


"""simple docstring"""


from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


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

A	:  Union[str, Any]            =     "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(\"kandinsky-community/Kandinsky-2-1-prior\")\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\")\n        >>> pipe.to(\"cuda\")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save(\"cat.png\")\n        ```\n"


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase=8 ):

			'''simple docstring'''




			__lowerCAmelCase							       =     h // scale_factor**2
			if h % scale_factor**2 != 0:
						new_h += 1
			__lowerCAmelCase							       =     w // scale_factor**2
			if w % scale_factor**2 != 0:
						new_w += 1
			return new_h * scale_factor, new_w * scale_factor


class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







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

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

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a      ):
										if latents is None:
													__lowerCAmelCase							       =     randn_tensor(__a       ,  generator=__a       ,  device=__a       ,  dtype=__a      )
										else:
													if latents.shape != shape:
																raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"      )
													__lowerCAmelCase							       =     latents.to(__a      )

										__lowerCAmelCase							       =     latents * scheduler.init_noise_sigma
										return latents

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a=None       ,  ):
										__lowerCAmelCase							       =     len(__a      ) if isinstance(__a       ,  __a      ) else 1
										# get prompt text embeddings
										__lowerCAmelCase							       =     self.tokenizer(
										    __a       ,  padding="max_length"       ,  truncation=__a       ,  max_length=77       ,  return_attention_mask=__a       ,  add_special_tokens=__a       ,  return_tensors="pt"       ,  )

										__lowerCAmelCase							       =     text_inputs.input_ids
										__lowerCAmelCase							       =     self.tokenizer(__a       ,  padding="longest"       ,  return_tensors="pt"      ).input_ids

										if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(__a       ,  __a      ):
													__lowerCAmelCase							       =     self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]      )
													logger.warning(
													    "The following part of your input was truncated because CLIP can only handle sequences up to"
													    f" {self.tokenizer.model_max_length} tokens: {removed_text}"      )

										__lowerCAmelCase							       =     text_input_ids.to(__a      )
										__lowerCAmelCase							       =     text_inputs.attention_mask.to(__a      )

										__lowerCAmelCase       ,      __lowerCAmelCase							       =     self.text_encoder(
										    input_ids=__a       ,  attention_mask=__a      )

										__lowerCAmelCase							       =     prompt_embeds.repeat_interleave(__a       ,  dim=0      )
										__lowerCAmelCase							       =     text_encoder_hidden_states.repeat_interleave(__a       ,  dim=0      )
										__lowerCAmelCase							       =     text_mask.repeat_interleave(__a       ,  dim=0      )

										if do_classifier_free_guidance:
													__lowerCAmelCase							       =     42
													if negative_prompt is None:
																__lowerCAmelCase							       =     [""] * batch_size
													elif type(__a      ) is not type(__a      ):
																raise TypeError(
																    f"`negative_prompt` should be the same type to `prompt`, but got {type(__a      )} !="
																    f" {type(__a      )}."      )
													elif isinstance(__a       ,  __a      ):
																__lowerCAmelCase							       =     [negative_prompt]
													elif batch_size != len(__a      ):
																raise ValueError(
																    f"`negative_prompt`: {negative_prompt} has batch size {len(__a      )}, but `prompt`:"
																    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
																    " the batch size of `prompt`."      )
													else:
																__lowerCAmelCase							       =     negative_prompt

													__lowerCAmelCase							       =     self.tokenizer(
													    __a       ,  padding="max_length"       ,  max_length=77       ,  truncation=__a       ,  return_attention_mask=__a       ,  add_special_tokens=__a       ,  return_tensors="pt"       ,  )
													__lowerCAmelCase							       =     uncond_input.input_ids.to(__a      )
													__lowerCAmelCase							       =     uncond_input.attention_mask.to(__a      )

													__lowerCAmelCase       ,      __lowerCAmelCase							       =     self.text_encoder(
													    input_ids=__a       ,  attention_mask=__a      )

													# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

													__lowerCAmelCase							       =     negative_prompt_embeds.shape[1]
													__lowerCAmelCase							       =     negative_prompt_embeds.repeat(1       ,  __a      )
													__lowerCAmelCase							       =     negative_prompt_embeds.view(batch_size * num_images_per_prompt       ,  __a      )

													__lowerCAmelCase							       =     uncond_text_encoder_hidden_states.shape[1]
													__lowerCAmelCase							       =     uncond_text_encoder_hidden_states.repeat(1       ,  __a       ,  1      )
													__lowerCAmelCase							       =     uncond_text_encoder_hidden_states.view(
													    batch_size * num_images_per_prompt       ,  __a       ,  -1      )
													__lowerCAmelCase							       =     uncond_text_mask.repeat_interleave(__a       ,  dim=0      )

													# done duplicates

													# For classifier free guidance, we need to do two forward passes.
													# Here we concatenate the unconditional and text embeddings into a single batch
													# to avoid doing two forward passes
													__lowerCAmelCase							       =     torch.cat([negative_prompt_embeds, prompt_embeds]      )
													__lowerCAmelCase							       =     torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]      )

													__lowerCAmelCase							       =     torch.cat([uncond_text_mask, text_mask]      )

										return prompt_embeds, text_encoder_hidden_states, text_mask

							def      snake_case							(	self       ,  __a=0      ):
										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.text_encoder,
										    self.movq,
										]
										for cpu_offloaded_model in models:
													if cpu_offloaded_model is not None:
																cpu_offload(__a       ,  __a      )

							def      snake_case							(	self       ,  __a=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."      )

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

										if self.safety_checker is not None:
													__lowerCAmelCase       ,      __lowerCAmelCase							       =     cpu_offload_with_hook(self.safety_checker       ,  __a       ,  prev_module_hook=__a      )

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

							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def      snake_case							(	self      ):
										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       ,  __a       ,  __a       ,  __a       ,  __a = None       ,  __a = 5_12       ,  __a = 5_12       ,  __a = 1_00       ,  __a = 4.0       ,  __a = 1       ,  __a = None       ,  __a = None       ,  __a = "pil"       ,  __a = True       ,  ):

										if isinstance(__a       ,  __a      ):
													__lowerCAmelCase							       =     1
										elif isinstance(__a       ,  __a      ):
													__lowerCAmelCase							       =     len(__a      )
										else:
													raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(__a      )}"      )

										__lowerCAmelCase							       =     self._execution_device

										__lowerCAmelCase							       =     batch_size * num_images_per_prompt
										__lowerCAmelCase							       =     guidance_scale > 1.0

										__lowerCAmelCase       ,      __lowerCAmelCase       ,      __lowerCAmelCase							       =     self._encode_prompt(
										    __a       ,  __a       ,  __a       ,  __a       ,  __a      )

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

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

										__lowerCAmelCase							       =     torch.cat([negative_image_embeds, image_embeds]       ,  dim=0      ).to(
										    dtype=prompt_embeds.dtype       ,  device=__a      )

										self.scheduler.set_timesteps(__a       ,  device=__a      )
										__lowerCAmelCase							       =     self.scheduler.timesteps

										__lowerCAmelCase							       =     self.unet.config.in_channels

										__lowerCAmelCase       ,      __lowerCAmelCase							       =     get_new_h_w(__a       ,  __a       ,  self.movq_scale_factor      )

										# create initial latent
										__lowerCAmelCase							       =     self.prepare_latents(
										    (batch_size, num_channels_latents, height, width)       ,  text_encoder_hidden_states.dtype       ,  __a       ,  __a       ,  __a       ,  self.scheduler       ,  )

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

													__lowerCAmelCase							       =     {"text_embeds": prompt_embeds, "image_embeds": image_embeds}
													__lowerCAmelCase							       =     self.unet(
													    sample=__a       ,  timestep=__a       ,  encoder_hidden_states=__a       ,  added_cond_kwargs=__a       ,  return_dict=__a       ,  )[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(
													    __a       ,  __a       ,  __a       ,  generator=__a       ,  ).prev_sample
										# post-processing
										__lowerCAmelCase							       =     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							       =     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(__a      )

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=__a      )








"""simple docstring"""


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




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

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


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




