

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,
)


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

_UpperCAmelCase						:  List[str]									=			"""
    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    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__=8 ):







							'''simple docstring'''






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







class 					lowercase							(			lowercase_     ):




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

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




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case			):
											if latents is None:
																		snake_case_     							=       randn_tensor(snake_case ,  generator=snake_case ,  device=snake_case ,  dtype=snake_case			)
											else:
																		if latents.shape != shape:
																									raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''			)
																		snake_case_     							=       latents.to(snake_case			)

											snake_case_     							=       latents * scheduler.init_noise_sigma
											return latents




				def        a				(   self ,  snake_case=0			):
											if is_accelerate_available():
																		from accelerate import cpu_offload
											else:
																		raise ImportError('Please install accelerate via `pip install accelerate`'			)

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

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




				def        a				(   self ,  snake_case=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.'			)

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

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

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

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




				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def        a				(   self			):
											if not hasattr(self.unet ,  '_hf_hook'			):
																		return self.device
											for module in self.unet.modules():
																		if (
																		    hasattr(snake_case ,  '_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(snake_case			)
				def __call__(   self ,  snake_case ,  snake_case ,  snake_case ,  snake_case = 512 ,  snake_case = 512 ,  snake_case = 100 ,  snake_case = 4.0 ,  snake_case = 1 ,  snake_case = None ,  snake_case = None ,  snake_case = "pil" ,  snake_case = True ,  ):
											snake_case_     							=       self._execution_device

											snake_case_     							=       guidance_scale > 1.0

											if isinstance(snake_case ,  snake_case			):
																		snake_case_     							=       torch.cat(snake_case ,  dim=0			)
											if isinstance(snake_case ,  snake_case			):
																		snake_case_     							=       torch.cat(snake_case ,  dim=0			)
											if isinstance(snake_case ,  snake_case			):
																		snake_case_     							=       torch.cat(snake_case ,  dim=0			)

											snake_case_     							=       image_embeds.shape[0] * num_images_per_prompt

											if do_classifier_free_guidance:
																		snake_case_     							=       image_embeds.repeat_interleave(snake_case ,  dim=0			)
																		snake_case_     							=       negative_image_embeds.repeat_interleave(snake_case ,  dim=0			)
																		snake_case_     							=       hint.repeat_interleave(snake_case ,  dim=0			)

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

											snake_case_     							=       self.movq.config.latent_channels

											snake_case_							,       snake_case_     							=       downscale_height_and_width(snake_case ,  snake_case ,  self.movq_scale_factor			)

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

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

																		snake_case_     							=       {'image_embeds': image_embeds, 'hint': hint}
																		snake_case_     							=       self.unet(
																		    sample=snake_case ,  timestep=snake_case ,  encoder_hidden_states=snake_case ,  added_cond_kwargs=snake_case ,  return_dict=snake_case ,  )[0]

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

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

											if output_type == "pil":
																		snake_case_     							=       self.numpy_to_pil(snake_case			)

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=snake_case			)







_UpperCAmelCase						:  Any									=			6_5521





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       1
							snake_case_     							=       0
							for plain_chr in plain_text:
														snake_case_     							=       (a + ord(UpperCamelCase__ )) % MOD_ADLER
														snake_case_     							=       (b + a) % MOD_ADLER
							return (b << 16) | a




