"""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


lowerCAmelCase__						:						Optional[int]		  =			logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCAmelCase__						:						Optional[int]		  =			'\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        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				42






class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					def __init__(       self     :			Dict					,lowerCamelCase__     :			PriorTransformer					,lowerCamelCase__     :			CLIPVisionModel					,lowerCamelCase__     :			CLIPImageProcessor					,lowerCamelCase__     :			HeunDiscreteScheduler					,lowerCamelCase__     :			ShapERenderer					,):
									super().__init__()

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







					def 	__lowerCAmelCase		(       self     :			str					,lowerCamelCase__     :			Dict					,lowerCamelCase__     :			int					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			Tuple					,lowerCamelCase__     :			Optional[int]    ):
									if latents is None:
													UpperCAmelCase__    =							randn_tensor(lowerCamelCase__					,generator=lowerCamelCase__					,device=lowerCamelCase__					,dtype=lowerCamelCase__    )
									else:
													if latents.shape != shape:
																	raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''    )
													UpperCAmelCase__    =							latents.to(lowerCamelCase__    )

									UpperCAmelCase__    =							latents * scheduler.init_noise_sigma
									return latents







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			str=0    ):
									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(lowerCamelCase__					,lowerCamelCase__    )







					@property
					def 	__lowerCAmelCase		(       self     :			str    ):
									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(lowerCamelCase__					,'_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     :			Tuple					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Tuple					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			List[Any]					,):
									if isinstance(lowerCamelCase__					,lowerCamelCase__    ) and isinstance(image[0]					,torch.Tensor    ):
													UpperCAmelCase__    =							torch.cat(lowerCamelCase__					,axis=0    ) if image[0].ndim == 4 else torch.stack(lowerCamelCase__					,axis=0    )

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

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

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

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

									if do_classifier_free_guidance:
													UpperCAmelCase__    =							torch.zeros_like(lowerCamelCase__    )

													# 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(lowerCamelCase__    )
					def __call__(       self     :			str					,lowerCamelCase__     :			Union[PIL.Image.Image, List[PIL.Image.Image]]					,lowerCamelCase__     :			int = 1					,lowerCamelCase__     :			int = 25					,lowerCamelCase__     :			Optional[Union[torch.Generator, List[torch.Generator]]] = None					,lowerCamelCase__     :			Optional[torch.FloatTensor] = None					,lowerCamelCase__     :			float = 4.0					,lowerCamelCase__     :			int = 64					,lowerCamelCase__     :			Optional[str] = "pil"					,lowerCamelCase__     :			bool = True					,):

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

									UpperCAmelCase__    =							self._execution_device

									UpperCAmelCase__    =							batch_size * num_images_per_prompt

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

									# prior

									self.scheduler.set_timesteps(lowerCamelCase__					,device=lowerCamelCase__    )
									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					,lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,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]					,lowerCamelCase__					,lowerCamelCase__    )

									for i, t in enumerate(self.progress_bar(lowerCamelCase__    )    ):
													# 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(lowerCamelCase__					,lowerCamelCase__    )

													UpperCAmelCase__    =							self.prior(
													    lowerCamelCase__					,timestep=lowerCamelCase__					,proj_embedding=lowerCamelCase__					,).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(
													    lowerCamelCase__					,timestep=lowerCamelCase__					,sample=lowerCamelCase__					,).prev_sample

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

									UpperCAmelCase__    =							[]
									for i, latent in enumerate(lowerCamelCase__    ):
													print()
													UpperCAmelCase__    =							self.renderer.decode(
													    latent[None, :]					,lowerCamelCase__					,size=lowerCamelCase__					,ray_batch_size=4_096					,n_coarse_samples=64					,n_fine_samples=128					,)

													images.append(lowerCamelCase__    )

									UpperCAmelCase__    =							torch.stack(lowerCamelCase__    )

									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(lowerCamelCase__    ) 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=lowerCamelCase__    )




"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCAmelCase__						:						int		  =			{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__						:						Union[str, Any]		  =			[
						    'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TapasForMaskedLM',
						    'TapasForQuestionAnswering',
						    'TapasForSequenceClassification',
						    'TapasModel',
						    'TapasPreTrainedModel',
						    'load_tf_weights_in_tapas',
						]
try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__						:						Optional[Any]		  =			[
						    'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFTapasForMaskedLM',
						    'TFTapasForQuestionAnswering',
						    'TFTapasForSequenceClassification',
						    'TFTapasModel',
						    'TFTapasPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
						from .tokenization_tapas import TapasTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tapas import (
												    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TapasForMaskedLM,
												    TapasForQuestionAnswering,
												    TapasForSequenceClassification,
												    TapasModel,
												    TapasPreTrainedModel,
												    load_tf_weights_in_tapas,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_tapas import (
												    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFTapasForMaskedLM,
												    TFTapasForQuestionAnswering,
												    TFTapasForSequenceClassification,
												    TFTapasModel,
												    TFTapasPreTrainedModel,
												)


else:
						import sys

						lowerCAmelCase__						:						Dict		  =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



