






"""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


a			:    Tuple				 =		logging.get_logger(__name__)  # pylint: disable=invalid-name

a			:    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    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						__lowerCamelCase    	= 42


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__	,  ):
												'''simple docstring'''
												super().__init__()

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




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												if latents is None:
																		lowercase__    :				Tuple=	randn_tensor(snake_case__	,  generator=snake_case__	,  device=snake_case__	,  dtype=snake_case__      )
												else:
																		if latents.shape != shape:
																								raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''      )
																		lowercase__    :				Dict=	latents.to(snake_case__      )

												lowercase__    :				Any=	latents * scheduler.init_noise_sigma
												return latents




						def 		UpperCAmelCase_	(       self	,  snake_case__=0      ):
												'''simple docstring'''
												if is_accelerate_available():
																		from accelerate import cpu_offload
												else:
																		raise ImportError("Please install accelerate via `pip install accelerate`"      )

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

												lowercase__    :				Union[str, Any]=	[self.image_encoder, self.prior]
												for cpu_offloaded_model in models:
																		if cpu_offloaded_model is not None:
																								cpu_offload(snake_case__	,  snake_case__      )




						@property
						def 		UpperCAmelCase_	(       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(snake_case__	,  "_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 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__	,  ):
												'''simple docstring'''
												if isinstance(snake_case__	,  snake_case__      ) and isinstance(image[0]	,  torch.Tensor      ):
																		lowercase__    :				Optional[Any]=	torch.cat(snake_case__	,  axis=0      ) if image[0].ndim == 4 else torch.stack(snake_case__	,  axis=0      )

												if not isinstance(snake_case__	,  torch.Tensor      ):
																		lowercase__    :				Dict=	self.image_processor(snake_case__	,  return_tensors="pt"      ).pixel_values[0].unsqueeze(0      )

												lowercase__    :				str=	image.to(dtype=self.image_encoder.dtype	,  device=snake_case__      )

												lowercase__    :				List[Any]=	self.image_encoder(snake_case__      )["last_hidden_state"]
												lowercase__    :				int=	image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

												lowercase__    :				Union[str, Any]=	image_embeds.repeat_interleave(snake_case__	,  dim=0      )

												if do_classifier_free_guidance:
																		lowercase__    :				Dict=	torch.zeros_like(snake_case__      )

																		# 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
																		lowercase__    :				Tuple=	torch.cat([negative_image_embeds, image_embeds]      )

												return image_embeds




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

												if isinstance(snake_case__	,  PIL.Image.Image      ):
																		lowercase__    :				Tuple=	1
												elif isinstance(snake_case__	,  torch.Tensor      ):
																		lowercase__    :				Tuple=	image.shape[0]
												elif isinstance(snake_case__	,  snake_case__      ) and isinstance(image[0]	,  (torch.Tensor, PIL.Image.Image)      ):
																		lowercase__    :				int=	len(snake_case__      )
												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(snake_case__      )}'''      )

												lowercase__    :				Dict=	self._execution_device

												lowercase__    :				Union[str, Any]=	batch_size * num_images_per_prompt

												lowercase__    :				int=	guidance_scale > 1.0
												lowercase__    :				Tuple=	self._encode_image(snake_case__	,  snake_case__	,  snake_case__	,  snake_case__      )

												# prior

												self.scheduler.set_timesteps(snake_case__	,  device=snake_case__      )
												lowercase__    :				int=	self.scheduler.timesteps

												lowercase__    :				Any=	self.prior.config.num_embeddings
												lowercase__    :				int=	self.prior.config.embedding_dim

												lowercase__    :				int=	self.prepare_latents(
												    (batch_size, num_embeddings * embedding_dim)	,  image_embeds.dtype	,  snake_case__	,  snake_case__	,  snake_case__	,  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
												lowercase__    :				int=	latents.reshape(latents.shape[0]	,  snake_case__	,  snake_case__      )

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

																		lowercase__    :				Union[str, Any]=	self.prior(
																		    snake_case__	,  timestep=snake_case__	,  proj_embedding=snake_case__	,  ).predicted_image_embedding

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

																		lowercase__    :				Optional[Any]=	self.scheduler.step(
																		    snake_case__	,  timestep=snake_case__	,  sample=snake_case__	,  ).prev_sample

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

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

																		images.append(snake_case__      )

												lowercase__    :				Union[str, Any]=	torch.stack(snake_case__      )

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

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

												if output_type == "pil":
																		lowercase__    :				Union[str, Any]=	[self.numpy_to_pil(snake_case__      ) 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=snake_case__      )














"""simple docstring"""







import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
a			:    Any				 =		"""src/diffusers"""

# Matches is_xxx_available()
a			:    Optional[Any]				 =		re.compile(r"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
a			:    Dict				 =		re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")


a			:    Dict				 =		"""
{0} = None
"""

a			:    List[Any]				 =		"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
"""


a			:    Any				 =		"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""





def 						lowercase__(A						)    ->List[str]:




						"""simple docstring"""



						lowercase__    :				Optional[int]=	_re_backend.findall(A						)
						if len(A						) == 0:
												return None

						return "_and_".join(A						)





def 						lowercase__()    ->int:




						"""simple docstring"""



						with open(os.path.join(A ,		"__init__.py"						) ,		"r" ,		encoding="utf-8" ,		newline="\n"						) as f:
												lowercase__    :				Union[str, Any]=	f.readlines()

						# Get to the point we do the actual imports for type checking
						lowercase__    :				List[Any]=	0
						lowercase__    :				List[Any]=	{}
						# Go through the end of the file
						while line_index < len(A						):
												# If the line contains is_backend_available, we grab all objects associated with the `else` block
												lowercase__    :				Optional[Any]=	find_backend(lines[line_index]						)
												if backend is not None:
																		while not lines[line_index].startswith("else:"						):
																								line_index += 1
																		line_index += 1
																		lowercase__    :				str=	[]
																		# Until we unindent, add backend objects to the list
																		while line_index < len(A						) and len(lines[line_index]						) > 1:
																								lowercase__    :				List[str]=	lines[line_index]
																								lowercase__    :				Any=	_re_single_line_import.search(A						)
																								if single_line_import_search is not None:
																														objects.extend(single_line_import_search.groups()[0].split(", "						)						)
																								elif line.startswith(" " * 8						):
																														objects.append(line[8:-2]						)
																								line_index += 1

																		if len(A						) > 0:
																								lowercase__    :				str=	objects
												else:
																		line_index += 1

						return backend_specific_objects





def 						lowercase__(A ,		A						)    ->List[Any]:




						"""simple docstring"""



						if name.isupper():
												return DUMMY_CONSTANT.format(A						)
						elif name.islower():
												return DUMMY_FUNCTION.format(A ,		A						)
						else:
												return DUMMY_CLASS.format(A ,		A						)





def 						lowercase__(A=None						)    ->Optional[Any]:




						"""simple docstring"""



						if backend_specific_objects is None:
												lowercase__    :				int=	read_init()
						# For special correspondence backend to module name as used in the function requires_modulename
						lowercase__    :				Dict=	{}

						for backend, objects in backend_specific_objects.items():
												lowercase__    :				str=	"[" + ", ".join(f'''"{b}"''' for b in backend.split("_and_"						)						) + "]"
												lowercase__    :				List[str]=	"# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
												dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
												dummy_file += "\n".join([create_dummy_object(A ,		A						) for o in objects]						)
												lowercase__    :				Optional[Any]=	dummy_file

						return dummy_files





def 						lowercase__(A=False						)    ->List[Any]:




						"""simple docstring"""



						lowercase__    :				Tuple=	create_dummy_files()
						# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
						lowercase__    :				int=	{"torch": "pt"}

						# Locate actual dummy modules and read their content.
						lowercase__    :				Dict=	os.path.join(A ,		"utils"						)
						lowercase__    :				str=	{
						    backend: os.path.join(A ,		f'''dummy_{short_names.get(A ,		A						)}_objects.py'''						)
						    for backend in dummy_files.keys()
						}

						lowercase__    :				Union[str, Any]=	{}
						for backend, file_path in dummy_file_paths.items():
												if os.path.isfile(A						):
																		with open(A ,		"r" ,		encoding="utf-8" ,		newline="\n"						) as f:
																								lowercase__    :				List[Any]=	f.read()
												else:
																		lowercase__    :				Tuple=	""

						for backend in dummy_files.keys():
												if dummy_files[backend] != actual_dummies[backend]:
																		if overwrite:
																								print(
																								    f'''Updating diffusers.utils.dummy_{short_names.get(A ,		A						)}_objects.py as the main '''
																								    "__init__ has new objects."						)
																								with open(dummy_file_paths[backend] ,		"w" ,		encoding="utf-8" ,		newline="\n"						) as f:
																														f.write(dummy_files[backend]						)
																		else:
																								raise ValueError(
																								    "The main __init__ has objects that are not present in "
																								    f'''diffusers.utils.dummy_{short_names.get(A ,		A						)}_objects.py. Run `make fix-copies` '''
																								    "to fix this."						)


if __name__ == "__main__":
			a			:    Optional[Any]				 =		argparse.ArgumentParser()
			parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
			a			:    Tuple				 =		parser.parse_args()

			check_dummies(args.fix_and_overwrite)






