
'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, 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 UpperCAmelCase__	( lowercase__    ,				unittest.TestCase	):





  """simple docstring"""

  __UpperCAmelCase				:				Optional[Any]   =					ShapEImgaImgPipeline
  __UpperCAmelCase				:				Any   =					['''image''']
  __UpperCAmelCase				:				Optional[Any]   =					['''image''']
  __UpperCAmelCase				:				List[str]   =					[
      '''num_images_per_prompt''',
      '''num_inference_steps''',
      '''generator''',
      '''latents''',
      '''guidance_scale''',
      '''frame_size''',
      '''output_type''',
      '''return_dict''',
  ]
  __UpperCAmelCase				:				List[str]   =					False


  @property
  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     return 32


  @property
  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     return 32


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


     '''simple docstring'''






     return self.time_input_dim * 4


  @property
  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     return 8


  @property
  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	Optional[int]			=   CLIPVisionConfig(
         hidden_size=self.text_embedder_hidden_size							,image_size=64							,projection_dim=self.text_embedder_hidden_size							,intermediate_size=37							,num_attention_heads=4							,num_channels=3							,num_hidden_layers=5							,patch_size=1							,)

     _a				:	int			=   CLIPVisionModel(_a       )
     return model


  @property
  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Dict			=   CLIPImageProcessor(
         crop_size=224							,do_center_crop=_a							,do_normalize=_a							,do_resize=_a							,image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]							,image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]							,resample=3							,size=224							,)

     return image_processor


  @property
  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     torch.manual_seed(0       )

     _a				:	str			=   {
         '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',
         'embedding_proj_norm_type': 'layer',
         'encoder_hid_proj_type': None,
         'added_emb_type': None,
     }

     _a				:	Tuple			=   PriorTransformer(**_a       )
     return model


  @property
  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     torch.manual_seed(0       )

     _a				:	Tuple			=   {
         '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,
         ),
     }
     _a				:	List[str]			=   ShapERenderer(**_a       )
     return model


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a				:	int			=   self.dummy_prior
     _a				:	Optional[int]			=   self.dummy_image_encoder
     _a				:	Dict			=   self.dummy_image_processor
     _a				:	List[Any]			=   self.dummy_renderer

     _a				:	Union[str, Any]			=   HeunDiscreteScheduler(
         beta_schedule='exp'							,num_train_timesteps=1024							,prediction_type='sample'							,use_karras_sigmas=_a							,clip_sample=_a							,clip_sample_range=1.0							,)
     _a				:	Optional[int]			=   {
         'prior': prior,
         'image_encoder': image_encoder,
         'image_processor': image_processor,
         'renderer': renderer,
         'scheduler': scheduler,
     }

     return components


  def   __lowercase		(		self      :     Optional[int]							,_a      :     Tuple							,_a      :     str=0       ):


     '''simple docstring'''






     _a				:	int			=   floats_tensor((1, 3, 64, 64)							,rng=random.Random(_a       )       ).to(_a       )

     if str(_a       ).startswith('mps'       ):
        _a				:	Optional[Any]			=   torch.manual_seed(_a       )
     else:
        _a				:	str			=   torch.Generator(device=_a       ).manual_seed(_a       )
     _a				:	str			=   {
         'image': input_image,
         'generator': generator,
         'num_inference_steps': 1,
         'frame_size': 32,
         'output_type': 'np',
     }
     return inputs


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	str			=   'cpu'

     _a				:	List[str]			=   self.get_dummy_components()

     _a				:	int			=   self.pipeline_class(**_a       )
     _a				:	int			=   pipe.to(_a       )

     pipe.set_progress_bar_config(disable=_a       )

     _a				:	List[str]			=   pipe(**self.get_dummy_inputs(_a       )       )
     _a				:	List[Any]			=   output.images[0]
     _a				:	Any			=   image[0, -3:, -3:, -1]

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

     _a				:	Any			=   np.array(
         [
             0.0003_9216,
             0.0003_9216,
             0.0003_9216,
             0.0003_9216,
             0.0003_9216,
             0.0003_9216,
             0.0003_9216,
             0.0003_9216,
             0.0003_9216,
         ]       )

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     self._test_inference_batch_consistent(batch_sizes=[1, 2]       )


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	int			=   torch_device == 'cpu'
     _a				:	Optional[Any]			=   True
     self._test_inference_batch_single_identical(
         batch_size=2							,test_max_difference=_a							,relax_max_difference=_a							,)


  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     _a				:	List[str]			=   self.get_dummy_components()
     _a				:	List[str]			=   self.pipeline_class(**_a       )
     _a				:	Any			=   pipe.to(_a       )
     pipe.set_progress_bar_config(disable=_a       )

     _a				:	str			=   1
     _a				:	Dict			=   2

     _a				:	List[str]			=   self.get_dummy_inputs(_a       )

     for key in inputs.keys():
        if key in self.batch_params:
           _a				:	Optional[int]			=   batch_size * [inputs[key]]

     _a				:	Union[str, Any]			=   pipe(**_a							,num_images_per_prompt=_a       )[0]

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







@slow
@require_torch_gpu
class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	Optional[Any]			=   load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png'       )
     _a				:	Any			=   load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/shap_e/test_shap_e_img2img_out.npy'       )
     _a				:	Optional[int]			=   ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img'       )
     _a				:	List[Any]			=   pipe.to(_a       )
     pipe.set_progress_bar_config(disable=_a       )

     _a				:	List[str]			=   torch.Generator(device=_a       ).manual_seed(0       )

     _a				:	int			=   pipe(
         _a							,generator=_a							,guidance_scale=3.0							,num_inference_steps=64							,frame_size=64							,output_type='np'							,).images[0]

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

     assert_mean_pixel_difference(_a							,_a       )





'''simple docstring'''







def 					UpperCAmelCase_		(__a      :    list ,	__a      :    list ,	__a      :    int ):






   """simple docstring"""

   _a				:	Optional[Any]			=   len(__a )
   _a				:	int			=   [[0] * n for i in range(__a )]
   for i in range(__a ):
      _a				:	Tuple			=   y_points[i]

   for i in range(2 ,	__a ):
      for j in range(__a ,	__a ):
         _a				:	Tuple			=   (
             (xa - x_points[j - i + 1]) * q[j][i - 1]
             - (xa - x_points[j]) * q[j - 1][i - 1]
         ) / (x_points[j] - x_points[j - i + 1])

   return [q[n - 1][n - 1], q]


if __name__ == "__main__":
  import doctest

  doctest.testmod()



