code: 
  
  
  import numpy as np
  from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
  
  from digital_image_processing.filters.convolve import img_convolve
  
  
  
  
  
  
  def       a__						(		snake_case__				:       Optional[Any]    ):
    _UpperCAmelCase		:       Dict       =    np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]    )
    _UpperCAmelCase		:       int       =    np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]    )
  
    _UpperCAmelCase		:       List[Any]       =    np.abs(img_convolve(snake_case__	,      snake_case__    )    )
    _UpperCAmelCase		:       Optional[int]       =    np.abs(img_convolve(snake_case__	,      snake_case__    )    )
    # modify the pix within [0, 255]
    _UpperCAmelCase		:       Optional[Any]       =    dst_x * 255 / np.max(snake_case__    )
    _UpperCAmelCase		:       Tuple       =    dst_y * 255 / np.max(snake_case__    )
  
    _UpperCAmelCase		:       Any       =    np.sqrt((np.square(snake_case__    )) + (np.square(snake_case__    ))    )
    _UpperCAmelCase		:       Optional[int]       =    dst_xy * 255 / np.max(snake_case__    )
    _UpperCAmelCase		:       Tuple       =    dst_xy.astype(np.uinta    )
  
    _UpperCAmelCase		:       List[str]       =    np.arctana(snake_case__	,      snake_case__    )
    return dst, theta
  
  
  if __name__ == "__main__":
   # read original image
   SCREAMING_SNAKE_CASE__				:    str         =    imread('../image_data/lena.jpg')
   # turn image in gray scale value
   SCREAMING_SNAKE_CASE__				:    Union[str, Any]         =    cvtColor(img, COLOR_BGR2GRAY)
  
   SCREAMING_SNAKE_CASE__				:    List[Any]         =    sobel_filter(gray)
  
   # show result images
   imshow('sobel filter', sobel_grad)
   imshow('sobel theta', sobel_theta)
   waitKey(0)
  
  
  
  
  
  
style_context: 
  
  
  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
  
  
  SCREAMING_SNAKE_CASE__				:    Optional[int]         =    logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  SCREAMING_SNAKE_CASE__				:    Tuple         =    '\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 		_SCREAMING_SNAKE_CASE    (	A    ):
       __SCREAMING_SNAKE_CASE    				=						42
  
  
  class 		_SCREAMING_SNAKE_CASE    (	A    ):
  
  
       def __init__(     self       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_       ,				):
         super().__init__()
  
         self.register_modules(
             prior=A_       ,				text_encoder=A_       ,				tokenizer=A_       ,				scheduler=A_       ,				renderer=A_       ,				)
  
  
       def 					__snake_case(     self       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_						):
         if latents is None:
           _UpperCAmelCase		:       List[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}'''						)
           _UpperCAmelCase		:       Optional[int]       =    latents.to(A_						)
  
         _UpperCAmelCase		:       str       =    latents * scheduler.init_noise_sigma
         return latents
  
  
       def 					__snake_case(     self       ,				A_=0						):
         if is_accelerate_available():
           from accelerate import cpu_offload
         else:
           raise ImportError("""Please install accelerate via `pip install accelerate`"""						)
  
         _UpperCAmelCase		:       Any       =    torch.device(F'''cuda:{gpu_id}'''						)
  
         _UpperCAmelCase		:       Dict       =    [self.text_encoder, self.prior]
         for cpu_offloaded_model in models:
           if cpu_offloaded_model is not None:
             cpu_offload(A_       ,				A_						)
  
  
       def 					__snake_case(     self       ,				A_=0						):
         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."""						)
  
         _UpperCAmelCase		:       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)
  
         _UpperCAmelCase		:       List[str]       =    None
         for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
           _UpperCAmelCase,_UpperCAmelCase		:       List[Any]       =    cpu_offload_with_hook(A_       ,				A_       ,				prev_module_hook=A_						)
  
         if self.safety_checker is not None:
           _UpperCAmelCase,_UpperCAmelCase		:       int       =    cpu_offload_with_hook(self.safety_checker       ,				A_       ,				prev_module_hook=A_						)
  
         # We'll offload the last model manually.
         _UpperCAmelCase		:       Optional[Any]       =    hook
  
  
       @property
       def 					__snake_case(     self						):
         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 					__snake_case(     self       ,				A_       ,				A_       ,				A_       ,				A_       ,				):
         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
         _UpperCAmelCase		:       str       =    0
         # get prompt text embeddings
         _UpperCAmelCase		:       List[str]       =    self.tokenizer(
             A_       ,				padding="""max_length"""       ,				max_length=self.tokenizer.model_max_length       ,				truncation=A_       ,				return_tensors="""pt"""       ,				)
         _UpperCAmelCase		:       Any       =    text_inputs.input_ids
         _UpperCAmelCase		:       Dict       =    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_						):
           _UpperCAmelCase		:       Union[str, 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}'''						)
  
         _UpperCAmelCase		:       List[Any]       =    self.text_encoder(text_input_ids.to(A_						)						)
         _UpperCAmelCase		:       Union[str, Any]       =    text_encoder_output.text_embeds
  
         _UpperCAmelCase		:       Tuple       =    prompt_embeds.repeat_interleave(A_       ,				dim=0						)
         # in Shap-E it normalize the prompt_embeds and then later rescale it
         _UpperCAmelCase		:       Any       =    prompt_embeds / torch.linalg.norm(A_       ,				dim=-1       ,				keepdim=A_						)
  
         if do_classifier_free_guidance:
           _UpperCAmelCase		:       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
           _UpperCAmelCase		:       Optional[Any]       =    torch.cat([negative_prompt_embeds, prompt_embeds]						)
  
         # Rescale the features to have unit variance
         _UpperCAmelCase		:       List[Any]       =    math.sqrt(prompt_embeds.shape[1]						) * prompt_embeds
  
         return prompt_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       ,				):
  
         if isinstance(A_       ,				A_						):
           _UpperCAmelCase		:       Optional[int]       =    1
         elif isinstance(A_       ,				A_						):
           _UpperCAmelCase		:       Union[str, Any]       =    len(A_						)
         else:
           raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(A_						)}'''						)
  
         _UpperCAmelCase		:       Tuple       =    self._execution_device
  
         _UpperCAmelCase		:       List[str]       =    batch_size * num_images_per_prompt
  
         _UpperCAmelCase		:       str       =    guidance_scale > 1.0
         _UpperCAmelCase		:       Tuple       =    self._encode_prompt(A_       ,				A_       ,				A_       ,				A_						)
  
         # prior
  
         self.scheduler.set_timesteps(A_       ,				device=A_						)
         _UpperCAmelCase		:       Any       =    self.scheduler.timesteps
  
         _UpperCAmelCase		:       List[str]       =    self.prior.config.num_embeddings
         _UpperCAmelCase		:       Union[str, Any]       =    self.prior.config.embedding_dim
  
         _UpperCAmelCase		:       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
         _UpperCAmelCase		:       Optional[Any]       =    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[int]       =    torch.cat([latents] * 2						) if do_classifier_free_guidance else latents
           _UpperCAmelCase		:       Union[str, Any]       =    self.scheduler.scale_model_input(A_       ,				A_						)
  
           _UpperCAmelCase		:       Any       =    self.prior(
               A_       ,				timestep=A_       ,				proj_embedding=A_       ,				).predicted_image_embedding
  
           # remove the variance
           _UpperCAmelCase,_UpperCAmelCase		:       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:
             _UpperCAmelCase,_UpperCAmelCase		:       Union[str, Any]       =    noise_pred.chunk(2						)
             _UpperCAmelCase		:       List[Any]       =    noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
  
           _UpperCAmelCase		:       Tuple       =    self.scheduler.step(
               A_       ,				timestep=A_       ,				sample=A_       ,				).prev_sample
  
         if output_type == "latent":
           return ShapEPipelineOutput(images=A_						)
  
         _UpperCAmelCase		:       Dict       =    []
         for i, latent in enumerate(A_						):
           _UpperCAmelCase		:       int       =    self.renderer.decode(
               latent[None, :]       ,				A_       ,				size=A_       ,				ray_batch_size=40_96       ,				n_coarse_samples=64       ,				n_fine_samples=1_28       ,				)
           images.append(A_						)
  
         _UpperCAmelCase		:       List[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}'''						)
  
         _UpperCAmelCase		:       str       =    images.cpu().numpy()
  
         if output_type == "pil":
           _UpperCAmelCase		:       List[str]       =    [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_						)
  
  
  
  
  
  