





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     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      42

class     snake_case_		(   a							):






 '''simple docstring'''





 def __init__(  self,		A_,		A_,		A_,		A_,		A_,		)	->       List[str]:
    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_		)	->       str:
    if latents is None:
       UpperCAmelCase__						=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__						=latents.to(A_		)

    UpperCAmelCase__						=latents * scheduler.init_noise_sigma
    return latents

 def  __UpperCAmelCase						(  self,		A_=0		)	->       List[str]:
    if is_accelerate_available():
       from accelerate import cpu_offload
    else:
       raise ImportError("Please install accelerate via `pip install accelerate`"		)

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

    UpperCAmelCase__						=[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:
    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_,		)	->       Tuple:
    if isinstance(A_,		A_		) and isinstance(image[0],		torch.Tensor		):
       UpperCAmelCase__						=torch.cat(A_,		axis=0		) if image[0].ndim == 4 else torch.stack(A_,		axis=0		)

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

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

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

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

    if do_classifier_free_guidance:
       UpperCAmelCase__						=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__						=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,		)	->       Tuple:

    if isinstance(A_,		PIL.Image.Image		):
       UpperCAmelCase__						=1
    elif isinstance(A_,		torch.Tensor		):
       UpperCAmelCase__						=image.shape[0]
    elif isinstance(A_,		A_		) and isinstance(image[0],		(torch.Tensor, PIL.Image.Image)		):
       UpperCAmelCase__						=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__						=self._execution_device

    UpperCAmelCase__						=batch_size * num_images_per_prompt

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

    # prior

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

    UpperCAmelCase__						=self.prior.config.num_embeddings
    UpperCAmelCase__						=self.prior.config.embedding_dim

    UpperCAmelCase__						=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__						=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__						=torch.cat([latents] * 2		) if do_classifier_free_guidance else latents
       UpperCAmelCase__						=self.scheduler.scale_model_input(A_,		A_		)

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

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

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

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

    UpperCAmelCase__						=[]
    for i, latent in enumerate(A_		):
       print()
       UpperCAmelCase__						=self.renderer.decode(
           latent[None, :],		A_,		size=A_,		ray_batch_size=4096,		n_coarse_samples=64,		n_fine_samples=128,		)

       images.append(A_		)

    UpperCAmelCase__						=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__						=images.cpu().numpy()

    if output_type == "pil":
       UpperCAmelCase__						=[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 typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'EncodecFeatureExtractor'
 __UpperCamelCase       =      ('T5Tokenizer', 'T5TokenizerFast')

 def __init__(  self,		A_,		A_		)	->       Optional[int]:
    super().__init__(A_,		A_		)
    UpperCAmelCase__						=self.feature_extractor
    UpperCAmelCase__						=False

 def  __UpperCAmelCase						(  self,		A_=None,		A_=None,		A_=True		)	->       Union[str, Any]:
    return self.tokenizer.get_decoder_prompt_ids(task=A_,		language=A_,		no_timestamps=A_		)

 def __call__(  self,		*A_,		**A_		)	->       Any:
    # For backward compatibility
    if self._in_target_context_manager:
       return self.current_processor(*A_,		**A_		)

    UpperCAmelCase__						=kwargs.pop("audio",		A_		)
    UpperCAmelCase__						=kwargs.pop("sampling_rate",		A_		)
    UpperCAmelCase__						=kwargs.pop("text",		A_		)
    if len(A_		) > 0:
       UpperCAmelCase__						=args[0]
       UpperCAmelCase__						=args[1:]

    if audio is None and text is None:
       raise ValueError("You need to specify either an `audio` or `text` input to process."		)

    if text is not None:
       UpperCAmelCase__						=self.tokenizer(A_,		**A_		)

    if audio is not None:
       UpperCAmelCase__						=self.feature_extractor(A_,		*A_,		sampling_rate=A_,		**A_		)

    if audio is None:
       return inputs

    elif text is None:
       return audio_inputs

    else:
       UpperCAmelCase__						=audio_inputs["input_values"]
       if "padding_mask" in audio_inputs:
          UpperCAmelCase__						=audio_inputs["padding_mask"]
       return inputs

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       Dict:
    UpperCAmelCase__						=kwargs.pop("audio",		A_		)
    UpperCAmelCase__						=kwargs.pop("padding_mask",		A_		)

    if len(A_		) > 0:
       UpperCAmelCase__						=args[0]
       UpperCAmelCase__						=args[1:]

    if audio_values is not None:
       return self._decode_audio(A_,		padding_mask=A_		)
    else:
       return self.tokenizer.batch_decode(*A_,		**A_		)

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       int:
    return self.tokenizer.decode(*A_,		**A_		)






 def  __UpperCAmelCase						(  self,		A_,		A_ = None		)	->       List[np.ndarray]:
    UpperCAmelCase__						=to_numpy(A_		)
    UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__						=audio_values.shape

    if padding_mask is None:
       return list(A_		)

    UpperCAmelCase__						=to_numpy(A_		)

    # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
    # token (so that the generated audio values are **not** treated as padded tokens)
    UpperCAmelCase__						=seq_len - padding_mask.shape[-1]
    UpperCAmelCase__						=1 - self.feature_extractor.padding_value
    UpperCAmelCase__						=np.pad(A_,		((0, 0), (0, difference)),		"constant",		constant_values=A_		)

    UpperCAmelCase__						=audio_values.tolist()
    for i in range(A_		):
       UpperCAmelCase__						=np.asarray(audio_values[i]		)[
           padding_mask[i][None, :] != self.feature_extractor.padding_value
       ]
       UpperCAmelCase__						=sliced_audio.reshape(A_,		-1		)

    return audio_values



