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

__a  			=					'\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  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




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

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			PriorTransformer       ,							SCREAMING_SNAKE_CASE_  :			CLIPVisionModel       ,							SCREAMING_SNAKE_CASE_  :			CLIPImageProcessor       ,							SCREAMING_SNAKE_CASE_  :			HeunDiscreteScheduler       ,							SCREAMING_SNAKE_CASE_  :			ShapERenderer       ,							)       ->     int:
					super().__init__()

					self.register_modules(
					    prior=SCREAMING_SNAKE_CASE_       ,							image_encoder=SCREAMING_SNAKE_CASE_       ,							image_processor=SCREAMING_SNAKE_CASE_       ,							scheduler=SCREAMING_SNAKE_CASE_       ,							renderer=SCREAMING_SNAKE_CASE_       ,							)
	def 						_lowercase  (       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			Any       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       )       ->     Any:
					if latents is None:
									lowercase_       =					randn_tensor(SCREAMING_SNAKE_CASE_       ,							generator=SCREAMING_SNAKE_CASE_       ,							device=SCREAMING_SNAKE_CASE_       ,							dtype=SCREAMING_SNAKE_CASE_       )
					else:
									if latents.shape != shape:
													raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''       )
									lowercase_       =					latents.to(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					latents * scheduler.init_noise_sigma
					return latents
	def 						_lowercase  (       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			List[str]=0       )       ->     Tuple:
					if is_accelerate_available():
									from accelerate import cpu_offload
					else:
									raise ImportError('''Please install accelerate via `pip install accelerate`'''       )

					lowercase_       =					torch.device(f'''cuda:{gpu_id}'''       )

					lowercase_       =					[self.image_encoder, self.prior]
					for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
													cpu_offload(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
	@property
	def 						_lowercase  (       self  :			Tuple       )       ->     Dict:
					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(SCREAMING_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 						_lowercase  (       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			int       ,							)       ->     Tuple:
					if isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ) and isinstance(image[0]       ,							torch.Tensor       ):
									lowercase_       =					torch.cat(SCREAMING_SNAKE_CASE_       ,							axis=0       ) if image[0].ndim == 4 else torch.stack(SCREAMING_SNAKE_CASE_       ,							axis=0       )

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

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

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

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

					if do_classifier_free_guidance:
									lowercase_       =					torch.zeros_like(SCREAMING_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_       =					torch.cat([negative_image_embeds, image_embeds]       )

					return image_embeds




	@torch.no_grad()
	@replace_example_docstring(SCREAMING_SNAKE_CASE_       )
	def __call__(       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			Union[PIL.Image.Image, List[PIL.Image.Image]]       ,							SCREAMING_SNAKE_CASE_  :			int = 1       ,							SCREAMING_SNAKE_CASE_  :			int = 2_5       ,							SCREAMING_SNAKE_CASE_  :			Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[torch.FloatTensor] = None       ,							SCREAMING_SNAKE_CASE_  :			float = 4.0       ,							SCREAMING_SNAKE_CASE_  :			int = 6_4       ,							SCREAMING_SNAKE_CASE_  :			Optional[str] = "pil"       ,							SCREAMING_SNAKE_CASE_  :			bool = True       ,							)       ->     Optional[Any]:

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

					lowercase_       =					self._execution_device

					lowercase_       =					batch_size * num_images_per_prompt

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

					# prior

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

					lowercase_       =					self.prior.config.num_embeddings
					lowercase_       =					self.prior.config.embedding_dim

					lowercase_       =					self.prepare_latents(
					    (batch_size, num_embeddings * embedding_dim)       ,							image_embeds.dtype       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_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_       =					latents.reshape(latents.shape[0]       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

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

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

									# remove the variance
									lowercase_  ,	lowercase_       =					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_       =					noise_pred.chunk(2       )
													lowercase_       =					noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

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

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

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

									images.append(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					torch.stack(SCREAMING_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_       =					images.cpu().numpy()

					if output_type == "pil":
									lowercase_       =					[self.numpy_to_pil(SCREAMING_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=SCREAMING_SNAKE_CASE_       )






from typing import TYPE_CHECKING

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


__a  			=					{'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['DeiTFeatureExtractor']
						__a  			=					['DeiTImageProcessor']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'DeiTForImageClassification',
						    'DeiTForImageClassificationWithTeacher',
						    'DeiTForMaskedImageModeling',
						    'DeiTModel',
						    'DeiTPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFDeiTForImageClassification',
						    'TFDeiTForImageClassificationWithTeacher',
						    'TFDeiTForMaskedImageModeling',
						    'TFDeiTModel',
						    'TFDeiTPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_deit import DeiTFeatureExtractor
												from .image_processing_deit import DeiTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deit import (
												    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DeiTForImageClassification,
												    DeiTForImageClassificationWithTeacher,
												    DeiTForMaskedImageModeling,
												    DeiTModel,
												    DeiTPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_deit import (
												    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDeiTForImageClassification,
												    TFDeiTForImageClassificationWithTeacher,
												    TFDeiTForMaskedImageModeling,
												    TFDeiTModel,
												    TFDeiTPreTrainedModel,
												)


else:
						import sys

						__a  			=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




