'''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,
)


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

__lowercase        =    '''
    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 							snake_case__				(     _A:   List[str] ,  _A:   Dict ,  _A:   Optional[Any]=8					)       ->      Optional[int]:

		'''simple docstring'''


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






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			def __init__(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__()

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



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					if latents is None:
							lowerCAmelCase     =    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}")
							lowerCAmelCase     =    latents.to(__lowerCAmelCase)

					lowerCAmelCase     =    latents * scheduler.init_noise_sigma
					return latents



			def 				a_			(  self			,       __lowerCAmelCase=0):




					"""simple docstring"""


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

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

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



			def 				a_			(  self			,       __lowerCAmelCase=0):




					"""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.""")

					lowerCAmelCase     =    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)

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

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



			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 				a_			(  self):




					"""simple docstring"""


					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 = 512			,       __lowerCAmelCase = 512			,       __lowerCAmelCase = 100			,       __lowerCAmelCase = 4.0			,       __lowerCAmelCase = 1			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = "pil"			,       __lowerCAmelCase = True			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    self._execution_device

					lowerCAmelCase     =    guidance_scale > 1.0

					if isinstance(__lowerCAmelCase			,       __lowerCAmelCase):
							lowerCAmelCase     =    torch.cat(__lowerCAmelCase			,       dim=0)
					if isinstance(__lowerCAmelCase			,       __lowerCAmelCase):
							lowerCAmelCase     =    torch.cat(__lowerCAmelCase			,       dim=0)
					if isinstance(__lowerCAmelCase			,       __lowerCAmelCase):
							lowerCAmelCase     =    torch.cat(__lowerCAmelCase			,       dim=0)

					lowerCAmelCase     =    image_embeds.shape[0] * num_images_per_prompt

					if do_classifier_free_guidance:
							lowerCAmelCase     =    image_embeds.repeat_interleave(__lowerCAmelCase			,       dim=0)
							lowerCAmelCase     =    negative_image_embeds.repeat_interleave(__lowerCAmelCase			,       dim=0)
							lowerCAmelCase     =    hint.repeat_interleave(__lowerCAmelCase			,       dim=0)

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

					lowerCAmelCase     =    self.movq.config.latent_channels

					lowerCAmelCase   ,	lowerCAmelCase     =    downscale_height_and_width(__lowerCAmelCase			,       __lowerCAmelCase			,       self.movq_scale_factor)

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

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

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

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

					if output_type == "pil":
							lowerCAmelCase     =    self.numpy_to_pil(__lowerCAmelCase)

					if not return_dict:
							return (image,)

					return ImagePipelineOutput(images=__lowerCAmelCase)





'''simple docstring'''




import os
from distutils.util import strtobool






def 							snake_case__				(     _A:   int ,  _A:   List[Any]					)       ->      Union[str, Any]:

		'''simple docstring'''


		for e in env_keys:
				lowerCAmelCase     =    int(os.environ.get(_A ,  -1					)					)
				if val >= 0:
						return val
		return default






def 							snake_case__				(     _A:   str ,  _A:   List[Any]=False					)       ->      int:

		'''simple docstring'''


		lowerCAmelCase     =    os.environ.get(_A ,  str(_A					)					)
		return strtobool(_A					) == 1  # As its name indicates `strtobool` actually returns an int...






def 							snake_case__				(     _A:   Dict ,  _A:   List[Any]="no"					)       ->      str:

		'''simple docstring'''


		lowerCAmelCase     =    os.environ.get(_A ,  str(_A					)					)
		return value




