



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

snake_case__				= '\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  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  42





class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     def __init__(					self				,				A_				,				A_				,				A_				,				A_				,				A_				,				) ->				Dict:






           """simple docstring"""
           super().__init__()

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



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_				,				A_				,				A_				,				A_						) ->				List[Any]:






           """simple docstring"""
           if latents is None:
                 _lowerCamelCase			    =				randn_tensor(A_				,				generator=A_				,				device=A_				,				dtype=A_						)
           else:
                 if latents.shape != shape:
                       raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'						)
                 _lowerCamelCase			    =				latents.to(A_						)

           _lowerCamelCase			    =				latents * scheduler.init_noise_sigma
           return latents



     def 		UpperCamelCase_						(					self				,				A_=0						) ->				List[Any]:






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

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

           _lowerCamelCase			    =				[self.image_encoder, self.prior]
           for cpu_offloaded_model in models:
                 if cpu_offloaded_model is not None:
                       cpu_offload(A_				,				A_						)



     @property
     def 		UpperCamelCase_						(					self						) ->				Dict:






           """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(A_				,				'''_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				,				A_				,				A_				,				A_				,				A_				,				) ->				Optional[int]:






           """simple docstring"""
           if isinstance(A_				,				A_						) and isinstance(image[0]				,				torch.Tensor						):
                 _lowerCamelCase			    =				torch.cat(A_				,				axis=0						) if image[0].ndim == 4 else torch.stack(A_				,				axis=0						)

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

           _lowerCamelCase			    =				image.to(dtype=self.image_encoder.dtype				,				device=A_						)

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

           _lowerCamelCase			    =				image_embeds.repeat_interleave(A_				,				dim=0						)

           if do_classifier_free_guidance:
                 _lowerCamelCase			    =				torch.zeros_like(A_						)

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

           return image_embeds





     @torch.no_grad()
     @replace_example_docstring(A_						)
     def __call__(					self				,				A_				,				A_ = 1				,				A_ = 25				,				A_ = None				,				A_ = None				,				A_ = 4.0				,				A_ = 64				,				A_ = "pil"				,				A_ = True				,				) ->				Any:






           """simple docstring"""

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

           _lowerCamelCase			    =				self._execution_device

           _lowerCamelCase			    =				batch_size * num_images_per_prompt

           _lowerCamelCase			    =				guidance_scale > 1.0
           _lowerCamelCase			    =				self._encode_image(A_				,				A_				,				A_				,				A_						)

           # prior

           self.scheduler.set_timesteps(A_				,				device=A_						)
           _lowerCamelCase			    =				self.scheduler.timesteps

           _lowerCamelCase			    =				self.prior.config.num_embeddings
           _lowerCamelCase			    =				self.prior.config.embedding_dim

           _lowerCamelCase			    =				self.prepare_latents(
               (batch_size, num_embeddings * embedding_dim)				,				image_embeds.dtype				,				A_				,				A_				,				A_				,				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			    =				latents.reshape(latents.shape[0]				,				A_				,				A_						)

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

                 _lowerCamelCase			    =				self.prior(
                     A_				,				timestep=A_				,				proj_embedding=A_				,				).predicted_image_embedding

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

                 _lowerCamelCase			    =				self.scheduler.step(
                     A_				,				timestep=A_				,				sample=A_				,				).prev_sample

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

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

                 images.append(A_						)

           _lowerCamelCase			    =				torch.stack(A_						)

           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			    =				images.cpu().numpy()

           if output_type == "pil":
                 _lowerCamelCase			    =				[self.numpy_to_pil(A_						) 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=A_						)
from .data_collator import (
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForSeqaSeq,
    DataCollatorForSOP,
    DataCollatorForTokenClassification,
    DataCollatorForWholeWordMask,
    DataCollatorWithPadding,
    DefaultDataCollator,
    default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
    DataProcessor,
    InputExample,
    InputFeatures,
    SingleSentenceClassificationProcessor,
    SquadExample,
    SquadFeatures,
    SquadVaProcessor,
    SquadVaProcessor,
    glue_convert_examples_to_features,
    glue_output_modes,
    glue_processors,
    glue_tasks_num_labels,
    squad_convert_examples_to_features,
    xnli_output_modes,
    xnli_processors,
    xnli_tasks_num_labels,
)