






'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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

UpperCAmelCase__						:Any          =			"""
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to(\"cuda\")

        >>> prompt = \"A red cartoon frog, 4k\"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16
        ... )
        >>> pipe.to(\"cuda\")

        >>> init_image = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/frog.png\"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save(\"red_frog.png\")
        ```
"""
def     __lowercase  (_lowercase,						_lowercase,						_lowercase=8     )   ->     Optional[Any]:


					"""simple docstring"""
					__lowerCamelCase						:       Any				   =				height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					__lowerCamelCase						:       int				   =				width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor
def     __lowercase  (_lowercase,						_lowercase=512,						_lowercase=512     )   ->     List[str]:


					"""simple docstring"""
					__lowerCamelCase						:       Dict				   =				pil_image.resize((w, h),						resample=Image.BICUBIC,						reducing_gap=1     )
					__lowerCamelCase						:       List[Any]				   =				np.array(pil_image.convert("""RGB"""     )     )
					__lowerCamelCase						:       int				   =				arr.astype(np.floataa     ) / 127.5 - 1
					__lowerCamelCase						:       Any				   =				np.transpose(_lowercase,						[2, 0, 1]     )
					__lowerCamelCase						:       Any				   =				torch.from_numpy(_lowercase     ).unsqueeze(0     )
					return image







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):




							def __init__(					self      : Tuple	,    A__      : UNetaDConditionModel	,    A__      : DDPMScheduler	,    A__      : VQModel	,    ):


												"""simple docstring"""







												super().__init__()

												self.register_modules(
												    unet=A__	,    scheduler=A__	,    movq=A__	,    )
												__lowerCamelCase						:       int				   =				2 ** (len(self.movq.config.block_out_channels					) - 1)




							def 					a_					(					self      : List[str]	,    A__      : List[Any]	,    A__      : Union[str, Any]	,    A__      : int					):


												"""simple docstring"""







												__lowerCamelCase						:       List[Any]				   =				min(int(num_inference_steps * strength					)	,    A__					)

												__lowerCamelCase						:       int				   =				max(num_inference_steps - init_timestep	,    0					)
												__lowerCamelCase						:       List[Any]				   =				self.scheduler.timesteps[t_start:]

												return timesteps, num_inference_steps - t_start




							def 					a_					(					self      : List[str]	,    A__      : str	,    A__      : Dict	,    A__      : str	,    A__      : Optional[Any]	,    A__      : Dict	,    A__      : List[Any]	,    A__      : List[Any]=None					):


												"""simple docstring"""







												if not isinstance(A__	,    (torch.Tensor, PIL.Image.Image, list)					):
																	raise ValueError(
																	    f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(A__					)}"					)

												__lowerCamelCase						:       Optional[Any]				   =				image.to(device=A__	,    dtype=A__					)

												__lowerCamelCase						:       List[str]				   =				batch_size * num_images_per_prompt

												if image.shape[1] == 4:
																	__lowerCamelCase						:       int				   =				image

												else:
																	if isinstance(A__	,    A__					) and len(A__					) != batch_size:
																						raise ValueError(
																						    f"You have passed a list of generators of length {len(A__					)}, but requested an effective batch"
																						    f" size of {batch_size}. Make sure the batch size matches the length of the generators."					)

																	elif isinstance(A__	,    A__					):
																						__lowerCamelCase						:       Optional[int]				   =				[
																						    self.movq.encode(image[i : i + 1]					).latent_dist.sample(generator[i]					) for i in range(A__					)
																						]
																						__lowerCamelCase						:       str				   =				torch.cat(A__	,    dim=0					)
																	else:
																						__lowerCamelCase						:       Optional[Any]				   =				self.movq.encode(A__					).latent_dist.sample(A__					)

																	__lowerCamelCase						:       Optional[int]				   =				self.movq.config.scaling_factor * init_latents

												__lowerCamelCase						:       List[Any]				   =				torch.cat([init_latents]	,    dim=0					)

												__lowerCamelCase						:       List[str]				   =				init_latents.shape
												__lowerCamelCase						:       Dict				   =				randn_tensor(A__	,    generator=A__	,    device=A__	,    dtype=A__					)

												# get latents
												__lowerCamelCase						:       Any				   =				self.scheduler.add_noise(A__	,    A__	,    A__					)

												__lowerCamelCase						:       Optional[int]				   =				init_latents

												return latents




							def 					a_					(					self      : List[Any]	,    A__      : Tuple=0					):


												"""simple docstring"""







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

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

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




							def 					a_					(					self      : Tuple	,    A__      : Any=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						:       Tuple				   =				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)

												__lowerCamelCase						:       List[Any]				   =				None
												for cpu_offloaded_model in [self.unet, self.movq]:
																	__lowerCamelCase						:       int				   =				cpu_offload_with_hook(A__	,    A__	,    prev_module_hook=A__					)

												# We'll offload the last model manually.
												__lowerCamelCase						:       Union[str, Any]				   =				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      : Tuple	,    A__      : Union[torch.FloatTensor, List[torch.FloatTensor]]	,    A__      : Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]	,    A__      : Union[torch.FloatTensor, List[torch.FloatTensor]]	,    A__      : int = 512	,    A__      : int = 512	,    A__      : int = 100	,    A__      : float = 4.0	,    A__      : float = 0.3	,    A__      : int = 1	,    A__      : Optional[Union[torch.Generator, List[torch.Generator]]] = None	,    A__      : Optional[str] = "pil"	,    A__      : bool = True	,    ):


												"""simple docstring"""







												__lowerCamelCase						:       Union[str, Any]				   =				self._execution_device

												__lowerCamelCase						:       List[Any]				   =				guidance_scale > 1.0

												if isinstance(A__	,    A__					):
																	__lowerCamelCase						:       str				   =				torch.cat(A__	,    dim=0					)
												__lowerCamelCase						:       Union[str, Any]				   =				image_embeds.shape[0]
												if isinstance(A__	,    A__					):
																	__lowerCamelCase						:       Optional[int]				   =				torch.cat(A__	,    dim=0					)

												if do_classifier_free_guidance:
																	__lowerCamelCase						:       str				   =				image_embeds.repeat_interleave(A__	,    dim=0					)
																	__lowerCamelCase						:       Optional[int]				   =				negative_image_embeds.repeat_interleave(A__	,    dim=0					)

												__lowerCamelCase						:       Union[str, Any]				   =				torch.cat([negative_image_embeds, image_embeds]	,    dim=0					).to(dtype=self.unet.dtype	,    device=A__					)

												if not isinstance(A__	,    A__					):
																	__lowerCamelCase						:       str				   =				[image]
												if not all(isinstance(A__	,    (PIL.Image.Image, torch.Tensor)					) for i in image					):
																	raise ValueError(
																	    f"Input is in incorrect format: {[type(A__					) for i in image]}. Currently, we only support  PIL image and pytorch tensor"					)

												__lowerCamelCase						:       Tuple				   =				torch.cat([prepare_image(A__	,    A__	,    A__					) for i in image]	,    dim=0					)
												__lowerCamelCase						:       Tuple				   =				image.to(dtype=image_embeds.dtype	,    device=A__					)

												__lowerCamelCase						:       Union[str, Any]				   =				self.movq.encode(A__					)["""latents"""]
												__lowerCamelCase						:       Dict				   =				latents.repeat_interleave(A__	,    dim=0					)
												self.scheduler.set_timesteps(A__	,    device=A__					)
												__lowerCamelCase						:       Any				   =				self.get_timesteps(A__	,    A__	,    A__					)
												__lowerCamelCase						:       List[str]				   =				timesteps[:1].repeat(batch_size * num_images_per_prompt					)
												__lowerCamelCase						:       Any				   =				downscale_height_and_width(A__	,    A__	,    self.movq_scale_factor					)
												__lowerCamelCase						:       Dict				   =				self.prepare_latents(
												    A__	,    A__	,    A__	,    A__	,    image_embeds.dtype	,    A__	,    A__					)
												for i, t in enumerate(self.progress_bar(A__					)					):
																	# expand the latents if we are doing classifier free guidance
																	__lowerCamelCase						:       List[str]				   =				torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

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

																	if do_classifier_free_guidance:
																						__lowerCamelCase						:       List[str]				   =				noise_pred.split(latents.shape[1]	,    dim=1					)
																						__lowerCamelCase						:       Any				   =				noise_pred.chunk(2					)
																						__lowerCamelCase						:       Optional[Any]				   =				variance_pred.chunk(2					)
																						__lowerCamelCase						:       List[str]				   =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																						__lowerCamelCase						:       List[str]				   =				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						:       Optional[int]				   =				noise_pred.split(latents.shape[1]	,    dim=1					)

																	# compute the previous noisy sample x_t -> x_t-1
																	__lowerCamelCase						:       Dict				   =				self.scheduler.step(
																	    A__	,    A__	,    A__	,    generator=A__	,    )[0]

												# post-processing
												__lowerCamelCase						:       List[str]				   =				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"]:
																	__lowerCamelCase						:       int				   =				image * 0.5 + 0.5
																	__lowerCamelCase						:       Optional[Any]				   =				image.clamp(0	,    1					)
																	__lowerCamelCase						:       Any				   =				image.cpu().permute(0	,    2	,    3	,    1					).float().numpy()

												if output_type == "pil":
																	__lowerCamelCase						:       List[str]				   =				self.numpy_to_pil(A__					)

												if not return_dict:
																	return (image,)

												return ImagePipelineOutput(images=A__					)











'''simple docstring'''

def     __lowercase  (_lowercase     )   ->     list:


     """simple docstring"""

     # bit count represents no. of bits in the gray code
     if bit_count < 0:
          raise ValueError("""The given input must be positive"""     )

     # get the generated string sequence
     __lowerCamelCase						:       Optional[Any]				   =				gray_code_sequence_string(_lowercase     )
     #
     # convert them to integers
     for i in range(len(_lowercase     )     ):
          __lowerCamelCase						:       List[str]				   =				int(sequence[i],						2     )

     return sequence
def     __lowercase  (_lowercase     )   ->     list:


     """simple docstring"""

     # The approach is a recursive one
     # Base case achieved when either n = 0 or n=1
     if bit_count == 0:
          return ["0"]

     if bit_count == 1:
          return ["0", "1"]

     __lowerCamelCase						:       str				   =				1 << bit_count  # defines the length of the sequence
     # 1<< n is equivalent to 2^n

     # recursive answer will generate answer for n-1 bits
     __lowerCamelCase						:       Union[str, Any]				   =				gray_code_sequence_string(bit_count - 1     )

     __lowerCamelCase						:       Optional[int]				   =				[]

     # append 0 to first half of the smaller sequence generated
     for i in range(seq_len // 2     ):
          __lowerCamelCase						:       Union[str, Any]				   =				"""0""" + smaller_sequence[i]
          sequence.append(_lowercase     )

     # append 1 to second half ... start from the end of the list
     for i in reversed(range(seq_len // 2     )     ):
          __lowerCamelCase						:       Optional[Any]				   =				"""1""" + smaller_sequence[i]
          sequence.append(_lowercase     )

     return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()



