





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

lowercase      :       Optional[int]									=  """
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")

        >>> repo = \"openai/shap-e-img2img\"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"
        >>> image = load_image(image_url).convert(\"RGB\")

        >>> images = pipe(
        ...     image,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")
        ```
"""





@dataclass
class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   __A   :				Union[PIL.Image.Image, np.ndarray]





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  )			->						List[str]:







          '''simple docstring'''

          super().__init__()

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

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						Any:







          '''simple docstring'''

          if latents is None:
                 a__						:	List[Any] =				randn_tensor(lowercase					,  generator=lowercase					,  device=lowercase					,  dtype=lowercase)
          else:
                 if latents.shape != shape:
                        raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}')
                 a__						:	List[str] =				latents.to(lowercase)

          a__						:	Dict =				latents * scheduler.init_noise_sigma
          return latents

   def 							__lowercase		(					self					,  lowercase=0)			->						Optional[int]:







          '''simple docstring'''

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

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

          a__						:	Optional[Any] =				[self.image_encoder, self.prior]
          for cpu_offloaded_model in models:
                 if cpu_offloaded_model is not None:
                        cpu_offload(lowercase					,  lowercase)

   @property
   def 							__lowercase		(					self)			->						Any:







          '''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(lowercase					,  '_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					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  )			->						Any:







          '''simple docstring'''

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

          if not isinstance(lowercase					,  torch.Tensor):
                 a__						:	List[Any] =				self.image_processor(lowercase					,  return_tensors='pt').pixel_values[0].unsqueeze(0)

          a__						:	Optional[Any] =				image.to(dtype=self.image_encoder.dtype					,  device=lowercase)

          a__						:	int =				self.image_encoder(lowercase)['last_hidden_state']
          a__						:	Optional[Any] =				image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

          a__						:	Optional[int] =				image_embeds.repeat_interleave(lowercase					,  dim=0)

          if do_classifier_free_guidance:
                 a__						:	Tuple =				torch.zeros_like(lowercase)

                 # 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__						:	int =				torch.cat([negative_image_embeds, image_embeds])

          return image_embeds

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







          '''simple docstring'''

          if isinstance(lowercase					,  PIL.Image.Image):
                 a__						:	List[str] =				1
          elif isinstance(lowercase					,  torch.Tensor):
                 a__						:	List[str] =				image.shape[0]
          elif isinstance(lowercase					,  lowercase) and isinstance(image[0]					,  (torch.Tensor, PIL.Image.Image)):
                 a__						:	List[str] =				len(lowercase)
          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(lowercase)}')

          a__						:	Tuple =				self._execution_device

          a__						:	List[Any] =				batch_size * num_images_per_prompt

          a__						:	Optional[Any] =				guidance_scale > 1.0
          a__						:	Optional[int] =				self._encode_image(lowercase					,  lowercase					,  lowercase					,  lowercase)

          # prior

          self.scheduler.set_timesteps(lowercase					,  device=lowercase)
          a__						:	str =				self.scheduler.timesteps

          a__						:	Tuple =				self.prior.config.num_embeddings
          a__						:	Optional[int] =				self.prior.config.embedding_dim

          a__						:	Dict =				self.prepare_latents(
              (batch_size, num_embeddings * embedding_dim)					,  image_embeds.dtype					,  lowercase					,  lowercase					,  lowercase					,  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__						:	Tuple =				latents.reshape(latents.shape[0]					,  lowercase					,  lowercase)

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

                 a__						:	Tuple =				self.prior(
                     lowercase					,  timestep=lowercase					,  proj_embedding=lowercase					,  ).predicted_image_embedding

                 # remove the variance
                 a__ ,							a__						:	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:
                        a__ ,							a__						:	Any =				noise_pred.chunk(2)
                        a__						:	str =				noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

                 a__						:	List[str] =				self.scheduler.step(
                     lowercase					,  timestep=lowercase					,  sample=lowercase					,  ).prev_sample

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

          a__						:	List[Any] =				[]
          for i, latent in enumerate(lowercase):
                 print()
                 a__						:	Dict =				self.renderer.decode(
                     latent[None, :]					,  lowercase					,  size=lowercase					,  ray_batch_size=4096					,  n_coarse_samples=64					,  n_fine_samples=128					,  )

                 images.append(lowercase)

          a__						:	Union[str, Any] =				torch.stack(lowercase)

          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__						:	List[Any] =				images.cpu().numpy()

          if output_type == "pil":
                 a__						:	Any =				[self.numpy_to_pil(lowercase) 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=lowercase)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase      :       List[Any]									=  {
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase      :       Dict									=  [
        """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """NllbMoeForConditionalGeneration""",
        """NllbMoeModel""",
        """NllbMoePreTrainedModel""",
        """NllbMoeTop2Router""",
        """NllbMoeSparseMLP""",
    ]


if TYPE_CHECKING:
    from .configuration_nllb_moe import (
        NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
        NllbMoeConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_nllb_moe import (
            NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
            NllbMoeForConditionalGeneration,
            NllbMoeModel,
            NllbMoePreTrainedModel,
            NllbMoeSparseMLP,
            NllbMoeTopaRouter,
        )


else:
    import sys

    lowercase      :       List[Any]									=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


