


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

_UpperCAmelCase	:     Union[str, 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  snake_case__(							_lowerCAmelCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     List[Any]    =							42







class  snake_case__(							_lowerCAmelCase							):







						'''simple docstring'''




						def __init__( self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  )    ->			str:
										super().__init__()

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




						def   lowercase_       ( self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase							)    ->			Dict:




										if latents is None:
														lowerCAmelCase_       :					Any			 = randn_tensor(__lowercase      ,  generator=__lowercase      ,  device=__lowercase      ,  dtype=__lowercase							)
										else:
														if latents.shape != shape:
																		raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""							)
														lowerCAmelCase_       :					Dict			 = latents.to(__lowercase							)

										lowerCAmelCase_       :					str			 = latents * scheduler.init_noise_sigma
										return latents




						def   lowercase_       ( self      ,  __lowercase=0							)    ->			List[str]:




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

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

										lowerCAmelCase_       :					Optional[Any]			 = [self.image_encoder, self.prior]
										for cpu_offloaded_model in models:
														if cpu_offloaded_model is not None:
																		cpu_offload(__lowercase      ,  __lowercase							)




						@property
						def   lowercase_       ( self							)    ->			Any:




										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(__lowercase      ,  '''_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      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  )    ->			List[Any]:




										if isinstance(__lowercase      ,  __lowercase							) and isinstance(image[0]      ,  torch.Tensor							):
														lowerCAmelCase_       :					int			 = torch.cat(__lowercase      ,  axis=0							) if image[0].ndim == 4 else torch.stack(__lowercase      ,  axis=0							)

										if not isinstance(__lowercase      ,  torch.Tensor							):
														lowerCAmelCase_       :					Union[str, Any]			 = self.image_processor(__lowercase      ,  return_tensors='''pt'''							).pixel_values[0].unsqueeze(0							)

										lowerCAmelCase_       :					Dict			 = image.to(dtype=self.image_encoder.dtype      ,  device=__lowercase							)

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

										lowerCAmelCase_       :					Tuple			 = image_embeds.repeat_interleave(__lowercase      ,  dim=0							)

										if do_classifier_free_guidance:
														lowerCAmelCase_       :					Tuple			 = torch.zeros_like(__lowercase							)

														# 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
														lowerCAmelCase_       :					Any			 = torch.cat([negative_image_embeds, image_embeds]							)

										return image_embeds




						@torch.no_grad()
						@replace_example_docstring(__lowercase							)
						def __call__( self      ,  __lowercase      ,  __lowercase = 1      ,  __lowercase = 2_5      ,  __lowercase = None      ,  __lowercase = None      ,  __lowercase = 4.0      ,  __lowercase = 6_4      ,  __lowercase = "pil"      ,  __lowercase = True      ,  )    ->			Dict:




										if isinstance(__lowercase      ,  PIL.Image.Image							):
														lowerCAmelCase_       :					List[str]			 = 1
										elif isinstance(__lowercase      ,  torch.Tensor							):
														lowerCAmelCase_       :					Union[str, Any]			 = image.shape[0]
										elif isinstance(__lowercase      ,  __lowercase							) and isinstance(image[0]      ,  (torch.Tensor, PIL.Image.Image)							):
														lowerCAmelCase_       :					List[str]			 = len(__lowercase							)
										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(__lowercase							)}"""							)

										lowerCAmelCase_       :					List[str]			 = self._execution_device

										lowerCAmelCase_       :					Optional[Any]			 = batch_size * num_images_per_prompt

										lowerCAmelCase_       :					str			 = guidance_scale > 1.0
										lowerCAmelCase_       :					List[Any]			 = self._encode_image(__lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase							)

										# prior

										self.scheduler.set_timesteps(__lowercase      ,  device=__lowercase							)
										lowerCAmelCase_       :					Dict			 = self.scheduler.timesteps

										lowerCAmelCase_       :					Tuple			 = self.prior.config.num_embeddings
										lowerCAmelCase_       :					Dict			 = self.prior.config.embedding_dim

										lowerCAmelCase_       :					Union[str, Any]			 = self.prepare_latents(
										    (batch_size, num_embeddings * embedding_dim)      ,  image_embeds.dtype      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  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
										lowerCAmelCase_       :					List[Any]			 = latents.reshape(latents.shape[0]      ,  __lowercase      ,  __lowercase							)

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

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

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

														lowerCAmelCase_       :					int			 = self.scheduler.step(
														    __lowercase      ,  timestep=__lowercase      ,  sample=__lowercase      ,  ).prev_sample

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

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

														images.append(__lowercase							)

										lowerCAmelCase_       :					str			 = torch.stack(__lowercase							)

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

										lowerCAmelCase_       :					Dict			 = images.cpu().numpy()

										if output_type == "pil":
														lowerCAmelCase_       :					int			 = [self.numpy_to_pil(__lowercase							) 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=__lowercase							)






'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase					:     Optional[Any]												=logging.get_logger(__name__)

lowerCAmelCase					:     Tuple												={
    '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}
class       a_   (				_lowerCAmelCase							):
     __A      =			"vit_mae"





     def __init__( self		:					Any	,      lowercase		:					int=768	,      lowercase		:					Tuple=12	,      lowercase		:					str=12	,      lowercase		:					Optional[Any]=3_072	,      lowercase		:					List[Any]="gelu"	,      lowercase		:					Tuple=0.0	,      lowercase		:					Union[str, Any]=0.0	,      lowercase		:					str=0.02	,      lowercase		:					Optional[int]=1e-1_2	,      lowercase		:					List[Any]=224	,      lowercase		:					str=16	,      lowercase		:					List[str]=3	,      lowercase		:					Optional[Any]=True	,      lowercase		:					int=16	,      lowercase		:					Optional[Any]=512	,      lowercase		:					Optional[Any]=8	,      lowercase		:					Optional[Any]=2_048	,      lowercase		:					List[str]=0.75	,      lowercase		:					str=False	,      **lowercase		:					Union[str, Any]	,      ):





        """simple docstring"""




        super().__init__(**lowercase						)

        lowercase_			:Any    			= hidden_size
        lowercase_			:Optional[Any]    			= num_hidden_layers
        lowercase_			:Optional[Any]    			= num_attention_heads
        lowercase_			:int    			= intermediate_size
        lowercase_			:Optional[int]    			= hidden_act
        lowercase_			:str    			= hidden_dropout_prob
        lowercase_			:Optional[Any]    			= attention_probs_dropout_prob
        lowercase_			:str    			= initializer_range
        lowercase_			:Optional[int]    			= layer_norm_eps
        lowercase_			:str    			= image_size
        lowercase_			:Union[str, Any]    			= patch_size
        lowercase_			:Dict    			= num_channels
        lowercase_			:Any    			= qkv_bias
        lowercase_			:Optional[int]    			= decoder_num_attention_heads
        lowercase_			:Optional[Any]    			= decoder_hidden_size
        lowercase_			:Union[str, Any]    			= decoder_num_hidden_layers
        lowercase_			:List[Any]    			= decoder_intermediate_size
        lowercase_			:Optional[Any]    			= mask_ratio
        lowercase_			:Optional[Any]    			= norm_pix_loss


