



'''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


A_							: List[str]						 =	logging.get_logger(__name__)  # pylint: disable=invalid-name

A_							: Optional[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 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  42







class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 def __init__(							self  ,a_  ,a_  ,a_  ,a_  ,a_  ,)		->   int:
        super().__init__()

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







 def _snake_case (							self  ,a_  ,a_  ,a_  ,a_  ,a_  ,a_							)		->   Optional[Any]:
        if latents is None:
               _UpperCAmelCase  :						List[str]			     = 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}'''							)
               _UpperCAmelCase  :						Tuple			     = latents.to(a_							)

        _UpperCAmelCase  :						Any			     = latents * scheduler.init_noise_sigma
        return latents







 def _snake_case (							self  ,a_=0							)		->   Optional[int]:
        if is_accelerate_available():
               from accelerate import cpu_offload
        else:
               raise ImportError("""Please install accelerate via `pip install accelerate`"""							)

        _UpperCAmelCase  :						Optional[Any]			     = torch.device(f'''cuda:{gpu_id}'''							)

        _UpperCAmelCase  :						Optional[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 _snake_case (							self							)		->   Tuple:
        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 _snake_case (							self  ,a_  ,a_  ,a_  ,a_  ,)		->   Optional[int]:
        if isinstance(a_  ,a_							) and isinstance(image[0]  ,torch.Tensor							):
               _UpperCAmelCase  :						Dict			     = torch.cat(a_  ,axis=0							) if image[0].ndim == 4 else torch.stack(a_  ,axis=0							)

        if not isinstance(a_  ,torch.Tensor							):
               _UpperCAmelCase  :						List[str]			     = self.image_processor(a_  ,return_tensors="""pt"""							).pixel_values[0].unsqueeze(0							)

        _UpperCAmelCase  :						Tuple			     = image.to(dtype=self.image_encoder.dtype  ,device=a_							)

        _UpperCAmelCase  :						Dict			     = self.image_encoder(a_							)["""last_hidden_state"""]
        _UpperCAmelCase  :						List[str]			     = image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

        _UpperCAmelCase  :						Tuple			     = image_embeds.repeat_interleave(a_  ,dim=0							)

        if do_classifier_free_guidance:
               _UpperCAmelCase  :						Optional[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
               _UpperCAmelCase  :						Any			     = 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  ,)		->   int:

        if isinstance(a_  ,PIL.Image.Image							):
               _UpperCAmelCase  :						List[str]			     = 1
        elif isinstance(a_  ,torch.Tensor							):
               _UpperCAmelCase  :						int			     = image.shape[0]
        elif isinstance(a_  ,a_							) and isinstance(image[0]  ,(torch.Tensor, PIL.Image.Image)							):
               _UpperCAmelCase  :						Optional[int]			     = 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_							)}'''							)

        _UpperCAmelCase  :						Tuple			     = self._execution_device

        _UpperCAmelCase  :						List[Any]			     = batch_size * num_images_per_prompt

        _UpperCAmelCase  :						List[Any]			     = guidance_scale > 1.0
        _UpperCAmelCase  :						Optional[Any]			     = self._encode_image(a_  ,a_  ,a_  ,a_							)

        # prior

        self.scheduler.set_timesteps(a_  ,device=a_							)
        _UpperCAmelCase  :						Union[str, Any]			     = self.scheduler.timesteps

        _UpperCAmelCase  :						List[Any]			     = self.prior.config.num_embeddings
        _UpperCAmelCase  :						Any			     = self.prior.config.embedding_dim

        _UpperCAmelCase  :						Optional[int]			     = 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
        _UpperCAmelCase  :						List[str]			     = 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
               _UpperCAmelCase  :						Optional[Any]			     = torch.cat([latents] * 2							) if do_classifier_free_guidance else latents
               _UpperCAmelCase  :						Optional[int]			     = self.scheduler.scale_model_input(a_  ,a_							)

               _UpperCAmelCase  :						int			     = self.prior(
                   a_  ,timestep=a_  ,proj_embedding=a_  ,).predicted_image_embedding

               # remove the variance
               _UpperCAmelCase     ,_UpperCAmelCase  :						List[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:
                      _UpperCAmelCase     ,_UpperCAmelCase  :						Any			     = noise_pred.chunk(2							)
                      _UpperCAmelCase  :						Union[str, Any]			     = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

               _UpperCAmelCase  :						Optional[int]			     = self.scheduler.step(
                   a_  ,timestep=a_  ,sample=a_  ,).prev_sample

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

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

               images.append(a_							)

        _UpperCAmelCase  :						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}'''							)

        _UpperCAmelCase  :						Union[str, Any]			     = images.cpu().numpy()

        if output_type == "pil":
               _UpperCAmelCase  :						Tuple			     = [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'''






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import TFCamembertModel



@require_tf
@require_sentencepiece
@require_tokenizers
class 							lowercase       (  unittest.TestCase				):


 """simple docstring"""


 @slow
 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Dict			     = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""							)

        _UpperCAmelCase  :						Optional[int]			     = tf.convert_to_tensor(
            [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]  ,dtype=tf.intaa  ,)  # J'aime le camembert !"

        _UpperCAmelCase  :						Dict			     = model(a_							)["""last_hidden_state"""]
        _UpperCAmelCase  :						Dict			     = tf.TensorShape((1, 10, 768)							)
        self.assertEqual(output.shape  ,a_							)
        # compare the actual values for a slice.
        _UpperCAmelCase  :						Tuple			     = tf.convert_to_tensor(
            [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]  ,dtype=tf.floataa  ,)
        # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
        # camembert.eval()
        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

        self.assertTrue(np.allclose(output[:, :3, :3].numpy()  ,expected_slice.numpy()  ,atol=1E-4							)							)



