
"""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,
)


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

_a														=		'''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

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



def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case=8				)							->		List[str]:


					"""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    _UpperCAmelCase(   lowerCamelCase_     ):



		def __init__( self    ,		__a    ,		__a    ,		__a    ,		)	->	Any:






							'''simple docstring'''
							super().__init__()

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



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	Optional[Any]:






							'''simple docstring'''
							if latents is None:
												_UpperCamelCase										=  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}''')
												_UpperCamelCase										=  latents.to(__snake_case)

							_UpperCamelCase										=  latents * scheduler.init_noise_sigma
							return latents



		def 						UpperCAmelCase							( self    ,		__a=0)	->	int:






							'''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(__snake_case    ,		__snake_case)



		def 						UpperCAmelCase							( self    ,		__a=0)	->	int:






							'''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=__snake_case)
												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			,  _UpperCamelCase										=  cpu_offload_with_hook(__snake_case    ,		__snake_case    ,		prev_module_hook=__snake_case)

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



		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							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    ,		__a    ,		__a    ,		__a = 5_12    ,		__a = 5_12    ,		__a = 1_00    ,		__a = 4.0    ,		__a = 1    ,		__a = None    ,		__a = None    ,		__a = "pil"    ,		__a = True    ,		)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  self._execution_device

							_UpperCamelCase										=  guidance_scale > 1.0

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

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

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

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

							_UpperCamelCase										=  self.unet.config.in_channels

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

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

												_UpperCamelCase										=  {'''image_embeds''': image_embeds}
												_UpperCamelCase										=  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:
																	_UpperCamelCase			,  _UpperCamelCase										=  noise_pred.split(latents.shape[1]    ,		dim=1)
																	_UpperCamelCase			,  _UpperCamelCase										=  noise_pred.chunk(2)
																	_UpperCamelCase			,  _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			,  _UpperCamelCase										=  noise_pred.split(latents.shape[1]    ,		dim=1)

												# compute the previous noisy sample x_t -> x_t-1
												_UpperCamelCase										=  self.scheduler.step(
												    __snake_case    ,		__snake_case    ,		__snake_case    ,		generator=__snake_case    ,		)[0]
							# post-processing
							_UpperCamelCase										=  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"]:
												_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(__snake_case)

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=__snake_case)








"""simple docstring"""





def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		str:


					"""simple docstring"""





					if number < 0 or shift_amount < 0:
										raise ValueError('''both inputs must be positive integers'''     )

					_UpperCamelCase										=  str(bin(__snake_case     )     )
					binary_number += "0" * shift_amount
					return binary_number



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		str:


					"""simple docstring"""





					if number < 0 or shift_amount < 0:
										raise ValueError('''both inputs must be positive integers'''     )

					_UpperCamelCase										=  str(bin(__snake_case     )     )[2:]
					if shift_amount >= len(__snake_case     ):
										return "0b0"
					_UpperCamelCase										=  binary_number[: len(__snake_case     ) - shift_amount]
					return "0b" + shifted_binary_number



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		str:


					"""simple docstring"""





					if number >= 0:  # Get binary representation of positive number
										_UpperCamelCase										=  '''0''' + str(bin(__snake_case     )     ).strip('''-'''     )[2:]
					else:  # Get binary (2's complement) representation of negative number
										_UpperCamelCase										=  len(bin(__snake_case     )[3:]     )  # Find 2's complement of number
										_UpperCamelCase										=  bin(abs(__snake_case     ) - (1 << binary_number_length)     )[3:]
										_UpperCamelCase										=  (
										    '''1''' + '''0''' * (binary_number_length - len(__snake_case     )) + binary_number
										)

					if shift_amount >= len(__snake_case     ):
										return "0b" + binary_number[0] * len(__snake_case     )
					return (
					    "0b"
					    + binary_number[0] * shift_amount
					    + binary_number[: len(__snake_case     ) - shift_amount]
					)


if __name__ == "__main__":
				import doctest

				doctest.testmod()






