

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


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

UpperCAmelCase__ :     Optional[Any]				    =    """
    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"""




  UpperCAmelCase__      : Tuple			       =4_2







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  def __init__(			self    : List[str]				,				UpperCAmelCase__    : List[str]				,				UpperCAmelCase__    : Optional[int]				,				UpperCAmelCase__    : Dict				,				UpperCAmelCase__    : int				,				UpperCAmelCase__    : List[str]				,				)					->List[str]:
        """simple docstring"""







        super().__init__()

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


  def  _lowercase      (			self    : Tuple				,				UpperCAmelCase__    : Any				,				UpperCAmelCase__    : int				,				UpperCAmelCase__    : int				,				UpperCAmelCase__    : Any				,				UpperCAmelCase__    : Optional[int]				,				UpperCAmelCase__    : Dict		)					->Any:
        """simple docstring"""







        if latents is None:
              SCREAMING_SNAKE_CASE :      List[str]       =   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}"		)
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   latents.to(UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   latents * scheduler.init_noise_sigma
        return latents


  def  _lowercase      (			self    : Dict				,				UpperCAmelCase__    : Any=0		)					->int:
        """simple docstring"""







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

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

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


  @property
  def  _lowercase      (			self    : Dict		)					->Optional[int]:
        """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(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  _lowercase      (			self    : str				,				UpperCAmelCase__    : Union[str, Any]				,				UpperCAmelCase__    : Optional[int]				,				UpperCAmelCase__    : str				,				UpperCAmelCase__    : List[str]				,				)					->Dict:
        """simple docstring"""







        if isinstance(UpperCAmelCase__				,				UpperCAmelCase__		) and isinstance(image[0]				,				torch.Tensor		):
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   torch.cat(UpperCAmelCase__				,				axis=0		) if image[0].ndim == 4 else torch.stack(UpperCAmelCase__				,				axis=0		)

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

        SCREAMING_SNAKE_CASE :      Any       =   image.to(dtype=self.image_encoder.dtype				,				device=UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      Optional[Any]       =   self.image_encoder(UpperCAmelCase__		)["""last_hidden_state"""]
        SCREAMING_SNAKE_CASE :      Any       =   image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

        SCREAMING_SNAKE_CASE :      List[str]       =   image_embeds.repeat_interleave(UpperCAmelCase__				,				dim=0		)

        if do_classifier_free_guidance:
              SCREAMING_SNAKE_CASE :      List[str]       =   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
              SCREAMING_SNAKE_CASE :      List[Any]       =   torch.cat([negative_image_embeds, image_embeds]		)

        return image_embeds




  @torch.no_grad()
  @replace_example_docstring(UpperCAmelCase__		)
  def __call__(			self    : Tuple				,				UpperCAmelCase__    : Dict				,				UpperCAmelCase__    : List[Any] = 1				,				UpperCAmelCase__    : Optional[int] = 2_5				,				UpperCAmelCase__    : int = None				,				UpperCAmelCase__    : Dict = None				,				UpperCAmelCase__    : Optional[int] = 4.0				,				UpperCAmelCase__    : Optional[Any] = 6_4				,				UpperCAmelCase__    : Any = "pil"				,				UpperCAmelCase__    : Optional[Any] = True				,				)					->Any:
        """simple docstring"""







        if isinstance(UpperCAmelCase__				,				PIL.Image.Image		):
              SCREAMING_SNAKE_CASE :      List[Any]       =   1
        elif isinstance(UpperCAmelCase__				,				torch.Tensor		):
              SCREAMING_SNAKE_CASE :      List[str]       =   image.shape[0]
        elif isinstance(UpperCAmelCase__				,				UpperCAmelCase__		) and isinstance(image[0]				,				(torch.Tensor, PIL.Image.Image)		):
              SCREAMING_SNAKE_CASE :      Dict       =   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__		)}"		)

        SCREAMING_SNAKE_CASE :      List[str]       =   self._execution_device

        SCREAMING_SNAKE_CASE :      Any       =   batch_size * num_images_per_prompt

        SCREAMING_SNAKE_CASE :      Tuple       =   guidance_scale > 1.0
        SCREAMING_SNAKE_CASE :      Any       =   self._encode_image(UpperCAmelCase__				,				UpperCAmelCase__				,				UpperCAmelCase__				,				UpperCAmelCase__		)

        # prior

        self.scheduler.set_timesteps(UpperCAmelCase__				,				device=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.scheduler.timesteps

        SCREAMING_SNAKE_CASE :      List[str]       =   self.prior.config.num_embeddings
        SCREAMING_SNAKE_CASE :      str       =   self.prior.config.embedding_dim

        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   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
        SCREAMING_SNAKE_CASE :      Any       =   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
              SCREAMING_SNAKE_CASE :      List[Any]       =   torch.cat([latents] * 2		) if do_classifier_free_guidance else latents
              SCREAMING_SNAKE_CASE :      Tuple       =   self.scheduler.scale_model_input(UpperCAmelCase__				,				UpperCAmelCase__		)

              SCREAMING_SNAKE_CASE :      str       =   self.prior(
                  UpperCAmelCase__				,				timestep=UpperCAmelCase__				,				proj_embedding=UpperCAmelCase__				,				).predicted_image_embedding

              # remove the variance
              SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Optional[int]       =   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 :      List[str]       =   noise_pred.chunk(2		)
                    SCREAMING_SNAKE_CASE :      Optional[int]       =   noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

              SCREAMING_SNAKE_CASE :      Any       =   self.scheduler.step(
                  UpperCAmelCase__				,				timestep=UpperCAmelCase__				,				sample=UpperCAmelCase__				,				).prev_sample

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

        SCREAMING_SNAKE_CASE :      int       =   []
        for i, latent in enumerate(UpperCAmelCase__		):
              print()
              SCREAMING_SNAKE_CASE :      Optional[int]       =   self.renderer.decode(
                  latent[None, :]				,				UpperCAmelCase__				,				size=UpperCAmelCase__				,				ray_batch_size=4_0_9_6				,				n_coarse_samples=6_4				,				n_fine_samples=1_2_8				,				)

              images.append(UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      Optional[Any]       =   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}"		)

        SCREAMING_SNAKE_CASE :      List[str]       =   images.cpu().numpy()

        if output_type == "pil":
              SCREAMING_SNAKE_CASE :      str       =   [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__		)







import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request

from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version

from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging


UpperCAmelCase__ :     List[str]				    =    (
    """https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"""
)


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






def 					__lowercase      (     )     ->						List[Any]:
      SCREAMING_SNAKE_CASE :      Optional[int]       =   """https://pypi.org/pypi/diffusers/json"""
      SCREAMING_SNAKE_CASE :      Dict       =   json.loads(request.urlopen(_A			).read()			)["""releases"""].keys()
      return sorted(_A						,    key=lambda _A			: version.Version(_A			)			)






def 					__lowercase      (     )     ->						Any:
      # This function has already been executed if HF_MODULES_CACHE already is in the Python path.
      if HF_MODULES_CACHE in sys.path:
            return

      sys.path.append(_A			)
      os.makedirs(_A						,    exist_ok=_A			)
      SCREAMING_SNAKE_CASE :      Dict       =   Path(_A			) / """__init__.py"""
      if not init_path.exists():
            init_path.touch()






def 					__lowercase      (     _A			)     ->						Any:
      init_hf_modules()
      SCREAMING_SNAKE_CASE :      Any       =   Path(_A			) / name
      # If the parent module does not exist yet, recursively create it.
      if not dynamic_module_path.parent.exists():
            create_dynamic_module(dynamic_module_path.parent			)
      os.makedirs(_A						,    exist_ok=_A			)
      SCREAMING_SNAKE_CASE :      str       =   dynamic_module_path / """__init__.py"""
      if not init_path.exists():
            init_path.touch()






def 					__lowercase      (     _A			)     ->						Optional[int]:
      with open(_A						,    """r"""						,    encoding="""utf-8"""			) as f:
            SCREAMING_SNAKE_CASE :      Any       =   f.read()

      # Imports of the form `import .xxx`
      SCREAMING_SNAKE_CASE :      List[str]       =   re.findall("""^\s*import\s+\.(\S+)\s*$"""						,    _A						,    flags=re.MULTILINE			)
      # Imports of the form `from .xxx import yyy`
      relative_imports += re.findall("""^\s*from\s+\.(\S+)\s+import"""						,    _A						,    flags=re.MULTILINE			)
      # Unique-ify
      return list(set(_A			)			)






def 					__lowercase      (     _A			)     ->						Optional[Any]:
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   False
      SCREAMING_SNAKE_CASE :      List[Any]       =   [module_file]
      SCREAMING_SNAKE_CASE :      Any       =   []

      # Let's recurse through all relative imports
      while not no_change:
            SCREAMING_SNAKE_CASE :      List[str]       =   []
            for f in files_to_check:
                  new_imports.extend(get_relative_imports(_A			)			)

            SCREAMING_SNAKE_CASE :      List[str]       =   Path(_A			).parent
            SCREAMING_SNAKE_CASE :      int       =   [str(module_path / m			) for m in new_imports]
            SCREAMING_SNAKE_CASE :      int       =   [f for f in new_import_files if f not in all_relative_imports]
            SCREAMING_SNAKE_CASE :      Optional[Any]       =   [F"{f}.py" for f in new_import_files]

            SCREAMING_SNAKE_CASE :      List[Any]       =   len(_A			) == 0
            all_relative_imports.extend(_A			)

      return all_relative_imports






def 					__lowercase      (     _A			)     ->						Union[str, Any]:
      with open(_A						,    """r"""						,    encoding="""utf-8"""			) as f:
            SCREAMING_SNAKE_CASE :      List[Any]       =   f.read()

      # Imports of the form `import xxx`
      SCREAMING_SNAKE_CASE :      Any       =   re.findall("""^\s*import\s+(\S+)\s*$"""						,    _A						,    flags=re.MULTILINE			)
      # Imports of the form `from xxx import yyy`
      imports += re.findall("""^\s*from\s+(\S+)\s+import"""						,    _A						,    flags=re.MULTILINE			)
      # Only keep the top-level module
      SCREAMING_SNAKE_CASE :      List[Any]       =   [imp.split("""."""			)[0] for imp in imports if not imp.startswith("""."""			)]

      # Unique-ify and test we got them all
      SCREAMING_SNAKE_CASE :      Dict       =   list(set(_A			)			)
      SCREAMING_SNAKE_CASE :      Dict       =   []
      for imp in imports:
            try:
                  importlib.import_module(_A			)
            except ImportError:
                  missing_packages.append(_A			)

      if len(_A			) > 0:
            raise ImportError(
                """This modeling file requires the following packages that were not found in your environment: """
                F"{', '.join(_A			)}. Run `pip install {' '.join(_A			)}`"			)

      return get_relative_imports(_A			)






def 					__lowercase      (     _A						,    _A			)     ->						List[str]:
      SCREAMING_SNAKE_CASE :      int       =   module_path.replace(os.path.sep						,    """."""			)
      SCREAMING_SNAKE_CASE :      List[str]       =   importlib.import_module(_A			)

      if class_name is None:
            return find_pipeline_class(_A			)
      return getattr(_A						,    _A			)






def 					__lowercase      (     _A			)     ->						Optional[int]:
      from ..pipelines import DiffusionPipeline

      SCREAMING_SNAKE_CASE :      Tuple       =   dict(inspect.getmembers(_A						,    inspect.isclass			)			)

      SCREAMING_SNAKE_CASE :      Dict       =   None
      for cls_name, cls in cls_members.items():
            if (
                cls_name != DiffusionPipeline.__name__
                and issubclass(cls						,    _A			)
                and cls.__module__.split("""."""			)[0] != "diffusers"
            ):
                  if pipeline_class is not None:
                        raise ValueError(
                            F"Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:"
                            F" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in"
                            F" {loaded_module}."			)
                  SCREAMING_SNAKE_CASE :      List[str]       =   cls

      return pipeline_class






def 					__lowercase      (     _A						,    _A						,    _A = None						,    _A = False						,    _A = False						,    _A = None						,    _A = None						,    _A = None						,    _A = False						,    )     ->						List[str]:
      SCREAMING_SNAKE_CASE :      List[str]       =   str(_A			)

      SCREAMING_SNAKE_CASE :      List[Any]       =   os.path.join(_A						,    _A			)

      if os.path.isfile(_A			):
            SCREAMING_SNAKE_CASE :      Union[str, Any]       =   module_file_or_url
            SCREAMING_SNAKE_CASE :      Dict       =   """local"""
      elif pretrained_model_name_or_path.count("""/"""			) == 0:
            SCREAMING_SNAKE_CASE :      Tuple       =   get_diffusers_versions()
            # cut ".dev0"
            SCREAMING_SNAKE_CASE :      str       =   """v""" + """.""".join(__version__.split("""."""			)[:3]			)

            # retrieve github version that matches
            if revision is None:
                  SCREAMING_SNAKE_CASE :      Any       =   latest_version if latest_version[1:] in available_versions else """main"""
                  logger.info(F"Defaulting to latest_version: {revision}."			)
            elif revision in available_versions:
                  SCREAMING_SNAKE_CASE :      List[Any]       =   F"v{revision}"
            elif revision == "main":
                  SCREAMING_SNAKE_CASE :      Union[str, Any]       =   revision
            else:
                  raise ValueError(
                      F"`custom_revision`: {revision} does not exist. Please make sure to choose one of"
                      F" {', '.join(available_versions + ['main']			)}."			)

            # community pipeline on GitHub
            SCREAMING_SNAKE_CASE :      Tuple       =   COMMUNITY_PIPELINES_URL.format(revision=_A						,    pipeline=_A			)
            try:
                  SCREAMING_SNAKE_CASE :      str       =   cached_download(
                      _A						,    cache_dir=_A						,    force_download=_A						,    proxies=_A						,    resume_download=_A						,    local_files_only=_A						,    use_auth_token=_A						,    )
                  SCREAMING_SNAKE_CASE :      Tuple       =   """git"""
                  SCREAMING_SNAKE_CASE :      Optional[int]       =   pretrained_model_name_or_path + """.py"""
            except EnvironmentError:
                  logger.error(F"Could not locate the {module_file} inside {pretrained_model_name_or_path}."			)
                  raise
      else:
            try:
                  # Load from URL or cache if already cached
                  SCREAMING_SNAKE_CASE :      int       =   hf_hub_download(
                      _A						,    _A						,    cache_dir=_A						,    force_download=_A						,    proxies=_A						,    resume_download=_A						,    local_files_only=_A						,    use_auth_token=_A						,    )
                  SCREAMING_SNAKE_CASE :      Union[str, Any]       =   os.path.join("""local"""						,    """--""".join(pretrained_model_name_or_path.split("""/"""			)			)			)
            except EnvironmentError:
                  logger.error(F"Could not locate the {module_file} inside {pretrained_model_name_or_path}."			)
                  raise

    # Check we have all the requirements in our environment
      SCREAMING_SNAKE_CASE :      int       =   check_imports(_A			)

      # Now we move the module inside our cached dynamic modules.
      SCREAMING_SNAKE_CASE :      List[Any]       =   DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
      create_dynamic_module(_A			)
      SCREAMING_SNAKE_CASE :      Tuple       =   Path(_A			) / full_submodule
      if submodule == "local" or submodule == "git":
            # We always copy local files (we could hash the file to see if there was a change, and give them the name of
            # that hash, to only copy when there is a modification but it seems overkill for now).
            # The only reason we do the copy is to avoid putting too many folders in sys.path.
            shutil.copy(_A						,    submodule_path / module_file			)
            for module_needed in modules_needed:
                  SCREAMING_SNAKE_CASE :      Optional[int]       =   F"{module_needed}.py"
                  shutil.copy(os.path.join(_A						,    _A			)						,    submodule_path / module_needed			)
      else:
            # Get the commit hash
            # TODO: we will get this info in the etag soon, so retrieve it from there and not here.
            if isinstance(_A						,    _A			):
                  SCREAMING_SNAKE_CASE :      Tuple       =   use_auth_token
            elif use_auth_token is True:
                  SCREAMING_SNAKE_CASE :      str       =   HfFolder.get_token()
            else:
                  SCREAMING_SNAKE_CASE :      Optional[int]       =   None

            SCREAMING_SNAKE_CASE :      List[Any]       =   model_info(_A						,    revision=_A						,    token=_A			).sha

            # The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
            # benefit of versioning.
            SCREAMING_SNAKE_CASE :      int       =   submodule_path / commit_hash
            SCREAMING_SNAKE_CASE :      Tuple       =   full_submodule + os.path.sep + commit_hash
            create_dynamic_module(_A			)

            if not (submodule_path / module_file).exists():
                  shutil.copy(_A						,    submodule_path / module_file			)
            # Make sure we also have every file with relative
            for module_needed in modules_needed:
                  if not (submodule_path / module_needed).exists():
                        get_cached_module_file(
                            _A						,    F"{module_needed}.py"						,    cache_dir=_A						,    force_download=_A						,    resume_download=_A						,    proxies=_A						,    use_auth_token=_A						,    revision=_A						,    local_files_only=_A						,    )
      return os.path.join(_A						,    _A			)







def 					__lowercase      (     _A						,    _A						,    _A = None						,    _A = None						,    _A = False						,    _A = False						,    _A = None						,    _A = None						,    _A = None						,    _A = False						,    **_A						,    )     ->						Tuple:
      SCREAMING_SNAKE_CASE :      Dict       =   get_cached_module_file(
          _A						,    _A						,    cache_dir=_A						,    force_download=_A						,    resume_download=_A						,    proxies=_A						,    use_auth_token=_A						,    revision=_A						,    local_files_only=_A						,    )
      return get_class_in_module(_A						,    final_module.replace(""".py"""						,    """"""			)			)




