






"""simple docstring"""
import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD

def 							snake_case (  A__		):
			def wrapper(*A__      ,**A__		):
						UpperCAmelCase_	:					Union[str, Any]     	=   timeit.default_timer()
						UpperCAmelCase_	:					Union[str, Any]     	=   func(*A__      ,**A__		)
						UpperCAmelCase_	:					Dict     	=   timeit.default_timer() - starttime
						return delta

			UpperCAmelCase_	:					Optional[Any]     	=   func.__name__

			return wrapper

def 							snake_case (  A__      ,A__=1_00      ,A__=None		):
			UpperCAmelCase_	:					str     	=   []
			UpperCAmelCase_	:					List[str]     	=   seq_shapes or {}
			for i in range(A__		):
						UpperCAmelCase_	:					int     	=   {}
						for col_id, (k, v) in enumerate(features.items()		):
									if isinstance(A__      ,_ArrayXD		):
												UpperCAmelCase_	:					Optional[int]     	=   np.random.rand(*v.shape		).astype(v.dtype		)
									elif isinstance(A__      ,datasets.Value		):
												if v.dtype == "string":
															UpperCAmelCase_	:					List[str]     	=   "The small grey turtle was surprisingly fast when challenged."
												else:
															UpperCAmelCase_	:					List[str]     	=   np.random.randint(10      ,size=1		).astype(v.dtype		).item()
									elif isinstance(A__      ,datasets.Sequence		):
												while isinstance(A__      ,datasets.Sequence		):
															UpperCAmelCase_	:					List[str]     	=   v.feature
												UpperCAmelCase_	:					List[Any]     	=   seq_shapes[k]
												UpperCAmelCase_	:					Dict     	=   np.random.rand(*A__		).astype(v.dtype		)
									UpperCAmelCase_	:					Dict     	=   data

						dummy_data.append((i, example)		)

			return dummy_data




def 							snake_case (  A__      ,A__      ,A__=1_00      ,A__=None		):
			UpperCAmelCase_	:					Optional[Any]     	=   generate_examples(A__      ,num_examples=A__      ,seq_shapes=A__		)

			with ArrowWriter(features=A__      ,path=A__		) as writer:
						for key, record in dummy_data:
									UpperCAmelCase_	:					Any     	=   features.encode_example(A__		)
									writer.write(A__		)

						UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   writer.finalize()

			if not num_final_examples == num_examples:
						raise ValueError(
						    F"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""		)

			UpperCAmelCase_	:					List[Any]     	=   datasets.Dataset.from_file(filename=A__      ,info=datasets.DatasetInfo(features=A__		)		)

			return dataset






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			 ="""
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")


        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> 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\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE=8      ):
	UpperCAmelCase__:	int									= height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	UpperCAmelCase__:	Optional[Any]									= width // scale_factor**2
	if width % scale_factor**2 != 0:
		new_width += 1
	return new_height * scale_factor, new_width * scale_factor

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					def __init__(				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      ):
						super().__init__()

						self.register_modules(
						    unet=lowerCamelCase__				,      scheduler=lowerCamelCase__				,      movq=lowerCamelCase__				,      )
						UpperCAmelCase__:	Optional[int]									= 2 ** (len(self.movq.config.block_out_channels						) - 1)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						if latents is None:
							UpperCAmelCase__:	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}"						)
							UpperCAmelCase__:	Tuple									= latents.to(lowerCamelCase__						)

						UpperCAmelCase__:	Union[str, Any]									= latents * scheduler.init_noise_sigma
						return latents





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__=0						):
						if is_accelerate_available():
							from accelerate import cpu_offload
						else:
							raise ImportError("Please install accelerate via `pip install accelerate`"						)

						UpperCAmelCase__:	Any									= torch.device(F"cuda:{gpu_id}"						)

						UpperCAmelCase__:	int									= [
						    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						):
						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__:	Union[str, Any]									= 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__:	int									= None
						for cpu_offloaded_model in [self.unet, self.movq]:
							UpperCAmelCase__							,						UpperCAmelCase__:	Tuple									= cpu_offload_with_hook(lowerCamelCase__				,      lowerCamelCase__				,      prev_module_hook=lowerCamelCase__						)

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





					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def    _UpperCAmelCase    (				self						):
						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__ = 5_1_2				,      lowerCamelCase__ = 5_1_2				,      lowerCamelCase__ = 1_0_0				,      lowerCamelCase__ = 4.0				,      lowerCamelCase__ = 1				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = "pil"				,      lowerCamelCase__ = True				,      ):
						UpperCAmelCase__:	str									= self._execution_device

						UpperCAmelCase__:	List[Any]									= guidance_scale > 1.0

						if isinstance(lowerCamelCase__				,      lowerCamelCase__						):
							UpperCAmelCase__:	List[Any]									= torch.cat(lowerCamelCase__				,      dim=0						)
						if isinstance(lowerCamelCase__				,      lowerCamelCase__						):
							UpperCAmelCase__:	List[Any]									= torch.cat(lowerCamelCase__				,      dim=0						)
						if isinstance(lowerCamelCase__				,      lowerCamelCase__						):
							UpperCAmelCase__:	Any									= torch.cat(lowerCamelCase__				,      dim=0						)

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

						if do_classifier_free_guidance:
							UpperCAmelCase__:	Tuple									= image_embeds.repeat_interleave(lowerCamelCase__				,      dim=0						)
							UpperCAmelCase__:	Union[str, Any]									= negative_image_embeds.repeat_interleave(lowerCamelCase__				,      dim=0						)
							UpperCAmelCase__:	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__:	Dict									= torch.cat([hint, hint]				,      dim=0						).to(dtype=self.unet.dtype				,      device=lowerCamelCase__						)
						self.scheduler.set_timesteps(lowerCamelCase__				,      device=lowerCamelCase__						)
						UpperCAmelCase__:	Optional[Any]									= self.scheduler.timesteps

						UpperCAmelCase__:	Optional[int]									= self.movq.config.latent_channels

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

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

							UpperCAmelCase__:	List[str]									= {"image_embeds": image_embeds, "hint": hint}
							UpperCAmelCase__:	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__:	str									= noise_pred.split(latents.shape[1]				,      dim=1						)
								UpperCAmelCase__							,						UpperCAmelCase__:	Any									= noise_pred.chunk(2						)
								UpperCAmelCase__							,						UpperCAmelCase__:	Any									= variance_pred.chunk(2						)
								UpperCAmelCase__:	List[str]									= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
								UpperCAmelCase__:	Dict									= 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__:	int									= noise_pred.split(latents.shape[1]				,      dim=1						)

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

						if output_type == "pil":
							UpperCAmelCase__:	List[str]									= self.numpy_to_pil(lowerCamelCase__						)

						if not return_dict:
							return (image,)

						return ImagePipelineOutput(images=lowerCamelCase__						)