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

lowerCamelCase__	=							"\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n        >>> pipe_prior.to(\"cuda\")\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n        >>> pipe.to(\"cuda\")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save(\"cat.png\")\n        ```\n"


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase=8 )		->				Tuple:

		"""simple docstring"""






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







class        lowerCAmelCase__      (   __SCREAMING_SNAKE_CASE	):

						def __init__(			self       ,							a       ,							a       ,							a       ,							)					->      Tuple:


								'''simple docstring'''

								super().__init__()

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

						def  A_				(			self       ,							a       ,							a       ,							a       ,							a       ,							a       ,							a				)					->      int:


								'''simple docstring'''

								if latents is None:
										_UpperCamelCase						    =	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}'				)
										_UpperCamelCase						    =	latents.to(_a				)

								_UpperCamelCase						    =	latents * scheduler.init_noise_sigma
								return latents

						def  A_				(			self       ,							a=0				)					->      Dict:


								'''simple docstring'''

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

								_UpperCamelCase						    =	torch.device(F'cuda:{gpu_id}'				)

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

						def  A_				(			self       ,							a=0				)					->      List[str]:


								'''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						    =	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)

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

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

						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								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       ,							a       ,							a       ,							a = 5_12       ,							a = 5_12       ,							a = 1_00       ,							a = 4.0       ,							a = 1       ,							a = None       ,							a = None       ,							a = "pil"       ,							a = True       ,							)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	self._execution_device

								_UpperCamelCase						    =	guidance_scale > 1.0

								if isinstance(_a       ,							_a				):
										_UpperCamelCase						    =	torch.cat(_a       ,							dim=0				)
								_UpperCamelCase						    =	image_embeds.shape[0] * num_images_per_prompt
								if isinstance(_a       ,							_a				):
										_UpperCamelCase						    =	torch.cat(_a       ,							dim=0				)

								if do_classifier_free_guidance:
										_UpperCamelCase						    =	image_embeds.repeat_interleave(_a       ,							dim=0				)
										_UpperCamelCase						    =	negative_image_embeds.repeat_interleave(_a       ,							dim=0				)

								_UpperCamelCase						    =	torch.cat([negative_image_embeds, image_embeds]       ,							dim=0				).to(dtype=self.unet.dtype       ,							device=_a				)

								self.scheduler.set_timesteps(_a       ,							device=_a				)
								_UpperCamelCase						    =	self.scheduler.timesteps

								_UpperCamelCase						    =	self.unet.config.in_channels

								_UpperCamelCase						    =	downscale_height_and_width(_a       ,							_a       ,							self.movq_scale_factor				)

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

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

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

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

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

								if not return_dict:
										return (image,)

								return ImagePipelineOutput(images=_a				)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase__	=							{"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase__	=							["YolosFeatureExtractor"]
		lowerCamelCase__	=							["YolosImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase__	=							[
		    "YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "YolosForObjectDetection",
		    "YolosModel",
		    "YolosPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_yolos import YolosFeatureExtractor
				from .image_processing_yolos import YolosImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_yolos import (
				    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    YolosForObjectDetection,
				    YolosModel,
				    YolosPreTrainedModel,
				)


else:
		import sys

		lowerCamelCase__	=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



