






'''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


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

UpperCamelCase__ =			'\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						(		snake_case			):
    __lowerCamelCase:   Union[PIL.Image.Image, np.ndarray]


class        _UpperCAmelCase						(		snake_case			):




    def __init__(			self  :       Union[str, Any]  ,     a  :       PriorTransformer  ,     a  :       CLIPVisionModel  ,     a  :       CLIPImageProcessor  ,     a  :       HeunDiscreteScheduler  ,     a  :       ShapERenderer  ,     ):






       '''simple docstring'''






       super().__init__()

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




    def 				lowerCAmelCase__				(			self  :       List[str]  ,     a  :       List[Any]  ,     a  :       List[str]  ,     a  :       str  ,     a  :       str  ,     a  :       Tuple  ,     a  :       Tuple			):






       '''simple docstring'''






       if latents is None:
          lowercase_	:    Any           =     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}"""			)
          lowercase_	:    Optional[int]           =     latents.to(a			)

       lowercase_	:    Optional[int]           =     latents * scheduler.init_noise_sigma
       return latents




    def 				lowerCAmelCase__				(			self  :       Dict  ,     a  :       Union[str, Any]=0			):






       '''simple docstring'''






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

       lowercase_	:    Optional[int]           =     torch.device(f"""cuda:{gpu_id}"""			)

       lowercase_	:    Union[str, Any]           =     [self.image_encoder, self.prior]
       for cpu_offloaded_model in models:
          if cpu_offloaded_model is not None:
             cpu_offload(a  ,     a			)




    @property
    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''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 				lowerCAmelCase__				(			self  :       Optional[Any]  ,     a  :       List[str]  ,     a  :       List[Any]  ,     a  :       Any  ,     a  :       Optional[int]  ,     ):






       '''simple docstring'''






       if isinstance(a  ,     a			) and isinstance(image[0]  ,     torch.Tensor			):
          lowercase_	:    List[str]           =     torch.cat(a  ,     axis=0			) if image[0].ndim == 4 else torch.stack(a  ,     axis=0			)

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

       lowercase_	:    Optional[Any]           =     image.to(dtype=self.image_encoder.dtype  ,     device=a			)

       lowercase_	:    int           =     self.image_encoder(a			)["last_hidden_state"]
       lowercase_	:    Tuple           =     image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

       lowercase_	:    Dict           =     image_embeds.repeat_interleave(a  ,     dim=0			)

       if do_classifier_free_guidance:
          lowercase_	:    Union[str, Any]           =     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
          lowercase_	:    List[str]           =     torch.cat([negative_image_embeds, image_embeds]			)

       return image_embeds





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






       '''simple docstring'''






       if isinstance(a  ,     PIL.Image.Image			):
          lowercase_	:    Optional[Any]           =     1
       elif isinstance(a  ,     torch.Tensor			):
          lowercase_	:    str           =     image.shape[0]
       elif isinstance(a  ,     a			) and isinstance(image[0]  ,     (torch.Tensor, PIL.Image.Image)			):
          lowercase_	:    Tuple           =     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			)}"""			)

       lowercase_	:    Union[str, Any]           =     self._execution_device

       lowercase_	:    Dict           =     batch_size * num_images_per_prompt

       lowercase_	:    Dict           =     guidance_scale > 1.0
       lowercase_	:    Optional[Any]           =     self._encode_image(a  ,     a  ,     a  ,     a			)

       # prior

       self.scheduler.set_timesteps(a  ,     device=a			)
       lowercase_	:    Any           =     self.scheduler.timesteps

       lowercase_	:    List[str]           =     self.prior.config.num_embeddings
       lowercase_	:    Optional[int]           =     self.prior.config.embedding_dim

       lowercase_	:    str           =     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
       lowercase_	:    Dict           =     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
          lowercase_	:    Dict           =     torch.cat([latents] * 2			) if do_classifier_free_guidance else latents
          lowercase_	:    Any           =     self.scheduler.scale_model_input(a  ,     a			)

          lowercase_	:    List[Any]           =     self.prior(
              a  ,     timestep=a  ,     proj_embedding=a  ,     ).predicted_image_embedding

          # remove the variance
          lowercase_    ,		lowercase_	:    Optional[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:
             lowercase_    ,		lowercase_	:    Dict           =     noise_pred.chunk(2			)
             lowercase_	:    List[str]           =     noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

          lowercase_	:    List[Any]           =     self.scheduler.step(
              a  ,     timestep=a  ,     sample=a  ,     ).prev_sample

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

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

          images.append(a			)

       lowercase_	:    int           =     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}"""			)

       lowercase_	:    str           =     images.cpu().numpy()

       if output_type == "pil":
          lowercase_	:    Optional[int]           =     [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			)












'''simple docstring'''
import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


UpperCamelCase__ =			namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   return (abs(source - target							) / target) < 0.01





@pytest.mark.integration
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Optional[int]           =     _TestCommandArgs(dataset=_UpperCamelCase						,		all_configs=_UpperCamelCase						,		save_infos=_UpperCamelCase							)
   lowercase_	:    int           =     TestCommand(*_UpperCamelCase							)
   test_command.run()
   lowercase_	:    List[str]           =     os.path.join(_UpperCamelCase						,		"README.md"							)
   assert os.path.exists(_UpperCamelCase							)
   lowercase_	:    Any           =     DatasetInfosDict.from_directory(_UpperCamelCase							)
   lowercase_	:    Optional[int]           =     DatasetInfosDict(
       {
           "default": DatasetInfo(
               features=Features(
                   {
                       "tokens": Sequence(Value("string"							)							),
                       "ner_tags": Sequence(
                           ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]							)							),
                       "langs": Sequence(Value("string"							)							),
                       "spans": Sequence(Value("string"							)							),
                   }							)						,		splits=[
                   {
                       "name": "train",
                       "num_bytes": 235_1563,
                       "num_examples": 1_0000,
                   },
                   {
                       "name": "validation",
                       "num_bytes": 23_8418,
                       "num_examples": 1000,
                   },
               ]						,		download_size=394_0680						,		dataset_size=258_9981						,		)
       }							)
   assert dataset_infos.keys() == expected_dataset_infos.keys()
   for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
      lowercase_    ,		lowercase_	:    Optional[int]           =     getattr(dataset_infos["default"]						,		_UpperCamelCase							), getattr(expected_dataset_infos["default"]						,		_UpperCamelCase							)
      if key == "num_bytes":
         assert is_apercent_close(_UpperCamelCase						,		_UpperCamelCase							)
      elif key == "splits":
         assert list(_UpperCamelCase							) == list(_UpperCamelCase							)
         for split in result:
            assert result[split].name == expected[split].name
            assert result[split].num_examples == expected[split].num_examples
            assert is_apercent_close(result[split].num_bytes						,		expected[split].num_bytes							)
      else:
         result == expected




