





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


snake_case__      :							List[Any]									=  logging.get_logger(__name__)  # pylint: disable=invalid-name

snake_case__      :							Union[str, Any]									=  '\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 A_      (    _lowerCamelCase     ):
			lowerCAmelCase__       =				42





class A_      (    _lowerCamelCase     ):




			def __init__(self			:Tuple					,     _UpperCamelCase			:PriorTransformer					,     _UpperCamelCase			:CLIPTextModelWithProjection					,     _UpperCamelCase			:CLIPTokenizer					,     _UpperCamelCase			:HeunDiscreteScheduler					,     _UpperCamelCase			:ShapERenderer					,     )->      Any:
									super().__init__()

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




			def _lowerCAmelCase   (self			:Tuple					,     _UpperCamelCase			:int					,     _UpperCamelCase			:Tuple					,     _UpperCamelCase			:List[Any]					,     _UpperCamelCase			:Optional[int]					,     _UpperCamelCase			:List[Any]					,     _UpperCamelCase			:int				)->      Any:
									if latents is None:
															__A      =       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}"""				)
															__A      =       latents.to(_UpperCamelCase				)

									__A      =       latents * scheduler.init_noise_sigma
									return latents




			def _lowerCAmelCase   (self			:Tuple					,     _UpperCamelCase			:List[str]=0				)->      Any:
									if is_accelerate_available():
															from accelerate import cpu_offload
									else:
															raise ImportError('''Please install accelerate via `pip install accelerate`'''				)

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

									__A      =       [self.text_encoder, self.prior]
									for cpu_offloaded_model in models:
															if cpu_offloaded_model is not None:
																					cpu_offload(_UpperCamelCase					,     _UpperCamelCase				)




			def _lowerCAmelCase   (self			:Union[str, Any]					,     _UpperCamelCase			:Optional[Any]=0				)->      Optional[int]:
									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.'''				)

									__A      =       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)

									__A      =       None
									for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
															__A     ,		__A      =       cpu_offload_with_hook(_UpperCamelCase					,     _UpperCamelCase					,     prev_module_hook=_UpperCamelCase				)

									if self.safety_checker is not None:
															__A     ,		__A      =       cpu_offload_with_hook(self.safety_checker					,     _UpperCamelCase					,     prev_module_hook=_UpperCamelCase				)

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




			@property
			def _lowerCAmelCase   (self			:int				)->      Optional[int]:
									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 _lowerCAmelCase   (self			:str					,     _UpperCamelCase			:Optional[Any]					,     _UpperCamelCase			:List[Any]					,     _UpperCamelCase			:Union[str, Any]					,     _UpperCamelCase			:Union[str, Any]					,     )->      Tuple:
									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
									__A      =       0
									# get prompt text embeddings
									__A      =       self.tokenizer(
									    _UpperCamelCase					,     padding='''max_length'''					,     max_length=self.tokenizer.model_max_length					,     truncation=_UpperCamelCase					,     return_tensors='''pt'''					,     )
									__A      =       text_inputs.input_ids
									__A      =       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				):
															__A      =       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}"""				)

									__A      =       self.text_encoder(text_input_ids.to(_UpperCamelCase				)				)
									__A      =       text_encoder_output.text_embeds

									__A      =       prompt_embeds.repeat_interleave(_UpperCamelCase					,     dim=0				)
									# in Shap-E it normalize the prompt_embeds and then later rescale it
									__A      =       prompt_embeds / torch.linalg.norm(_UpperCamelCase					,     dim=-1					,     keepdim=_UpperCamelCase				)

									if do_classifier_free_guidance:
															__A      =       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
															__A      =       torch.cat([negative_prompt_embeds, prompt_embeds]				)

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

									return prompt_embeds






			@torch.no_grad()
			@replace_example_docstring(_UpperCamelCase				)
			def __call__(self			:List[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					,     )->      Dict:

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

									__A      =       self._execution_device

									__A      =       batch_size * num_images_per_prompt

									__A      =       guidance_scale > 1.0
									__A      =       self._encode_prompt(_UpperCamelCase					,     _UpperCamelCase					,     _UpperCamelCase					,     _UpperCamelCase				)

									# prior

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

									__A      =       self.prior.config.num_embeddings
									__A      =       self.prior.config.embedding_dim

									__A      =       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
									__A      =       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
															__A      =       torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
															__A      =       self.scheduler.scale_model_input(_UpperCamelCase					,     _UpperCamelCase				)

															__A      =       self.prior(
															    _UpperCamelCase					,     timestep=_UpperCamelCase					,     proj_embedding=_UpperCamelCase					,     ).predicted_image_embedding

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

															if do_classifier_free_guidance is not None:
																					__A     ,		__A      =       noise_pred.chunk(2				)
																					__A      =       noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

															__A      =       self.scheduler.step(
															    _UpperCamelCase					,     timestep=_UpperCamelCase					,     sample=_UpperCamelCase					,     ).prev_sample

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

									__A      =       []
									for i, latent in enumerate(_UpperCamelCase				):
															__A      =       self.renderer.decode(
															    latent[None, :]					,     _UpperCamelCase					,     size=_UpperCamelCase					,     ray_batch_size=4096					,     n_coarse_samples=64					,     n_fine_samples=128					,     )
															images.append(_UpperCamelCase				)

									__A      =       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}"""				)

									__A      =       images.cpu().numpy()

									if output_type == "pil":
															__A      =       [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				)












class A_      :




   def __init__(self			:Union[str, Any]				)->      None:
         # numbermap keys are the number and its values are lists of indexes sorted
         # in ascending order
         __A      =       {}
         # indexmap keys are an index and it's values are the number at that index
         __A      =       {}




   def _lowerCAmelCase   (self			:Any					,     _UpperCamelCase			:list | str | range					,     _UpperCamelCase			:int				)->      list[int]:
         if isinstance(_UpperCamelCase					,     (range, str)				):
               __A      =       list(_UpperCamelCase				)
         elif not isinstance(_UpperCamelCase					,     _UpperCamelCase				):
               raise TypeError(
                   '''binary_search_delete() only accepts either a list, range or str'''				)

         __A      =       0
         __A      =       len(_UpperCamelCase				) - 1

         while low <= high:
               __A      =       (low + high) // 2
               if array[mid] == item:
                     array.pop(_UpperCamelCase				)
                     return array
               elif array[mid] < item:
                     __A      =       mid + 1
               else:
                     __A      =       mid - 1
         raise ValueError(
             '''Either the item is not in the array or the array was unsorted'''				)




   def _lowerCAmelCase   (self			:Optional[Any]					,     _UpperCamelCase			:list | str | range					,     _UpperCamelCase			:int				)->      list[int]:
         if isinstance(_UpperCamelCase					,     (range, str)				):
               __A      =       list(_UpperCamelCase				)
         elif not isinstance(_UpperCamelCase					,     _UpperCamelCase				):
               raise TypeError(
                   '''binary_search_insert() only accepts either a list, range or str'''				)

         __A      =       0
         __A      =       len(_UpperCamelCase				) - 1

         while low <= high:
               __A      =       (low + high) // 2
               if array[mid] == index:
                     # If the item already exists in the array,
                     # insert it after the existing item
                     array.insert(mid + 1					,     _UpperCamelCase				)
                     return array
               elif array[mid] < index:
                     __A      =       mid + 1
               else:
                     __A      =       mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
         array.insert(_UpperCamelCase					,     _UpperCamelCase				)
         return array




   def _lowerCAmelCase   (self			:Union[str, Any]					,     _UpperCamelCase			:int					,     _UpperCamelCase			:int				)->      None:
         # Remove previous index
         if index in self.indexmap:
               __A      =       self.indexmap[index]
               if len(self.numbermap[n]				) == 1:
                     del self.numbermap[n]
               else:
                     __A      =       self.binary_search_delete(self.numbermap[n]					,     _UpperCamelCase				)

        # Set new index
         __A      =       number

         # Number not seen before or empty so insert number value
         if number not in self.numbermap:
               __A      =       [index]

         # Here we need to perform a binary search insertion in order to insert
         # The item in the correct place
         else:
               __A      =       self.binary_search_insert(
                   self.numbermap[number]					,     _UpperCamelCase				)






   def _lowerCAmelCase   (self			:List[Any]					,     _UpperCamelCase			:int				)->      int:
         return self.numbermap.get(_UpperCamelCase					,     [-1]				)[0]


if __name__ == "__main__":
       import doctest

       doctest.testmod()





