





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        snake_case_    (		__lowercase					,unittest.TestCase						):
  A_      							=   ShapEImgaImgPipeline
  A_      							=   ['image']
  A_      							=   ['image']
  A_      							=   [
      'num_images_per_prompt',
      'num_inference_steps',
      'generator',
      'latents',
      'guidance_scale',
      'frame_size',
      'output_type',
      'return_dict',
  ]
  A_      							=   False






  @property
  def 				UpperCAmelCase__						(							self			:					Dict      )->Optional[Any]:






   '''simple docstring'''

   return 32






  @property
  def 				UpperCAmelCase__						(							self			:					Any      )->Optional[Any]:






   '''simple docstring'''

   return 32






  @property
  def 				UpperCAmelCase__						(							self			:					List[Any]      )->Any:






   '''simple docstring'''

   return self.time_input_dim * 4






  @property
  def 				UpperCAmelCase__						(							self			:					int      )->int:






   '''simple docstring'''

   return 8






  @property
  def 				UpperCAmelCase__						(							self			:					Optional[Any]      )->Union[str, Any]:






   '''simple docstring'''

   torch.manual_seed(0      )
   __lowerCAmelCase					:	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       ,  )

   __lowerCAmelCase					:	int      =       CLIPVisionModel(_snake_case      )
   return model






  @property
  def 				UpperCAmelCase__						(							self			:					Optional[Any]      )->Dict:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       CLIPImageProcessor(
       crop_size=224       ,  do_center_crop=_snake_case       ,  do_normalize=_snake_case       ,  do_resize=_snake_case       ,  image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]       ,  image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]       ,  resample=3       ,  size=224       ,  )

   return image_processor






  @property
  def 				UpperCAmelCase__						(							self			:					Dict      )->Tuple:






   '''simple docstring'''

   torch.manual_seed(0      )

   __lowerCAmelCase					:	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,
   }

   __lowerCAmelCase					:	List[str]      =       PriorTransformer(**_snake_case      )
   return model






  @property
  def 				UpperCAmelCase__						(							self			:					str      )->Dict:






   '''simple docstring'''

   torch.manual_seed(0      )

   __lowerCAmelCase					:	Any      =       {
       """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,
       ),
   }
   __lowerCAmelCase					:	Any      =       ShapERenderer(**_snake_case      )
   return model






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






   '''simple docstring'''

   __lowerCAmelCase					:	Dict      =       self.dummy_prior
   __lowerCAmelCase					:	str      =       self.dummy_image_encoder
   __lowerCAmelCase					:	Tuple      =       self.dummy_image_processor
   __lowerCAmelCase					:	Union[str, Any]      =       self.dummy_renderer

   __lowerCAmelCase					:	str      =       HeunDiscreteScheduler(
       beta_schedule="""exp"""       ,  num_train_timesteps=1024       ,  prediction_type="""sample"""       ,  use_karras_sigmas=_snake_case       ,  clip_sample=_snake_case       ,  clip_sample_range=1.0       ,  )
   __lowerCAmelCase					:	str      =       {
       """prior""": prior,
       """image_encoder""": image_encoder,
       """image_processor""": image_processor,
       """renderer""": renderer,
       """scheduler""": scheduler,
   }

   return components






  def 				UpperCAmelCase__						(							self			:					Optional[Any]       ,  _snake_case			:					Tuple       ,  _snake_case			:					Dict=0      )->str:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[Any]      =       floats_tensor((1, 3, 64, 64)       ,  rng=random.Random(_snake_case      )      ).to(_snake_case      )

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






  def 				UpperCAmelCase__						(							self			:					Any      )->Optional[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Dict      =       """cpu"""

   __lowerCAmelCase					:	Dict      =       self.get_dummy_components()

   __lowerCAmelCase					:	Optional[int]      =       self.pipeline_class(**_snake_case      )
   __lowerCAmelCase					:	List[str]      =       pipe.to(_snake_case      )

   pipe.set_progress_bar_config(disable=_snake_case      )

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

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

   __lowerCAmelCase					:	Union[str, 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			:					str      )->Optional[Any]:






   '''simple docstring'''

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






  def 				UpperCAmelCase__						(							self			:					Dict      )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	int      =       torch_device == """cpu"""
   __lowerCAmelCase					:	List[Any]      =       True
   self._test_inference_batch_single_identical(
       batch_size=2       ,  test_max_difference=_snake_case       ,  relax_max_difference=_snake_case       ,  )






  def 				UpperCAmelCase__						(							self			:					str      )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	int      =       self.get_dummy_components()
   __lowerCAmelCase					:	Dict      =       self.pipeline_class(**_snake_case      )
   __lowerCAmelCase					:	int      =       pipe.to(_snake_case      )
   pipe.set_progress_bar_config(disable=_snake_case      )

   __lowerCAmelCase					:	List[Any]      =       1
   __lowerCAmelCase					:	Tuple      =       2

   __lowerCAmelCase					:	Any      =       self.get_dummy_inputs(_snake_case      )

   for key in inputs.keys():
    if key in self.batch_params:
     __lowerCAmelCase					:	Dict      =       batch_size * [inputs[key]]

   __lowerCAmelCase					:	Optional[int]      =       pipe(**_snake_case       ,  num_images_per_prompt=_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      )->Optional[int]:






   '''simple docstring'''

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






  def 				UpperCAmelCase__						(							self			:					List[Any]      )->Optional[Any]:






   '''simple docstring'''

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

   __lowerCAmelCase					:	Optional[Any]      =       torch.Generator(device=_snake_case      ).manual_seed(0      )

   __lowerCAmelCase					:	List[str]      =       pipe(
       _snake_case       ,  generator=_snake_case       ,  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(_snake_case       ,  _snake_case      )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int | float]]   )      ->   int:
 __lowerCAmelCase					:	str      =       len(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[int]      =       len(matrix[0]   )
 __lowerCAmelCase					:	List[Any]      =       min(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

 for row in range(SCREAMING_SNAKE_CASE   ):
  # Check if diagonal element is not zero
  if matrix[row][row] != 0:
   # Eliminate all the elements below the diagonal
   for col in range(row + 1   ,   SCREAMING_SNAKE_CASE   ):
    __lowerCAmelCase					:	Union[str, Any]      =       matrix[col][row] / matrix[row][row]
    for i in range(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ):
     matrix[col][i] -= multiplier * matrix[row][i]
  else:
   # Find a non-zero diagonal element to swap rows
   __lowerCAmelCase					:	Optional[int]      =       True
   for i in range(row + 1   ,   SCREAMING_SNAKE_CASE   ):
    if matrix[i][row] != 0:
     __lowerCAmelCase ,					__lowerCAmelCase					:	Union[str, Any]      =       matrix[i], matrix[row]
     __lowerCAmelCase					:	Union[str, Any]      =       False
     break
   if reduce:
    rank -= 1
    for i in range(SCREAMING_SNAKE_CASE   ):
     __lowerCAmelCase					:	int      =       matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
   row -= 1

 return rank


if __name__ == "__main__":
      import doctest

      doctest.testmod()