	PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,			A__      :	Optional[int] = None							,			A__      :	Optional[bool] = None							,			)					-> dict:
						lowercase         = processed_features[self.model_input_names[0]]

						if padding_strategy == PaddingStrategy.LONGEST:
										lowercase         = len(A__				)

						if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
										lowercase         = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

						lowercase         = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A__				) < max_length

						if return_attention_mask and "attention_mask" not in processed_features:
										lowercase         = np.ones(len(A__				)							,			dtype=np.intaa				)

						if needs_to_be_padded:
										lowercase         = max_length - len(A__				)
										if self.padding_side == "right":
														if return_attention_mask:
																		lowercase         = np.pad(
																		    processed_features["attention_mask"]							,			(0, difference)				)
														lowercase         = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
														lowercase         = np.pad(
														    A__							,			A__							,			"constant"							,			constant_values=self.padding_value				)
										elif self.padding_side == "left":
														if return_attention_mask:
																		lowercase         = np.pad(
																		    processed_features["attention_mask"]							,			(difference, 0)				)
														lowercase         = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
														lowercase         = np.pad(
														    A__							,			A__							,			"constant"							,			constant_values=self.padding_value				)
										else:
														raise ValueError("Invalid padding strategy:" + str(self.padding_side				)				)

						return processed_features




		def 				UpperCAmelCase__   (self      :	Dict							,			A__      :	Union[Dict[str, np.ndarray], BatchFeature]							,			A__      :	Optional[int] = None							,			A__      :	Optional[int] = None							,			A__      :	Optional[bool] = None							,			)					-> str:
						if not truncation:
										return processed_features
						elif truncation and max_length is None:
										raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined."				)

						lowercase         = processed_features[self.model_input_names[0]]

						# find `max_length` that fits `pad_to_multiple_of`
						if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
										lowercase         = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

						lowercase         = len(A__				) > max_length

						if needs_to_be_truncated:
										lowercase         = processed_features[self.model_input_names[0]][:max_length]
										if "attention_mask" in processed_features:
														lowercase         = processed_features["attention_mask"][:max_length]

						return processed_features





		def 				UpperCAmelCase__   (self      :	int							,			A__      :	Optional[Any]=False							,			A__      :	Dict=None				)					-> Optional[int]:

						# Get padding strategy
						if padding is not False:
										if padding is True:
														lowercase         = PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
										elif not isinstance(A__							,			A__				):
														lowercase         = PaddingStrategy(A__				)
										elif isinstance(A__							,			A__				):
														lowercase         = padding
						else:
										lowercase         = PaddingStrategy.DO_NOT_PAD

						# Set max length if needed
						if max_length is None:
										if padding_strategy == PaddingStrategy.MAX_LENGTH:
														raise ValueError(
														    f'When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'				)

        # Test if we have a padding value
						if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
										raise ValueError(
										    "Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
										    " as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."				)

						return padding_strategy




