




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


lowerCamelCase__					:  Union[str, Any]      							=				logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCamelCase__					:  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 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       42





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 def __init__(				self    :						Optional[Any]							,							_lowerCAmelCase    :						PriorTransformer							,							_lowerCAmelCase    :						CLIPVisionModel							,							_lowerCAmelCase    :						CLIPImageProcessor							,							_lowerCAmelCase    :						HeunDiscreteScheduler							,							_lowerCAmelCase    :						ShapERenderer							,							):
       super().__init__()

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



 def  lowerCAmelCase_    (				self    :						Tuple							,							_lowerCAmelCase    :						List[Any]							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						Tuple							,							_lowerCAmelCase    :						List[Any]							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						List[Any]		):
       if latents is None:
             SCREAMING_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}"		)
             SCREAMING_SNAKE_CASE_      		=     latents.to(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     latents * scheduler.init_noise_sigma
       return latents



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

       SCREAMING_SNAKE_CASE_      		=     torch.device(F"cuda:{gpu_id}"		)

       SCREAMING_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    :						int		):
       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    :						Optional[Any]							,							_lowerCAmelCase    :						List[Any]							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						List[str]							,							):
       if isinstance(_lowerCAmelCase							,							_lowerCAmelCase		) and isinstance(image[0]							,							torch.Tensor		):
             SCREAMING_SNAKE_CASE_      		=     torch.cat(_lowerCAmelCase							,							axis=0		) if image[0].ndim == 4 else torch.stack(_lowerCAmelCase							,							axis=0		)

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

       SCREAMING_SNAKE_CASE_      		=     image.to(dtype=self.image_encoder.dtype							,							device=_lowerCAmelCase		)

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

       SCREAMING_SNAKE_CASE_      		=     image_embeds.repeat_interleave(_lowerCAmelCase							,							dim=0		)

       if do_classifier_free_guidance:
             SCREAMING_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
             SCREAMING_SNAKE_CASE_      		=     torch.cat([negative_image_embeds, image_embeds]		)

       return image_embeds




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

       if isinstance(_lowerCAmelCase							,							PIL.Image.Image		):
             SCREAMING_SNAKE_CASE_      		=     1
       elif isinstance(_lowerCAmelCase							,							torch.Tensor		):
             SCREAMING_SNAKE_CASE_      		=     image.shape[0]
       elif isinstance(_lowerCAmelCase							,							_lowerCAmelCase		) and isinstance(image[0]							,							(torch.Tensor, PIL.Image.Image)		):
             SCREAMING_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		)}"		)

       SCREAMING_SNAKE_CASE_      		=     self._execution_device

       SCREAMING_SNAKE_CASE_      		=     batch_size * num_images_per_prompt

       SCREAMING_SNAKE_CASE_      		=     guidance_scale > 1.0
       SCREAMING_SNAKE_CASE_      		=     self._encode_image(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)

       # prior

       self.scheduler.set_timesteps(_lowerCAmelCase							,							device=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.scheduler.timesteps

       SCREAMING_SNAKE_CASE_      		=     self.prior.config.num_embeddings
       SCREAMING_SNAKE_CASE_      		=     self.prior.config.embedding_dim

       SCREAMING_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
       SCREAMING_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
             SCREAMING_SNAKE_CASE_      		=     torch.cat([latents] * 2		) if do_classifier_free_guidance else latents
             SCREAMING_SNAKE_CASE_      		=     self.scheduler.scale_model_input(_lowerCAmelCase							,							_lowerCAmelCase		)

             SCREAMING_SNAKE_CASE_      		=     self.prior(
                 _lowerCAmelCase							,							timestep=_lowerCAmelCase							,							proj_embedding=_lowerCAmelCase							,							).predicted_image_embedding

             # remove the variance
             SCREAMING_SNAKE_CASE_		,					SCREAMING_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:
                   SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     noise_pred.chunk(2		)
                   SCREAMING_SNAKE_CASE_      		=     noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

             SCREAMING_SNAKE_CASE_      		=     self.scheduler.step(
                 _lowerCAmelCase							,							timestep=_lowerCAmelCase							,							sample=_lowerCAmelCase							,							).prev_sample

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

       SCREAMING_SNAKE_CASE_      		=     []
       for i, latent in enumerate(_lowerCAmelCase		):
             print()
             SCREAMING_SNAKE_CASE_      		=     self.renderer.decode(
                 latent[None, :]							,							_lowerCAmelCase							,							size=_lowerCAmelCase							,							ray_batch_size=4_096							,							n_coarse_samples=64							,							n_fine_samples=128							,							)

             images.append(_lowerCAmelCase		)

       SCREAMING_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}"		)

       SCREAMING_SNAKE_CASE_      		=     images.cpu().numpy()

       if output_type == "pil":
             SCREAMING_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 typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__					:  Optional[int]      							=				{'configuration_mmbt': ['MMBTConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__					:  Any      							=				['MMBTForClassification', 'MMBTModel', 'ModalEmbeddings']


if TYPE_CHECKING:
   from .configuration_mmbt import MMBTConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
   import sys

   lowerCamelCase__					:  str      							=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)