

from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_torch_available():
  from ..models.auto.modeling_auto import (
      MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
      MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
      MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
      MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
  )


__UpperCAmelCase     					=  logging.get_logger(__name__)


__UpperCAmelCase     					=  Dict[str, Any]
__UpperCAmelCase     					=  List[Prediction]





@add_end_docstrings(A			)
class A__     (			A			):



 """simple docstring"""







 def __init__(		self			:						List[Any]  ,    *A_			:						int  ,    **A_			:						Dict							):






      '''simple docstring'''




      super().__init__(*A_  ,    **A_							)

      if self.framework == "tf":
           raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''							)

      requires_backends(self  ,    "vision"							)
      self.check_model_type(
          dict(
              MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
              + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
              + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
              + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()							)							)

 def  __magic_name__							(		self			:						List[Any]  ,    **A_			:						int							):






      '''simple docstring'''




      _lowerCAmelCase :       Union[str, Any]	   =     {}
      _lowerCAmelCase :       Dict	   =     {}
      if "subtask" in kwargs:
           _lowerCAmelCase :       int	   =     kwargs["subtask"]
           _lowerCAmelCase :       int	   =     kwargs["subtask"]
      if "threshold" in kwargs:
           _lowerCAmelCase :       Any	   =     kwargs["threshold"]
      if "mask_threshold" in kwargs:
           _lowerCAmelCase :       Tuple	   =     kwargs["mask_threshold"]
      if "overlap_mask_area_threshold" in kwargs:
           _lowerCAmelCase :       List[Any]	   =     kwargs["overlap_mask_area_threshold"]

      return preprocess_kwargs, {}, postprocess_kwargs

 def __call__(		self			:						Any  ,    A_			:						str  ,    **A_			:						Union[str, Any]							):






      '''simple docstring'''




      return super().__call__(A_  ,    **A_							)

 def  __magic_name__							(		self			:						int  ,    A_			:						Optional[Any]  ,    A_			:						Optional[Any]=None							):






      '''simple docstring'''




      _lowerCAmelCase :       Union[str, Any]	   =     load_image(A_							)
      _lowerCAmelCase :       Dict	   =     [(image.height, image.width)]
      if self.model.config.__class__.__name__ == "OneFormerConfig":
           if subtask is None:
                _lowerCAmelCase :       Dict	   =     {}
           else:
                _lowerCAmelCase :       int	   =     {"task_inputs": [subtask]}
           _lowerCAmelCase :       Optional[int]	   =     self.image_processor(images=[image]  ,    return_tensors="pt"  ,    **A_							)
           _lowerCAmelCase :       Tuple	   =     self.tokenizer(
               inputs["task_inputs"]  ,    padding="max_length"  ,    max_length=self.model.config.task_seq_len  ,    return_tensors=self.framework  ,    )["input_ids"]
      else:
           _lowerCAmelCase :       Optional[Any]	   =     self.image_processor(images=[image]  ,    return_tensors="pt"							)
      _lowerCAmelCase :       Union[str, Any]	   =     target_size
      return inputs

 def  __magic_name__							(		self			:						Optional[Any]  ,    A_			:						List[Any]							):






      '''simple docstring'''




      _lowerCAmelCase :       str	   =     model_inputs.pop("target_size"							)
      _lowerCAmelCase :       int	   =     self.model(**A_							)
      _lowerCAmelCase :       Optional[int]	   =     target_size
      return model_outputs


 def  __magic_name__							(		self			:						Any  ,    A_			:						Union[str, Any]  ,    A_			:						Optional[Any]=None  ,    A_			:						Dict=0.9  ,    A_			:						List[str]=0.5  ,    A_			:						Optional[Any]=0.5							):






      '''simple docstring'''




      _lowerCAmelCase :       Optional[Any]	   =     None
      if subtask in {"panoptic", None} and hasattr(self.image_processor  ,    "post_process_panoptic_segmentation"							):
           _lowerCAmelCase :       str	   =     self.image_processor.post_process_panoptic_segmentation
      elif subtask in {"instance", None} and hasattr(self.image_processor  ,    "post_process_instance_segmentation"							):
           _lowerCAmelCase :       List[str]	   =     self.image_processor.post_process_instance_segmentation

      if fn is not None:
           _lowerCAmelCase :       Union[str, Any]	   =     fn(
               A_  ,    threshold=A_  ,    mask_threshold=A_  ,    overlap_mask_area_threshold=A_  ,    target_sizes=model_outputs["target_size"]  ,    )[0]

           _lowerCAmelCase :       Tuple	   =     []
           _lowerCAmelCase :       List[str]	   =     outputs["segmentation"]

           for segment in outputs["segments_info"]:
                _lowerCAmelCase :       List[Any]	   =     (segmentation == segment["id"]) * 2_5_5
                _lowerCAmelCase :       Optional[Any]	   =     Image.fromarray(mask.numpy().astype(np.uinta							)  ,    mode="L"							)
                _lowerCAmelCase :       List[str]	   =     self.model.config.idalabel[segment["label_id"]]
                _lowerCAmelCase :       Union[str, Any]	   =     segment["score"]
                annotation.append({"score": score, "label": label, "mask": mask}							)

      elif subtask in {"semantic", None} and hasattr(self.image_processor  ,    "post_process_semantic_segmentation"							):
           _lowerCAmelCase :       Any	   =     self.image_processor.post_process_semantic_segmentation(
               A_  ,    target_sizes=model_outputs["target_size"]							)[0]

           _lowerCAmelCase :       Any	   =     []
           _lowerCAmelCase :       List[Any]	   =     outputs.numpy()
           _lowerCAmelCase :       Dict	   =     np.unique(A_							)

           for label in labels:
                _lowerCAmelCase :       Any	   =     (segmentation == label) * 2_5_5
                _lowerCAmelCase :       List[str]	   =     Image.fromarray(mask.astype(np.uinta							)  ,    mode="L"							)
                _lowerCAmelCase :       Any	   =     self.model.config.idalabel[label]
                annotation.append({"score": None, "label": label, "mask": mask}							)
      else:
           raise ValueError(F'''Subtask {subtask} is not supported for model {type(self.model							)}'''							)
      return annotation









import math
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    is_accelerate_version,
    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        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif\n\n        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n        >>> repo = "openai/shap-e"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 15.0\n        >>> prompt = "a shark"\n\n        >>> images = pipe(\n        ...     prompt,\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], "shark_3d.gif")\n        ```\n'





@dataclass
class A__     (			A			):



 """simple docstring"""







 _lowercase  :       Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]





class A__     (			A			):



 """simple docstring"""







 def __init__(		self			:						Dict  ,    A_			:						PriorTransformer  ,    A_			:						CLIPTextModelWithProjection  ,    A_			:						CLIPTokenizer  ,    A_			:						HeunDiscreteScheduler  ,    A_			:						ShapERenderer  ,    ):






      '''simple docstring'''




      super().__init__()

      self.register_modules(
          prior=A_  ,    text_encoder=A_  ,    tokenizer=A_  ,    scheduler=A_  ,    renderer=A_  ,    )

 def  __magic_name__							(		self			:						Tuple  ,    A_			:						List[Any]  ,    A_			:						Optional[Any]  ,    A_			:						int  ,    A_			:						Optional[Any]  ,    A_			:						str  ,    A_			:						Dict							):






      '''simple docstring'''




      if latents is None:
           _lowerCAmelCase :       int	   =     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 :       Union[str, Any]	   =     latents.to(A_							)

      _lowerCAmelCase :       Union[str, Any]	   =     latents * scheduler.init_noise_sigma
      return latents

 def  __magic_name__							(		self			:						Any  ,    A_			:						List[Any]=0							):






      '''simple docstring'''




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

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

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

 def  __magic_name__							(		self			:						List[str]  ,    A_			:						List[str]=0							):






      '''simple docstring'''




      if is_accelerate_available() and is_accelerate_version(">="  ,    "0.17.0.dev0"							):
           from accelerate import cpu_offload_with_hook
      else:
           raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."							)

      _lowerCAmelCase :       Optional[int]	   =     torch.device(F'''cuda:{gpu_id}'''							)

      if self.device.type != "cpu":
           self.to("cpu"  ,    silence_dtype_warnings=A_							)
           torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

      _lowerCAmelCase :       Union[str, Any]	   =     None
      for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
           _lowerCAmelCase   ,      _lowerCAmelCase :       Optional[int]	   =     cpu_offload_with_hook(A_  ,    A_  ,    prev_module_hook=A_							)

      if self.safety_checker is not None:
           _lowerCAmelCase   ,      _lowerCAmelCase :       Optional[Any]	   =     cpu_offload_with_hook(self.safety_checker  ,    A_  ,    prev_module_hook=A_							)

      # We'll offload the last model manually.
      _lowerCAmelCase :       Any	   =     hook

 @property
 def  __magic_name__							(		self			:						Union[str, Any]							):






      '''simple docstring'''




      if self.device != torch.device("meta"							) or not hasattr(self.text_encoder  ,    "_hf_hook"							):
           return self.device
      for module in self.text_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  __magic_name__							(		self			:						str  ,    A_			:						List[str]  ,    A_			:						Optional[int]  ,    A_			:						List[str]  ,    A_			:						str  ,    ):






      '''simple docstring'''




      len(A_							) if isinstance(A_  ,    A_							) else 1

      # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
      _lowerCAmelCase :       Dict	   =     0
      # get prompt text embeddings
      _lowerCAmelCase :       Any	   =     self.tokenizer(
          A_  ,    padding="max_length"  ,    max_length=self.tokenizer.model_max_length  ,    truncation=A_  ,    return_tensors="pt"  ,    )
      _lowerCAmelCase :       List[Any]	   =     text_inputs.input_ids
      _lowerCAmelCase :       Any	   =     self.tokenizer(A_  ,    padding="longest"  ,    return_tensors="pt"							).input_ids

      if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(A_  ,    A_							):
           _lowerCAmelCase :       Any	   =     self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]							)
           logger.warning(
               "The following part of your input was truncated because CLIP can only handle sequences up to"
               F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''							)

      _lowerCAmelCase :       Any	   =     self.text_encoder(text_input_ids.to(A_							)							)
      _lowerCAmelCase :       Union[str, Any]	   =     text_encoder_output.text_embeds

      _lowerCAmelCase :       Optional[Any]	   =     prompt_embeds.repeat_interleave(A_  ,    dim=0							)
      # in Shap-E it normalize the prompt_embeds and then later rescale it
      _lowerCAmelCase :       Any	   =     prompt_embeds / torch.linalg.norm(A_  ,    dim=-1  ,    keepdim=A_							)

      if do_classifier_free_guidance:
           _lowerCAmelCase :       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
           _lowerCAmelCase :       Union[str, Any]	   =     torch.cat([negative_prompt_embeds, prompt_embeds]							)

      # Rescale the features to have unit variance
      _lowerCAmelCase :       Tuple	   =     math.sqrt(prompt_embeds.shape[1]							) * prompt_embeds

      return prompt_embeds


 @torch.no_grad()
 @replace_example_docstring(A_							)
 def __call__(		self			:						Tuple  ,    A_			:						str  ,    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_  ,    A_							):
           _lowerCAmelCase :       Tuple	   =     1
      elif isinstance(A_  ,    A_							):
           _lowerCAmelCase :       List[Any]	   =     len(A_							)
      else:
           raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(A_							)}'''							)

      _lowerCAmelCase :       int	   =     self._execution_device

      _lowerCAmelCase :       int	   =     batch_size * num_images_per_prompt

      _lowerCAmelCase :       List[Any]	   =     guidance_scale > 1.0
      _lowerCAmelCase :       Tuple	   =     self._encode_prompt(A_  ,    A_  ,    A_  ,    A_							)

      # prior

      self.scheduler.set_timesteps(A_  ,    device=A_							)
      _lowerCAmelCase :       List[str]	   =     self.scheduler.timesteps

      _lowerCAmelCase :       Dict	   =     self.prior.config.num_embeddings
      _lowerCAmelCase :       int	   =     self.prior.config.embedding_dim

      _lowerCAmelCase :       Optional[Any]	   =     self.prepare_latents(
          (batch_size, num_embeddings * embedding_dim)  ,    prompt_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 :       int	   =     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 :       Any	   =     torch.cat([latents] * 2							) if do_classifier_free_guidance else latents
           _lowerCAmelCase :       Optional[int]	   =     self.scheduler.scale_model_input(A_  ,    A_							)

           _lowerCAmelCase :       Optional[int]	   =     self.prior(
               A_  ,    timestep=A_  ,    proj_embedding=A_  ,    ).predicted_image_embedding

           # remove the variance
           _lowerCAmelCase   ,      _lowerCAmelCase :       Dict	   =     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 :       int	   =     noise_pred.chunk(2							)
                _lowerCAmelCase :       Any	   =     noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

           _lowerCAmelCase :       str	   =     self.scheduler.step(
               A_  ,    timestep=A_  ,    sample=A_  ,    ).prev_sample

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

      _lowerCAmelCase :       Optional[Any]	   =     []
      for i, latent in enumerate(A_							):
           _lowerCAmelCase :       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_							)

      _lowerCAmelCase :       Any	   =     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 :       List[Any]	   =     images.cpu().numpy()

      if output_type == "pil":
           _lowerCAmelCase :       Optional[Any]	   =     [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_							)






