

'''simple docstring'''

import gc
import unittest

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

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference





class 							_snake_case (       _a	,  unittest.TestCase						):
   _A       :     int       =					ShapEPipeline
   _A       :     str       =					['''prompt''']
   _A       :     List[Any]       =					['''prompt''']
   _A       :     Union[str, Any]       =					[
       '''num_images_per_prompt''',
       '''num_inference_steps''',
       '''generator''',
       '''latents''',
       '''guidance_scale''',
       '''frame_size''',
       '''output_type''',
       '''return_dict''',
   ]
   _A       :     Any       =					False
   @property
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     return 32
   @property
   def     __UpperCamelCase						( self			:      List[str]							):
     return 32
   @property
   def     __UpperCamelCase						( self			:      Dict							):
     return self.time_input_dim * 4
   @property
   def     __UpperCamelCase						( self			:      List[Any]							):
     return 8
   @property
   def     __UpperCamelCase						( self			:      Optional[int]							):
     SCREAMING_SNAKE_CASE:Any						      =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)
     return tokenizer
   @property
   def     __UpperCamelCase						( self			:      Optional[int]							):
     torch.manual_seed(0							)
     SCREAMING_SNAKE_CASE:Any						      =					CLIPTextConfig(
         bos_token_id=0     ,eos_token_id=2     ,hidden_size=self.text_embedder_hidden_size     ,projection_dim=self.text_embedder_hidden_size     ,intermediate_size=37     ,layer_norm_eps=1e-05     ,num_attention_heads=4     ,num_hidden_layers=5     ,pad_token_id=1     ,vocab_size=1_000     ,)
     return CLIPTextModelWithProjection(SCREAMING_SNAKE_CASE__							)
   @property
   def     __UpperCamelCase						( self			:      str							):
     torch.manual_seed(0							)

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					{
         "num_attention_heads": 2,
         "attention_head_dim": 16,
         "embedding_dim": self.time_input_dim,
         "num_embeddings": 32,
         "embedding_proj_dim": self.text_embedder_hidden_size,
         "time_embed_dim": self.time_embed_dim,
         "num_layers": 1,
         "clip_embed_dim": self.time_input_dim * 2,
         "additional_embeddings": 0,
         "time_embed_act_fn": "gelu",
         "norm_in_type": "layer",
         "encoder_hid_proj_type": None,
         "added_emb_type": None,
     }

     SCREAMING_SNAKE_CASE:Optional[int]						      =					PriorTransformer(**SCREAMING_SNAKE_CASE__							)
     return model
   @property
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     torch.manual_seed(0							)

     SCREAMING_SNAKE_CASE:int						      =					{
         "param_shapes": (
             (self.renderer_dim, 93),
             (self.renderer_dim, 8),
             (self.renderer_dim, 8),
             (self.renderer_dim, 8),
         ),
         "d_latent": self.time_input_dim,
         "d_hidden": self.renderer_dim,
         "n_output": 12,
         "background": (
             0.1,
             0.1,
             0.1,
         ),
     }
     SCREAMING_SNAKE_CASE:Any						      =					ShapERenderer(**SCREAMING_SNAKE_CASE__							)
     return model
   def     __UpperCamelCase						( self			:      Optional[int]							):
     SCREAMING_SNAKE_CASE:List[Any]						      =					self.dummy_prior
     SCREAMING_SNAKE_CASE:List[Any]						      =					self.dummy_text_encoder
     SCREAMING_SNAKE_CASE:List[Any]						      =					self.dummy_tokenizer
     SCREAMING_SNAKE_CASE:int						      =					self.dummy_renderer

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					HeunDiscreteScheduler(
         beta_schedule="exp"     ,num_train_timesteps=1_024     ,prediction_type="sample"     ,use_karras_sigmas=SCREAMING_SNAKE_CASE__     ,clip_sample=SCREAMING_SNAKE_CASE__     ,clip_sample_range=1.0     ,)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					{
         "prior": prior,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "renderer": renderer,
         "scheduler": scheduler,
     }

     return components
   def     __UpperCamelCase						( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      List[str]=0							):
     if str(SCREAMING_SNAKE_CASE__							).startswith("mps"							):
       SCREAMING_SNAKE_CASE:int						      =					torch.manual_seed(SCREAMING_SNAKE_CASE__							)
     else:
       SCREAMING_SNAKE_CASE:Dict						      =					torch.Generator(device=SCREAMING_SNAKE_CASE__							).manual_seed(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Dict						      =					{
         "prompt": "horse",
         "generator": generator,
         "num_inference_steps": 1,
         "frame_size": 32,
         "output_type": "np",
     }
     return inputs
   def     __UpperCamelCase						( self			:      str							):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					"cpu"

     SCREAMING_SNAKE_CASE:Optional[int]						      =					self.get_dummy_components()

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					self.pipeline_class(**SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:List[Any]						      =					pipe.to(SCREAMING_SNAKE_CASE__							)

     pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					output.images[0]
     SCREAMING_SNAKE_CASE:Optional[int]						      =					image[0, -3:, -3:, -1]

     assert image.shape == (20, 32, 32, 3)

     SCREAMING_SNAKE_CASE:Any						      =					np.array(
         [
             0.00_039_216,
             0.00_039_216,
             0.00_039_216,
             0.00_039_216,
             0.00_039_216,
             0.00_039_216,
             0.00_039_216,
             0.00_039_216,
             0.00_039_216,
         ]							)

     assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
   def     __UpperCamelCase						( self			:      Tuple							):
     # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
     self._test_inference_batch_consistent(batch_sizes=[1, 2]							)
   def     __UpperCamelCase						( self			:      Any							):
     SCREAMING_SNAKE_CASE:List[str]						      =					torch_device == "cpu"
     SCREAMING_SNAKE_CASE:Tuple						      =					True

     self._test_inference_batch_single_identical(
         batch_size=2     ,test_max_difference=SCREAMING_SNAKE_CASE__     ,relax_max_difference=SCREAMING_SNAKE_CASE__     ,)







   def     __UpperCamelCase						( self			:      Dict							):
     SCREAMING_SNAKE_CASE:int						      =					self.get_dummy_components()
     SCREAMING_SNAKE_CASE:List[Any]						      =					self.pipeline_class(**SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Dict						      =					pipe.to(SCREAMING_SNAKE_CASE__							)
     pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:int						      =					1
     SCREAMING_SNAKE_CASE:Dict						      =					2

     SCREAMING_SNAKE_CASE:Any						      =					self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)

     for key in inputs.keys():
       if key in self.batch_params:
         SCREAMING_SNAKE_CASE:List[str]						      =					batch_size * [inputs[key]]

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					pipe(**SCREAMING_SNAKE_CASE__     ,num_images_per_prompt=SCREAMING_SNAKE_CASE__							)[0]

     assert images.shape[0] == batch_size * num_images_per_prompt





@slow
@require_torch_gpu
class 							_snake_case (       unittest.TestCase						):
   def     __UpperCamelCase						( self			:      str							):
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







   def     __UpperCamelCase						( self			:      int							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/shap_e/test_shap_e_np_out.npy"							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					ShapEPipeline.from_pretrained("openai/shap-e"							)
     SCREAMING_SNAKE_CASE:int						      =					pipe.to(SCREAMING_SNAKE_CASE__							)
     pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Tuple						      =					torch.Generator(device=SCREAMING_SNAKE_CASE__							).manual_seed(0							)

     SCREAMING_SNAKE_CASE:Any						      =					pipe(
         "a shark"     ,generator=SCREAMING_SNAKE_CASE__     ,guidance_scale=15.0     ,num_inference_steps=64     ,frame_size=64     ,output_type="np"     ,).images[0]

     assert images.shape == (20, 64, 64, 3)

     assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)









'''simple docstring'''

import warnings
from functools import wraps
from typing import Callable





def    A_  ( snake_case ):

  @wraps(snake_case )
  def _inner_fn(*snake_case      ,       **snake_case ):
    warnings.warn(
        (F'''\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.''')      ,       snake_case      ,       )
    return fn(*snake_case      ,       **snake_case )

  return _inner_fn






