





'''simple docstring'''






def      lowercase  (  lowerCAmelCase :						Union[str, Any]):



				"""simple docstring"""


				_A :				Union[str, Any]				=  [701, 301, 132, 57, 23, 10, 4, 1]
				for gap in gaps:
								for i in range(_lowercase   , len(_lowercase)):
												_A :				Union[str, Any]				=  collection[i]
												_A :				Union[str, Any]				=  i
												while j >= gap and collection[j - gap] > insert_value:
																_A :				Dict				=  collection[j - gap]
																j -= gap
												if j != i:
																_A :				Union[str, Any]				=  insert_value
				return collection


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	__UpperCamelCase      :   List[str]								=     input('''Enter numbers separated by a comma:\n''').strip()
	__UpperCamelCase      :   Dict								=     [int(item) for item in user_input.split(''',''')]
	print(shell_sort(unsorted))

"""simple docstring"""




import math
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

UpperCAmelCase       :    List[str]									=			"\n    Examples:\n        ```py\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif\n\n        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n        >>> repo = \"openai/shap-e\"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 15.0\n        >>> prompt = \"a shark\"\n\n        >>> images = pipe(\n        ...     prompt,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], \"shark_3d.gif\")\n        ```\n"

@dataclass
class        __SCREAMING_SNAKE_CASE     (   _lowerCAmelCase							):
				UpperCAmelCase	=      42





class        __SCREAMING_SNAKE_CASE     (   _lowerCAmelCase							):







				def __init__(    self    :Optional[Any]      ,__UpperCAmelCase    :PriorTransformer      ,__UpperCAmelCase    :CLIPTextModelWithProjection      ,__UpperCAmelCase    :CLIPTokenizer      ,__UpperCAmelCase    :HeunDiscreteScheduler      ,__UpperCAmelCase    :ShapERenderer      ,)      ->					List[Any]:




									"""simple docstring"""



									super().__init__()

									self.register_modules(
									    prior=__UpperCAmelCase      ,text_encoder=__UpperCAmelCase      ,tokenizer=__UpperCAmelCase      ,scheduler=__UpperCAmelCase      ,renderer=__UpperCAmelCase      ,)







				def   lowercase_		(    self    :Any      ,__UpperCAmelCase    :Tuple      ,__UpperCAmelCase    :Dict      ,__UpperCAmelCase    :Optional[int]      ,__UpperCAmelCase    :Dict      ,__UpperCAmelCase    :Any      ,__UpperCAmelCase    :Dict  )      ->					Optional[Any]:




									"""simple docstring"""



									if latents is None:
														lowerCamelCase__		:   Optional[Any]  				=					randn_tensor(__UpperCAmelCase      ,generator=__UpperCAmelCase      ,device=__UpperCAmelCase      ,dtype=__UpperCAmelCase  )
									else:
														if latents.shape != shape:
																			raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""  )
														lowerCamelCase__		:   Optional[Any]  				=					latents.to(__UpperCAmelCase  )

									lowerCamelCase__		:   Dict  				=					latents * scheduler.init_noise_sigma
									return latents







				def   lowercase_		(    self    :Dict      ,__UpperCAmelCase    :List[Any]=0  )      ->					str:




									"""simple docstring"""



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

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

									lowerCamelCase__		:   Optional[int]  				=					[self.text_encoder, self.prior]
									for cpu_offloaded_model in models:
														if cpu_offloaded_model is not None:
																			cpu_offload(__UpperCAmelCase      ,__UpperCAmelCase  )







				def   lowercase_		(    self    :int      ,__UpperCAmelCase    :Dict=0  )      ->					Dict:




									"""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__		:   List[Any]  				=					torch.device(F"""cuda:{gpu_id}"""  )

									if self.device.type != "cpu":
														self.to('''cpu'''      ,silence_dtype_warnings=__UpperCAmelCase  )
														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.text_encoder, self.prior, self.renderer]:
														lowerCamelCase__				,						lowerCamelCase__		:   Tuple  				=					cpu_offload_with_hook(__UpperCAmelCase      ,__UpperCAmelCase      ,prev_module_hook=__UpperCAmelCase  )

									if self.safety_checker is not None:
														lowerCamelCase__				,						lowerCamelCase__		:   Any  				=					cpu_offload_with_hook(self.safety_checker      ,__UpperCAmelCase      ,prev_module_hook=__UpperCAmelCase  )

									# We'll offload the last model manually.
									lowerCamelCase__		:   Dict  				=					hook







				@property
				def   lowercase_		(    self    :Dict  )      ->					Optional[Any]:




									"""simple docstring"""



									if self.device != torch.device('''meta'''  ) or not hasattr(self.text_encoder      ,'''_hf_hook'''  ):
														return self.device
									for module in self.text_encoder.modules():
														if (
														    hasattr(__UpperCAmelCase      ,'''_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







				def   lowercase_		(    self    :Tuple      ,__UpperCAmelCase    :Optional[int]      ,__UpperCAmelCase    :Optional[Any]      ,__UpperCAmelCase    :Any      ,__UpperCAmelCase    :Optional[Any]      ,)      ->					str:




									"""simple docstring"""



									len(__UpperCAmelCase  ) if isinstance(__UpperCAmelCase      ,__UpperCAmelCase  ) else 1

									# YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
									lowerCamelCase__		:   Optional[int]  				=					0
									# get prompt text embeddings
									lowerCamelCase__		:   Union[str, Any]  				=					self.tokenizer(
									    __UpperCAmelCase      ,padding='''max_length'''      ,max_length=self.tokenizer.model_max_length      ,truncation=__UpperCAmelCase      ,return_tensors='''pt'''      ,)
									lowerCamelCase__		:   Union[str, Any]  				=					text_inputs.input_ids
									lowerCamelCase__		:   Any  				=					self.tokenizer(__UpperCAmelCase      ,padding='''longest'''      ,return_tensors='''pt'''  ).input_ids

									if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(__UpperCAmelCase      ,__UpperCAmelCase  ):
														lowerCamelCase__		:   List[str]  				=					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__		:   Optional[int]  				=					self.text_encoder(text_input_ids.to(__UpperCAmelCase  )  )
									lowerCamelCase__		:   List[Any]  				=					text_encoder_output.text_embeds

									lowerCamelCase__		:   Optional[Any]  				=					prompt_embeds.repeat_interleave(__UpperCAmelCase      ,dim=0  )
									# in Shap-E it normalize the prompt_embeds and then later rescale it
									lowerCamelCase__		:   Union[str, Any]  				=					prompt_embeds / torch.linalg.norm(__UpperCAmelCase      ,dim=-1      ,keepdim=__UpperCAmelCase  )

									if do_classifier_free_guidance:
														lowerCamelCase__		:   Any  				=					torch.zeros_like(__UpperCAmelCase  )

														# 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__		:   Optional[int]  				=					torch.cat([negative_prompt_embeds, prompt_embeds]  )

									# Rescale the features to have unit variance
									lowerCamelCase__		:   Optional[int]  				=					math.sqrt(prompt_embeds.shape[1]  ) * prompt_embeds

									return prompt_embeds







				@torch.no_grad()
				@replace_example_docstring(__UpperCAmelCase  )
				def __call__(    self    :Any      ,__UpperCAmelCase    :str      ,__UpperCAmelCase    :int = 1      ,__UpperCAmelCase    :int = 25      ,__UpperCAmelCase    :Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,__UpperCAmelCase    :Optional[torch.FloatTensor] = None      ,__UpperCAmelCase    :float = 4.0      ,__UpperCAmelCase    :int = 64      ,__UpperCAmelCase    :Optional[str] = "pil"      ,__UpperCAmelCase    :bool = True      ,)      ->					Tuple:




									"""simple docstring"""



									if isinstance(__UpperCAmelCase      ,__UpperCAmelCase  ):
														lowerCamelCase__		:   Union[str, Any]  				=					1
									elif isinstance(__UpperCAmelCase      ,__UpperCAmelCase  ):
														lowerCamelCase__		:   Any  				=					len(__UpperCAmelCase  )
									else:
														raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(__UpperCAmelCase  )}"""  )

									lowerCamelCase__		:   Optional[Any]  				=					self._execution_device

									lowerCamelCase__		:   List[Any]  				=					batch_size * num_images_per_prompt

									lowerCamelCase__		:   Optional[int]  				=					guidance_scale > 1.0
									lowerCamelCase__		:   Tuple  				=					self._encode_prompt(__UpperCAmelCase      ,__UpperCAmelCase      ,__UpperCAmelCase      ,__UpperCAmelCase  )

									# prior

									self.scheduler.set_timesteps(__UpperCAmelCase      ,device=__UpperCAmelCase  )
									lowerCamelCase__		:   str  				=					self.scheduler.timesteps

									lowerCamelCase__		:   Dict  				=					self.prior.config.num_embeddings
									lowerCamelCase__		:   Optional[Any]  				=					self.prior.config.embedding_dim

									lowerCamelCase__		:   int  				=					self.prepare_latents(
									    (batch_size, num_embeddings * embedding_dim)      ,prompt_embeds.dtype      ,__UpperCAmelCase      ,__UpperCAmelCase      ,__UpperCAmelCase      ,self.scheduler      ,)

									# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
									lowerCamelCase__		:   Tuple  				=					latents.reshape(latents.shape[0]      ,__UpperCAmelCase      ,__UpperCAmelCase  )

									for i, t in enumerate(self.progress_bar(__UpperCAmelCase  )  ):
														# expand the latents if we are doing classifier free guidance
														lowerCamelCase__		:   Optional[Any]  				=					torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents
														lowerCamelCase__		:   List[Any]  				=					self.scheduler.scale_model_input(__UpperCAmelCase      ,__UpperCAmelCase  )

														lowerCamelCase__		:   Dict  				=					self.prior(
														    __UpperCAmelCase      ,timestep=__UpperCAmelCase      ,proj_embedding=__UpperCAmelCase      ,).predicted_image_embedding

														# remove the variance
														lowerCamelCase__				,						lowerCamelCase__		:   Optional[Any]  				=					noise_pred.split(
														    scaled_model_input.shape[2]      ,dim=2  )  # batch_size, num_embeddings, embedding_dim

														if do_classifier_free_guidance is not None:
																			lowerCamelCase__				,						lowerCamelCase__		:   Dict  				=					noise_pred.chunk(2  )
																			lowerCamelCase__		:   List[str]  				=					noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

														lowerCamelCase__		:   Union[str, Any]  				=					self.scheduler.step(
														    __UpperCAmelCase      ,timestep=__UpperCAmelCase      ,sample=__UpperCAmelCase      ,).prev_sample

									if output_type == "latent":
														return ShapEPipelineOutput(images=__UpperCAmelCase  )

									lowerCamelCase__		:   List[Any]  				=					[]
									for i, latent in enumerate(__UpperCAmelCase  ):
														lowerCamelCase__		:   Tuple  				=					self.renderer.decode(
														    latent[None, :]      ,__UpperCAmelCase      ,size=__UpperCAmelCase      ,ray_batch_size=40_96      ,n_coarse_samples=64      ,n_fine_samples=1_28      ,)
														images.append(__UpperCAmelCase  )

									lowerCamelCase__		:   Union[str, Any]  				=					torch.stack(__UpperCAmelCase  )

									if output_type not in ["np", "pil"]:
														raise ValueError(F"""Only the output types `pil` and `np` are supported not output_type={output_type}"""  )

									lowerCamelCase__		:   Optional[int]  				=					images.cpu().numpy()

									if output_type == "pil":
														lowerCamelCase__		:   Optional[Any]  				=					[self.numpy_to_pil(__UpperCAmelCase  ) for image in images]

									# Offload last model to CPU
									if hasattr(self      ,'''final_offload_hook'''  ) and self.final_offload_hook is not None:
														self.final_offload_hook.offload()

									if not return_dict:
														return (images,)

									return ShapEPipelineOutput(images=__UpperCAmelCase  )






