



'''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


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

lowercase_									=    "\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 __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			Union[PIL.Image.Image, np.ndarray]






class __A     (    A		):



 '''simple docstring'''







 def __init__(self					,  A					,  A					,  A					,  A					,  A					,  )       ->	Any:


      """simple docstring"""



      super().__init__()

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



 def    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A			)       ->	Any:


      """simple docstring"""



      if latents is None:
           _a           =    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}'''			)
           _a           =    latents.to(A			)

      _a           =    latents * scheduler.init_noise_sigma
      return latents



 def    a__    (self					,  A=0			)       ->	List[str]:


      """simple docstring"""



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

      _a           =    torch.device(f'''cuda:{gpu_id}'''			)

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



 @property
 def    a__    (self			)       ->	str:


      """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    a__    (self					,  A					,  A					,  A					,  A					,  )       ->	Tuple:


      """simple docstring"""



      if isinstance(A					,  A			) and isinstance(image[0]					,  torch.Tensor			):
           _a           =    torch.cat(A					,  axis=0			) if image[0].ndim == 4 else torch.stack(A					,  axis=0			)

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

      _a           =    image.to(dtype=self.image_encoder.dtype					,  device=A			)

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

      _a           =    image_embeds.repeat_interleave(A					,  dim=0			)

      if do_classifier_free_guidance:
           _a           =    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
           _a           =    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					,  )       ->	List[Any]:


      """simple docstring"""



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

      _a           =    self._execution_device

      _a           =    batch_size * num_images_per_prompt

      _a           =    guidance_scale > 1.0
      _a           =    self._encode_image(A					,  A					,  A					,  A			)

      # prior

      self.scheduler.set_timesteps(A					,  device=A			)
      _a           =    self.scheduler.timesteps

      _a           =    self.prior.config.num_embeddings
      _a           =    self.prior.config.embedding_dim

      _a           =    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
      _a           =    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
           _a           =    torch.cat([latents] * 2			) if do_classifier_free_guidance else latents
           _a           =    self.scheduler.scale_model_input(A					,  A			)

           _a           =    self.prior(
               A					,  timestep=A					,  proj_embedding=A					,  ).predicted_image_embedding

           # remove the variance
           _a    ,       _a           =    noise_pred.split(
               scaled_model_input.shape[2]					,  dim=2			)  # batch_size, num_embeddings, embedding_dim

           if do_classifier_free_guidance is not None:
                _a    ,       _a           =    noise_pred.chunk(2			)
                _a           =    noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

           _a           =    self.scheduler.step(
               A					,  timestep=A					,  sample=A					,  ).prev_sample

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

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

           images.append(A			)

      _a           =    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}'''			)

      _a           =    images.cpu().numpy()

      if output_type == "pil":
           _a           =    [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 json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Union[str, Any]		=			CTRLTokenizer
 __lowerCamelCase       :			Union[str, Any]		=			False
 __lowerCamelCase       :			Any		=			False



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      _a           =    ['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
      _a           =    dict(zip(A					,  range(len(A			)			)			)			)
      _a           =    ['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
      _a           =    {'''unk_token''': '''<unk>'''}

      _a           =    os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES['''vocab_file''']			)
      _a           =    os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES['''merges_file''']			)
      with open(self.vocab_file					,  '''w'''					,  encoding='''utf-8'''			) as fp:
           fp.write(json.dumps(A			) + '''\n'''			)
      with open(self.merges_file					,  '''w'''					,  encoding='''utf-8'''			) as fp:
           fp.write('''\n'''.join(A			)			)



 def    a__    (self					,  **A			)       ->	int:


      """simple docstring"""



      kwargs.update(self.special_tokens_map			)
      return CTRLTokenizer.from_pretrained(self.tmpdirname					,  **A			)



 def    a__    (self					,  A			)       ->	Tuple:


      """simple docstring"""



      _a           =    '''adapt react readapt apt'''
      _a           =    '''adapt react readapt apt'''
      return input_text, output_text



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      _a           =    CTRLTokenizer(self.vocab_file					,  self.merges_file					,  **self.special_tokens_map			)
      _a           =    '''adapt react readapt apt'''
      _a           =    '''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
      _a           =    tokenizer.tokenize(A			)
      self.assertListEqual(A					,  A			)

      _a           =    tokens + [tokenizer.unk_token]

      _a           =    [0, 1, 2, 4, 5, 1, 0, 3, 6]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)					,  A			)



