


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


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

snake_case							:	Optional[int]					      =				'''
    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 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   Union[PIL.Image.Image, np.ndarray]







class 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :Optional[int]      ,__snake_case       :PriorTransformer      ,__snake_case       :CLIPVisionModel      ,__snake_case       :CLIPImageProcessor      ,__snake_case       :HeunDiscreteScheduler      ,__snake_case       :ShapERenderer      ,) ->   str:
												super().__init__()

												self.register_modules(
												    prior=__snake_case      ,image_encoder=__snake_case      ,image_processor=__snake_case      ,scheduler=__snake_case      ,renderer=__snake_case      ,)
					def 			lowerCamelCase__(				self       :Union[str, Any]      ,__snake_case       :List[str]      ,__snake_case       :List[Any]      ,__snake_case       :List[str]      ,__snake_case       :List[Any]      ,__snake_case       :int      ,__snake_case       :List[str]				) ->   List[Any]:
												if latents is None:
																			a__					=					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}'				)
																			a__					=					latents.to(__snake_case				)

												a__					=					latents * scheduler.init_noise_sigma
												return latents
					def 			lowerCamelCase__(				self       :Optional[Any]      ,__snake_case       :Any=0				) ->   List[str]:
												if is_accelerate_available():
																			from accelerate import cpu_offload
												else:
																			raise ImportError('Please install accelerate via `pip install accelerate`'				)

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

												a__					=					[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 			lowerCamelCase__(				self       :Dict				) ->   Optional[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(__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 			lowerCamelCase__(				self       :Tuple      ,__snake_case       :Any      ,__snake_case       :List[Any]      ,__snake_case       :Dict      ,__snake_case       :Union[str, Any]      ,) ->   Union[str, Any]:
												if isinstance(__snake_case      ,__snake_case				) and isinstance(image[0]      ,torch.Tensor				):
																			a__					=					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				):
																			a__					=					self.image_processor(__snake_case      ,return_tensors='pt'				).pixel_values[0].unsqueeze(0				)

												a__					=					image.to(dtype=self.image_encoder.dtype      ,device=__snake_case				)

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

												a__					=					image_embeds.repeat_interleave(__snake_case      ,dim=0				)

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

												return image_embeds



					@torch.no_grad()
					@replace_example_docstring(__snake_case				)
					def __call__(				self       :List[str]      ,__snake_case       :Union[PIL.Image.Image, List[PIL.Image.Image]]      ,__snake_case       :int = 1      ,__snake_case       :int = 25      ,__snake_case       :Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,__snake_case       :Optional[torch.FloatTensor] = None      ,__snake_case       :float = 4.0      ,__snake_case       :int = 64      ,__snake_case       :Optional[str] = "pil"      ,__snake_case       :bool = True      ,) ->   Any:

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

												a__					=					self._execution_device

												a__					=					batch_size * num_images_per_prompt

												a__					=					guidance_scale > 1.0
												a__					=					self._encode_image(__snake_case      ,__snake_case      ,__snake_case      ,__snake_case				)

												# prior

												self.scheduler.set_timesteps(__snake_case      ,device=__snake_case				)
												a__					=					self.scheduler.timesteps

												a__					=					self.prior.config.num_embeddings
												a__					=					self.prior.config.embedding_dim

												a__					=					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
												a__					=					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
																			a__					=					torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
																			a__					=					self.scheduler.scale_model_input(__snake_case      ,__snake_case				)

																			a__					=					self.prior(
																			    __snake_case      ,timestep=__snake_case      ,proj_embedding=__snake_case      ,).predicted_image_embedding

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

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

																			a__					=					self.scheduler.step(
																			    __snake_case      ,timestep=__snake_case      ,sample=__snake_case      ,).prev_sample

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

												a__					=					[]
												for i, latent in enumerate(__snake_case				):
																			print()
																			a__					=					self.renderer.decode(
																			    latent[None, :]      ,__snake_case      ,size=__snake_case      ,ray_batch_size=40_96      ,n_coarse_samples=64      ,n_fine_samples=1_28      ,)

																			images.append(__snake_case				)

												a__					=					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}'				)

												a__					=					images.cpu().numpy()

												if output_type == "pil":
																			a__					=					[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				)








from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *




