
'''simple docstring'''







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

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

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


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

lowercase    :							Optional[Any]      =					"\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n        >>> repo = \"openai/shap-e-img2img\"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"\n        >>> image = load_image(image_url).convert(\"RGB\")\n\n        >>> images = pipe(\n        ...     image,\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], \"corgi_3d.gif\")\n        ```\n"


@dataclass
class 						__UpperCAmelCase      (					_lowerCamelCase						):
							__lowercase      		=       42


class 						__UpperCAmelCase      (					_lowerCamelCase						):


							def __init__(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   ):





								"""simple docstring"""






								super().__init__()

								self.register_modules(
								    prior=lowerCAmelCase_    ,   image_encoder=lowerCAmelCase_    ,   image_processor=lowerCAmelCase_    ,   scheduler=lowerCAmelCase_    ,   renderer=lowerCAmelCase_    ,   )


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								if latents is None:
									_snake_case   						=		randn_tensor(lowerCAmelCase_    ,   generator=lowerCAmelCase_    ,   device=lowerCAmelCase_    ,   dtype=lowerCAmelCase_  )
								else:
									if latents.shape != shape:
										raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'  )
									_snake_case   						=		latents.to(lowerCAmelCase_  )

								_snake_case   						=		latents * scheduler.init_noise_sigma
								return latents


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_=0  ):





								"""simple docstring"""






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

								_snake_case   						=		torch.device(F'cuda:{gpu_id}'  )

								_snake_case   						=		[self.image_encoder, self.prior]
								for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
										cpu_offload(lowerCAmelCase_    ,   lowerCAmelCase_  )


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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





								"""simple docstring"""






								if isinstance(lowerCAmelCase_    ,   lowerCAmelCase_  ) and isinstance(image[0]    ,   torch.Tensor  ):
									_snake_case   						=		torch.cat(lowerCAmelCase_    ,   axis=0  ) if image[0].ndim == 4 else torch.stack(lowerCAmelCase_    ,   axis=0  )

								if not isinstance(lowerCAmelCase_    ,   torch.Tensor  ):
									_snake_case   						=		self.image_processor(lowerCAmelCase_    ,   return_tensors='pt'  ).pixel_values[0].unsqueeze(0  )

								_snake_case   						=		image.to(dtype=self.image_encoder.dtype    ,   device=lowerCAmelCase_  )

								_snake_case   						=		self.image_encoder(lowerCAmelCase_  )['last_hidden_state']
								_snake_case   						=		image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

								_snake_case   						=		image_embeds.repeat_interleave(lowerCAmelCase_    ,   dim=0  )

								if do_classifier_free_guidance:
									_snake_case   						=		torch.zeros_like(lowerCAmelCase_  )

									# 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
									_snake_case   						=		torch.cat([negative_image_embeds, image_embeds]  )

								return image_embeds




							@torch.no_grad()
							@replace_example_docstring(lowerCAmelCase_  )
							def __call__(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = 1    ,   lowerCAmelCase_ = 25    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = 4.0    ,   lowerCAmelCase_ = 64    ,   lowerCAmelCase_ = "pil"    ,   lowerCAmelCase_ = True    ,   ):





								"""simple docstring"""






								if isinstance(lowerCAmelCase_    ,   PIL.Image.Image  ):
									_snake_case   						=		1
								elif isinstance(lowerCAmelCase_    ,   torch.Tensor  ):
									_snake_case   						=		image.shape[0]
								elif isinstance(lowerCAmelCase_    ,   lowerCAmelCase_  ) and isinstance(image[0]    ,   (torch.Tensor, PIL.Image.Image)  ):
									_snake_case   						=		len(lowerCAmelCase_  )
								else:
									raise ValueError(
									    F'`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(lowerCAmelCase_  )}'  )

								_snake_case   						=		self._execution_device

								_snake_case   						=		batch_size * num_images_per_prompt

								_snake_case   						=		guidance_scale > 1.0
								_snake_case   						=		self._encode_image(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_  )

								# prior

								self.scheduler.set_timesteps(lowerCAmelCase_    ,   device=lowerCAmelCase_  )
								_snake_case   						=		self.scheduler.timesteps

								_snake_case   						=		self.prior.config.num_embeddings
								_snake_case   						=		self.prior.config.embedding_dim

								_snake_case   						=		self.prepare_latents(
								    (batch_size, num_embeddings * embedding_dim)    ,   image_embeds.dtype    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   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
								_snake_case   						=		latents.reshape(latents.shape[0]    ,   lowerCAmelCase_    ,   lowerCAmelCase_  )

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

									_snake_case   						=		self.prior(
									    lowerCAmelCase_    ,   timestep=lowerCAmelCase_    ,   proj_embedding=lowerCAmelCase_    ,   ).predicted_image_embedding

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

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

									_snake_case   						=		self.scheduler.step(
									    lowerCAmelCase_    ,   timestep=lowerCAmelCase_    ,   sample=lowerCAmelCase_    ,   ).prev_sample

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

								_snake_case   						=		[]
								for i, latent in enumerate(lowerCAmelCase_  ):
									print()
									_snake_case   						=		self.renderer.decode(
									    latent[None, :]    ,   lowerCAmelCase_    ,   size=lowerCAmelCase_    ,   ray_batch_size=40_96    ,   n_coarse_samples=64    ,   n_fine_samples=1_28    ,   )

									images.append(lowerCAmelCase_  )

								_snake_case   						=		torch.stack(lowerCAmelCase_  )

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

								_snake_case   						=		images.cpu().numpy()

								if output_type == "pil":
									_snake_case   						=		[self.numpy_to_pil(lowerCAmelCase_  ) 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=lowerCAmelCase_  )





from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments







def        SCREAMING_SNAKE_CASE_						(			)	->		Any:

       """simple docstring"""

       a_					:		Optional[Any]							=  HfArgumentParser(__A				)
       a_					:		Optional[int]							=  parser.parse_args_into_dataclasses()[0]
       a_					:		List[Any]							=  TensorFlowBenchmark(args=__A				)
       try:
              a_					:		List[str]							=  parser.parse_args_into_dataclasses()[0]
       except ValueError as e:
              a_					:		Dict							=  'Arg --no_{0} is no longer used, please use --no-{0} instead.'
              a_					:		Dict							=  ' '.join(str(__A				).split(' '				)[:-1]				)
              a_					:		int							=  ''
              a_					:		int							=  eval(str(__A				).split(' '				)[-1]				)
              a_					:		Any							=  []
              for arg in depreciated_args:
                     # arg[2:] removes '--'
                     if arg[2:] in TensorFlowBenchmark.deprecated_args:
                            # arg[5:] removes '--no_'
                            full_error_msg += arg_error_msg.format(arg[5:]				)
                     else:
                            wrong_args.append(__A				)
              if len(__A				) > 0:
                     a_					:		str							=  full_error_msg + begin_error_msg + str(__A				)
              raise ValueError(__A				)
       benchmark.run()


if __name__ == "__main__":
   main()


