






import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset






@pytest.mark.parametrize('''dataset_size'''		,   [None, 400 * 2**20, 600 * 2**20]				)
@pytest.mark.parametrize('''input_in_memory_max_size'''		,   ['''default''', 0, 100 * 2**20, 900 * 2**20]				)
def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				if input_in_memory_max_size != "default":
								monkeypatch.setattr(datasets.config		,   '''IN_MEMORY_MAX_SIZE'''		,   lowerCamelCase_				)
				lowercase__    =			datasets.config.IN_MEMORY_MAX_SIZE
				if input_in_memory_max_size == "default":
								assert in_memory_max_size == 0
				else:
								assert in_memory_max_size == input_in_memory_max_size
				if dataset_size and in_memory_max_size:
								lowercase__    =			dataset_size < in_memory_max_size
				else:
								lowercase__    =			False
				lowercase__    =			is_small_dataset(lowerCamelCase_				)
				assert result == expected











"""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     :		List[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 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase=8       ):
							A		=  height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							A		=  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			:Any     ,       __UpperCamelCase			:UNetaDConditionModel     ,       __UpperCamelCase			:DDPMScheduler     ,       __UpperCamelCase			:VQModel     ,       ):
												super().__init__()

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


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

												A		=  latents * scheduler.init_noise_sigma
												return latents


					def 	lowerCamelCase       (					self			:Tuple     ,       __UpperCamelCase			:Any=0		):
												if is_accelerate_available():
																			from accelerate import cpu_offload
												else:
																			raise ImportError("Please install accelerate via `pip install accelerate`"		)

												A		=  torch.device(f"cuda:{gpu_id}"		)

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


					def 	lowerCamelCase       (					self			:Dict     ,       __UpperCamelCase			:int=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."		)

												A		=  torch.device(f"cuda:{gpu_id}"		)

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

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

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


					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def 	lowerCamelCase       (					self			:str		):
												if not hasattr(self.unet     ,       "_hf_hook"		):
																			return self.device
												for module in self.unet.modules():
																			if (
																			    hasattr(__UpperCamelCase     ,       "_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(__UpperCamelCase		)
					def __call__(					self			:List[Any]     ,       __UpperCamelCase			:Union[torch.FloatTensor, List[torch.FloatTensor]]     ,       __UpperCamelCase			:Union[torch.FloatTensor, List[torch.FloatTensor]]     ,       __UpperCamelCase			:torch.FloatTensor     ,       __UpperCamelCase			:int = 5_12     ,       __UpperCamelCase			:int = 5_12     ,       __UpperCamelCase			:int = 1_00     ,       __UpperCamelCase			:float = 4.0     ,       __UpperCamelCase			:int = 1     ,       __UpperCamelCase			:Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,       __UpperCamelCase			:Optional[torch.FloatTensor] = None     ,       __UpperCamelCase			:Optional[str] = "pil"     ,       __UpperCamelCase			:bool = True     ,       ):
												A		=  self._execution_device

												A		=  guidance_scale > 1.0

												if isinstance(__UpperCamelCase     ,       __UpperCamelCase		):
																			A		=  torch.cat(__UpperCamelCase     ,       dim=0		)
												if isinstance(__UpperCamelCase     ,       __UpperCamelCase		):
																			A		=  torch.cat(__UpperCamelCase     ,       dim=0		)
												if isinstance(__UpperCamelCase     ,       __UpperCamelCase		):
																			A		=  torch.cat(__UpperCamelCase     ,       dim=0		)

												A		=  image_embeds.shape[0] * num_images_per_prompt

												if do_classifier_free_guidance:
																			A		=  image_embeds.repeat_interleave(__UpperCamelCase     ,       dim=0		)
																			A		=  negative_image_embeds.repeat_interleave(__UpperCamelCase     ,       dim=0		)
																			A		=  hint.repeat_interleave(__UpperCamelCase     ,       dim=0		)

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

												A		=  self.movq.config.latent_channels

												A,	A		=  downscale_height_and_width(__UpperCamelCase     ,       __UpperCamelCase     ,       self.movq_scale_factor		)

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

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

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

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

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

												if output_type == "pil":
																			A		=  self.numpy_to_pil(__UpperCamelCase		)

												if not return_dict:
																			return (image,)

												return ImagePipelineOutput(images=__UpperCamelCase		)


