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

lowercase__  :      Union[str, 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							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	42







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  def __init__(      self			:       List[str]  ,							UpperCAmelCase__			:       PriorTransformer  ,							UpperCAmelCase__			:       CLIPVisionModel  ,							UpperCAmelCase__			:       CLIPImageProcessor  ,							UpperCAmelCase__			:       HeunDiscreteScheduler  ,							UpperCAmelCase__			:       ShapERenderer  ,							)				->Any:
        super().__init__()

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






  def  lowerCAmelCase__ (      self			:       List[Any]  ,							UpperCAmelCase__			:       Optional[Any]  ,							UpperCAmelCase__			:       Union[str, Any]  ,							UpperCAmelCase__			:       List[str]  ,							UpperCAmelCase__			:       Dict  ,							UpperCAmelCase__			:       Optional[int]  ,							UpperCAmelCase__			:       Optional[Any]  )				->str:
        if latents is None:
              UpperCAmelCase_            =  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}"""  )
              UpperCAmelCase_            =  latents.to(UpperCAmelCase__  )

        UpperCAmelCase_            =  latents * scheduler.init_noise_sigma
        return latents






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

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

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






  @property
  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->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(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			:       Any  ,							UpperCAmelCase__			:       Union[str, Any]  ,							UpperCAmelCase__			:       Dict  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       Any  ,							)				->Any:
        if isinstance(UpperCAmelCase__  ,							UpperCAmelCase__  ) and isinstance(image[0]  ,							torch.Tensor  ):
              UpperCAmelCase_            =  torch.cat(UpperCAmelCase__  ,							axis=0  ) if image[0].ndim == 4 else torch.stack(UpperCAmelCase__  ,							axis=0  )

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

        UpperCAmelCase_            =  image.to(dtype=self.image_encoder.dtype  ,							device=UpperCAmelCase__  )

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

        UpperCAmelCase_            =  image_embeds.repeat_interleave(UpperCAmelCase__  ,							dim=0  )

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

        return image_embeds






  @torch.no_grad()
  @replace_example_docstring(UpperCAmelCase__  )
  def __call__(      self			:       Optional[int]  ,							UpperCAmelCase__			:       Union[PIL.Image.Image, List[PIL.Image.Image]]  ,							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  ,							)				->int:

        if isinstance(UpperCAmelCase__  ,							PIL.Image.Image  ):
              UpperCAmelCase_            =  1
        elif isinstance(UpperCAmelCase__  ,							torch.Tensor  ):
              UpperCAmelCase_            =  image.shape[0]
        elif isinstance(UpperCAmelCase__  ,							UpperCAmelCase__  ) and isinstance(image[0]  ,							(torch.Tensor, PIL.Image.Image)  ):
              UpperCAmelCase_            =  len(UpperCAmelCase__  )
        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(UpperCAmelCase__  )}"""  )

        UpperCAmelCase_            =  self._execution_device

        UpperCAmelCase_            =  batch_size * num_images_per_prompt

        UpperCAmelCase_            =  guidance_scale > 1.0
        UpperCAmelCase_            =  self._encode_image(UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  )

        # prior

        self.scheduler.set_timesteps(UpperCAmelCase__  ,							device=UpperCAmelCase__  )
        UpperCAmelCase_            =  self.scheduler.timesteps

        UpperCAmelCase_            =  self.prior.config.num_embeddings
        UpperCAmelCase_            =  self.prior.config.embedding_dim

        UpperCAmelCase_            =  self.prepare_latents(
            (batch_size, num_embeddings * embedding_dim)  ,							image_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
        UpperCAmelCase_            =  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
              UpperCAmelCase_            =  torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents
              UpperCAmelCase_            =  self.scheduler.scale_model_input(UpperCAmelCase__  ,							UpperCAmelCase__  )

              UpperCAmelCase_            =  self.prior(
                  UpperCAmelCase__  ,							timestep=UpperCAmelCase__  ,							proj_embedding=UpperCAmelCase__  ,							).predicted_image_embedding

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

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

              UpperCAmelCase_            =  self.scheduler.step(
                  UpperCAmelCase__  ,							timestep=UpperCAmelCase__  ,							sample=UpperCAmelCase__  ,							).prev_sample

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

        UpperCAmelCase_            =  []
        for i, latent in enumerate(UpperCAmelCase__  ):
              print()
              UpperCAmelCase_            =  self.renderer.decode(
                  latent[None, :]  ,							UpperCAmelCase__  ,							size=UpperCAmelCase__  ,							ray_batch_size=4096  ,							n_coarse_samples=64  ,							n_fine_samples=128  ,							)

              images.append(UpperCAmelCase__  )

        UpperCAmelCase_            =  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}"""  )

        UpperCAmelCase_            =  images.cpu().numpy()

        if output_type == "pil":
              UpperCAmelCase_            =  [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__  )






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase__  :      Union[str, Any]        =					{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      Union[str, Any]        =					["MobileViTFeatureExtractor"]
      lowercase__  :      List[Any]        =					["MobileViTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      Dict        =					[
          "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "MobileViTForImageClassification",
          "MobileViTForSemanticSegmentation",
          "MobileViTModel",
          "MobileViTPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      Optional[int]        =					[
          "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFMobileViTForImageClassification",
          "TFMobileViTForSemanticSegmentation",
          "TFMobileViTModel",
          "TFMobileViTPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_mobilevit import MobileViTFeatureExtractor
            from .image_processing_mobilevit import MobileViTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilevit import (
                MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileViTForImageClassification,
                MobileViTForSemanticSegmentation,
                MobileViTModel,
                MobileViTPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mobilevit import (
                TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMobileViTForImageClassification,
                TFMobileViTForSemanticSegmentation,
                TFMobileViTModel,
                TFMobileViTPreTrainedModel,
            )


else:
      import sys

      lowercase__  :      int        =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





