





import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
       import transformer_engine.pytorch as te



def _a							(      lowerCamelCase:  int   ,							lowerCamelCase:  Union[str, Any]=True   ,							lowerCamelCase:  Optional[Any]=True   ,							lowerCamelCase:  Union[str, Any]=True					)      ->		Tuple:







      '''simple docstring'''






      if not is_fpa_available():
            raise ImportError('''Using `convert_model` requires transformer_engine to be installed.'''					)
      for name, module in model.named_children():
            if isinstance(lowerCamelCase   ,							nn.Linear					) and to_transformer_engine and _convert_linear:
                  # Return early if the linear layer weights are not multiples of 16
                  if any(p % 16 != 0 for p in module.weight.shape					):
                        return
                  __A			  =							module.bias is not None
                  __A			  =							te.Linear(
                      module.in_features   ,							module.out_features   ,							bias=lowerCamelCase   ,							params_dtype=module.weight.dtype					)
                  __A			  =							module.weight.data.clone()
                  if has_bias:
                        __A			  =							module.bias.data.clone()

                  setattr(lowerCamelCase   ,							lowerCamelCase   ,							lowerCamelCase					)
            elif isinstance(lowerCamelCase   ,							nn.LayerNorm					) and to_transformer_engine and _convert_ln:
                  __A			  =							te.LayerNorm(module.normalized_shape[0]   ,							eps=module.eps   ,							params_dtype=module.weight.dtype					)
                  __A			  =							module.weight.data.clone()
                  __A			  =							module.bias.data.clone()

                  setattr(lowerCamelCase   ,							lowerCamelCase   ,							lowerCamelCase					)
            elif isinstance(lowerCamelCase   ,							te.Linear					) and not to_transformer_engine and _convert_linear:
                  __A			  =							module.bias is not None
                  __A			  =							nn.Linear(
                      module.in_features   ,							module.out_features   ,							bias=lowerCamelCase   ,							params_dtype=module.weight.dtype					)
                  __A			  =							module.weight.data.clone()
                  if has_bias:
                        __A			  =							module.bias.data.clone()

                  setattr(lowerCamelCase   ,							lowerCamelCase   ,							lowerCamelCase					)
            elif isinstance(lowerCamelCase   ,							te.LayerNorm					) and not to_transformer_engine and _convert_ln:
                  __A			  =							nn.LayerNorm(module.normalized_shape[0]   ,							eps=module.eps   ,							params_dtype=module.weight.dtype					)
                  __A			  =							module.weight.data.clone()
                  __A			  =							module.bias.data.clone()

                  setattr(lowerCamelCase   ,							lowerCamelCase   ,							lowerCamelCase					)
            else:
                  convert_model(
                      lowerCamelCase   ,							to_transformer_engine=lowerCamelCase   ,							_convert_linear=_convert_linear   ,							_convert_ln=_convert_ln   ,							)







def _a							(      lowerCamelCase:  Tuple					)      ->		Optional[int]:







      '''simple docstring'''






      if not is_fpa_available():
            raise ImportError('''Using `has_transformer_engine_layers` requires transformer_engine to be installed.'''					)
      for m in model.modules():
            if isinstance(lowerCamelCase   ,							(te.LayerNorm, te.Linear)					):
                  return True
      return False












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

snake_case__   :				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      :List[str]       ,  _UpperCamelCase      :PriorTransformer       ,  _UpperCamelCase      :CLIPTextModelWithProjection       ,  _UpperCamelCase      :CLIPTokenizer       ,  _UpperCamelCase      :HeunDiscreteScheduler       ,  _UpperCamelCase      :ShapERenderer       ,  )->   Tuple:
         super().__init__()

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




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

         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					)





