






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,
)


lowerCamelCase     :							Optional[int]    =logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCamelCase     :							List[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 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase=8      )			->     Any:
					UpperCamelCase__      :       Optional[int]	    =  height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					UpperCamelCase__      :       str	    =  width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase=512 ,   __lowerCAmelCase=512      )			->     Optional[Any]:
					UpperCamelCase__      :       Tuple	    =  pil_image.resize((w, h) ,   resample=Image.BICUBIC ,   reducing_gap=1      )
					UpperCamelCase__      :       List[Any]	    =  np.array(pil_image.convert("RGB"      )      )
					UpperCamelCase__      :       Dict	    =  arr.astype(np.floataa      ) / 1_2_7.5 - 1
					UpperCamelCase__      :       Any	    =  np.transpose(__lowerCAmelCase ,   [2, 0, 1]      )
					UpperCamelCase__      :       Union[str, Any]	    =  torch.from_numpy(__lowerCAmelCase      ).unsqueeze(0      )
					return image





class       __a  (   lowerCamelCase_	):







	def __init__(		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       UNetaDConditionModel							,       SCREAMING_SNAKE_CASE :       DDPMScheduler							,       SCREAMING_SNAKE_CASE :       VQModel							,       ):


						'''simple docstring'''






						super().__init__()

						self.register_modules(
						    unet=__snake_case							,       scheduler=__snake_case							,       movq=__snake_case							,       )
						UpperCamelCase__      :       Optional[Any]	    =  2 ** (len(self.movq.config.block_out_channels						) - 1)







	def  __lowercase (		self :       Optional[int]							,       SCREAMING_SNAKE_CASE :       Dict							,       SCREAMING_SNAKE_CASE :       Tuple							,       SCREAMING_SNAKE_CASE :       Union[str, Any]						):


						'''simple docstring'''






						UpperCamelCase__      :       str	    =  min(int(num_inference_steps * strength						)							,       __snake_case						)

						UpperCamelCase__      :       str	    =  max(num_inference_steps - init_timestep							,       0						)
						UpperCamelCase__      :       Any	    =  self.scheduler.timesteps[t_start:]

						return timesteps, num_inference_steps - t_start







	def  __lowercase (		self :       Dict							,       SCREAMING_SNAKE_CASE :       List[str]							,       SCREAMING_SNAKE_CASE :       Union[str, Any]							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       Union[str, Any]							,       SCREAMING_SNAKE_CASE :       Tuple							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       Tuple=None						):


						'''simple docstring'''






						if not isinstance(__snake_case							,       (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(__snake_case						)}'						)

						UpperCamelCase__      :       Optional[Any]	    =  image.to(device=__snake_case							,       dtype=__snake_case						)

						UpperCamelCase__      :       List[str]	    =  batch_size * num_images_per_prompt

						if image.shape[1] == 4:
											UpperCamelCase__      :       Optional[Any]	    =  image

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

											elif isinstance(__snake_case							,       __snake_case						):
																UpperCamelCase__      :       int	    =  [
																    self.movq.encode(image[i : i + 1]						).latent_dist.sample(generator[i]						) for i in range(__snake_case						)
																]
																UpperCamelCase__      :       int	    =  torch.cat(__snake_case							,       dim=0						)
											else:
																UpperCamelCase__      :       Tuple	    =  self.movq.encode(__snake_case						).latent_dist.sample(__snake_case						)

											UpperCamelCase__      :       List[str]	    =  self.movq.config.scaling_factor * init_latents

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

						UpperCamelCase__      :       Dict	    =  init_latents.shape
						UpperCamelCase__      :       Dict	    =  randn_tensor(__snake_case							,       generator=__snake_case							,       device=__snake_case							,       dtype=__snake_case						)

						# get latents
						UpperCamelCase__      :       List[str]	    =  self.scheduler.add_noise(__snake_case							,       __snake_case							,       __snake_case						)

						UpperCamelCase__      :       Tuple	    =  init_latents

						return latents







	def  __lowercase (		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       List[str]=0						):


						'''simple docstring'''






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

						UpperCamelCase__      :       List[str]	    =  torch.device(F'cuda:{gpu_id}'						)

						UpperCamelCase__      :       Optional[int]	    =  [
						    self.unet,
						    self.movq,
						]
						for cpu_offloaded_model in models:
											if cpu_offloaded_model is not None:
																cpu_offload(__snake_case							,       __snake_case						)







	def  __lowercase (		self :       List[str]							,       SCREAMING_SNAKE_CASE :       Optional[int]=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."						)

						UpperCamelCase__      :       List[Any]	    =  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__      :       List[str]	    =  None
						for cpu_offloaded_model in [self.unet, self.movq]:
											UpperCamelCase__    ,					UpperCamelCase__      :       str	    =  cpu_offload_with_hook(__snake_case							,       __snake_case							,       prev_module_hook=__snake_case						)

						# We'll offload the last model manually.
						UpperCamelCase__      :       Union[str, Any]	    =  hook







	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def  __lowercase (		self :       Optional[Any]						):


						'''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 :       str							,       SCREAMING_SNAKE_CASE :       Union[torch.FloatTensor, List[torch.FloatTensor]]							,       SCREAMING_SNAKE_CASE :       Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]							,       SCREAMING_SNAKE_CASE :       Union[torch.FloatTensor, List[torch.FloatTensor]]							,       SCREAMING_SNAKE_CASE :       int = 5_12							,       SCREAMING_SNAKE_CASE :       int = 5_12							,       SCREAMING_SNAKE_CASE :       int = 1_00							,       SCREAMING_SNAKE_CASE :       float = 4.0							,       SCREAMING_SNAKE_CASE :       float = 0.3							,       SCREAMING_SNAKE_CASE :       int = 1							,       SCREAMING_SNAKE_CASE :       Optional[Union[torch.Generator, List[torch.Generator]]] = None							,       SCREAMING_SNAKE_CASE :       Optional[str] = "pil"							,       SCREAMING_SNAKE_CASE :       bool = True							,       ):


						'''simple docstring'''






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

						UpperCamelCase__      :       int	    =  guidance_scale > 1.0

						if isinstance(__snake_case							,       __snake_case						):
											UpperCamelCase__      :       Tuple	    =  torch.cat(__snake_case							,       dim=0						)
						UpperCamelCase__      :       Union[str, Any]	    =  image_embeds.shape[0]
						if isinstance(__snake_case							,       __snake_case						):
											UpperCamelCase__      :       str	    =  torch.cat(__snake_case							,       dim=0						)

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

						UpperCamelCase__      :       Optional[Any]	    =  torch.cat([negative_image_embeds, image_embeds]							,       dim=0						).to(dtype=self.unet.dtype							,       device=__snake_case						)

						if not isinstance(__snake_case							,       __snake_case						):
											UpperCamelCase__      :       Union[str, Any]	    =  [image]
						if not all(isinstance(__snake_case							,       (PIL.Image.Image, torch.Tensor)						) for i in image						):
											raise ValueError(
											    F'Input is in incorrect format: {[type(__snake_case						) for i in image]}. Currently, we only support  PIL image and pytorch tensor'						)

						UpperCamelCase__      :       List[str]	    =  torch.cat([prepare_image(__snake_case							,       __snake_case							,       __snake_case						) for i in image]							,       dim=0						)
						UpperCamelCase__      :       Optional[Any]	    =  image.to(dtype=image_embeds.dtype							,       device=__snake_case						)

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

											UpperCamelCase__      :       Dict	    =  {"image_embeds": image_embeds}
											UpperCamelCase__      :       Optional[int]	    =  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__      :       Any	    =  noise_pred.split(latents.shape[1]							,       dim=1						)
																UpperCamelCase__    ,					UpperCamelCase__      :       Union[str, Any]	    =  noise_pred.chunk(2						)
																UpperCamelCase__    ,					UpperCamelCase__      :       List[Any]	    =  variance_pred.chunk(2						)
																UpperCamelCase__      :       str	    =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																UpperCamelCase__      :       Dict	    =  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__      :       Any	    =  noise_pred.split(latents.shape[1]							,       dim=1						)

											# compute the previous noisy sample x_t -> x_t-1
											UpperCamelCase__      :       str	    =  self.scheduler.step(
											    __snake_case							,       __snake_case							,       __snake_case							,       generator=__snake_case							,       )[0]

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

						if output_type == "pil":
											UpperCamelCase__      :       Optional[int]	    =  self.numpy_to_pil(__snake_case						)

						if not return_dict:
											return (image,)

						return ImagePipelineOutput(images=__snake_case						)







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     list:
     UpperCamelCase__      :       Tuple	    =  False
     while is_sorted is False:  # Until all the indices are traversed keep looping
          UpperCamelCase__      :       Tuple	    =  True
          for i in range(0 ,   len(__lowerCAmelCase      ) - 1 ,   2      ):  # iterating over all even indices
               if input_list[i] > input_list[i + 1]:
                    UpperCamelCase__    ,					UpperCamelCase__      :       Dict	    =  input_list[i + 1], input_list[i]
                    # swapping if elements not in order
                    UpperCamelCase__      :       List[str]	    =  False

          for i in range(1 ,   len(__lowerCAmelCase      ) - 1 ,   2      ):  # iterating over all odd indices
               if input_list[i] > input_list[i + 1]:
                    UpperCamelCase__    ,					UpperCamelCase__      :       Optional[int]	    =  input_list[i + 1], input_list[i]
                    # swapping if elements not in order
                    UpperCamelCase__      :       List[Any]	    =  False
     return input_list


if __name__ == "__main__":
 print('''Enter list to be sorted''')
 lowerCamelCase     :							Any    =[int(x) for x in input().split()]
 # inputing elements of the list in one line
 lowerCamelCase     :							Optional[int]    =odd_even_sort(input_list)
 print('''The sorted list is''')
 print(sorted_list)