"""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 		__A							(				SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
					_UpperCamelCase							:    Optional[int]	      =	ShapEPipeline
					_UpperCamelCase							:    Optional[Any]	      =	["prompt"]
					_UpperCamelCase							:    Tuple	      =	["prompt"]
					_UpperCamelCase							:    Dict	      =	[
					    "num_images_per_prompt",
					    "num_inference_steps",
					    "generator",
					    "latents",
					    "guidance_scale",
					    "frame_size",
					    "output_type",
					    "return_dict",
					]
					_UpperCamelCase							:    str	      =	False






					@property
					def 					__A						(	self						):
							return 32






					@property
					def 					__A						(	self						):
							return 32






					@property
					def 					__A						(	self						):
							return self.time_input_dim * 4






					@property
					def 					__A						(	self						):
							return 8






					@property
					def 					__A						(	self						):
							_lowerCAmelCase		: Union[str, Any]						    =  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)
							return tokenizer






					@property
					def 					__A						(	self						):
							torch.manual_seed(0						)
							_lowerCAmelCase		: List[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=1000       ,  )
							return CLIPTextModelWithProjection(a__						)






					@property
					def 					__A						(	self						):
							torch.manual_seed(0						)

							_lowerCAmelCase		: Tuple						    =  {
							    """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,
							}

							_lowerCAmelCase		: Any						    =  PriorTransformer(**a__						)
							return model






					@property
					def 					__A						(	self						):
							torch.manual_seed(0						)

							_lowerCAmelCase		: 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,
							    ),
							}
							_lowerCAmelCase		: Dict						    =  ShapERenderer(**a__						)
							return model






					def 					__A						(	self						):
							_lowerCAmelCase		: Union[str, Any]						    =  self.dummy_prior
							_lowerCAmelCase		: Any						    =  self.dummy_text_encoder
							_lowerCAmelCase		: List[Any]						    =  self.dummy_tokenizer
							_lowerCAmelCase		: Dict						    =  self.dummy_renderer

							_lowerCAmelCase		: List[Any]						    =  HeunDiscreteScheduler(
							    beta_schedule="""exp"""       ,  num_train_timesteps=1024       ,  prediction_type="""sample"""       ,  use_karras_sigmas=a__       ,  clip_sample=a__       ,  clip_sample_range=1.0       ,  )
							_lowerCAmelCase		: List[Any]						    =  {
							    """prior""": prior,
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """renderer""": renderer,
							    """scheduler""": scheduler,
							}

							return components






					def 					__A						(	self       ,  a__       ,  a__=0						):
							if str(a__						).startswith("""mps"""						):
									_lowerCAmelCase		: List[str]						    =  torch.manual_seed(a__						)
							else:
									_lowerCAmelCase		: Union[str, Any]						    =  torch.Generator(device=a__						).manual_seed(a__						)
							_lowerCAmelCase		: Dict						    =  {
							    """prompt""": """horse""",
							    """generator""": generator,
							    """num_inference_steps""": 1,
							    """frame_size""": 32,
							    """output_type""": """np""",
							}
							return inputs






					def 					__A						(	self						):
							_lowerCAmelCase		: List[Any]						    =  """cpu"""

							_lowerCAmelCase		: List[Any]						    =  self.get_dummy_components()

							_lowerCAmelCase		: str						    =  self.pipeline_class(**a__						)
							_lowerCAmelCase		: List[Any]						    =  pipe.to(a__						)

							pipe.set_progress_bar_config(disable=a__						)

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

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

							_lowerCAmelCase		: Union[str, Any]						    =  np.array(
							    [
							        0.0_0_0_3_9_2_1_6,
							        0.0_0_0_3_9_2_1_6,
							        0.0_0_0_3_9_2_1_6,
							        0.0_0_0_3_9_2_1_6,
							        0.0_0_0_3_9_2_1_6,
							        0.0_0_0_3_9_2_1_6,
							        0.0_0_0_3_9_2_1_6,
							        0.0_0_0_3_9_2_1_6,
							        0.0_0_0_3_9_2_1_6,
							    ]						)

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






					def 					__A						(	self						):
							# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
							self._test_inference_batch_consistent(batch_sizes=[1, 2]						)






					def 					__A						(	self						):
							_lowerCAmelCase		: Any						    =  torch_device == """cpu"""
							_lowerCAmelCase		: Dict						    =  True

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






					def 					__A						(	self						):
							_lowerCAmelCase		: int						    =  self.get_dummy_components()
							_lowerCAmelCase		: Optional[Any]						    =  self.pipeline_class(**a__						)
							_lowerCAmelCase		: int						    =  pipe.to(a__						)
							pipe.set_progress_bar_config(disable=a__						)

							_lowerCAmelCase		: str						    =  1
							_lowerCAmelCase		: Optional[Any]						    =  2

							_lowerCAmelCase		: List[Any]						    =  self.get_dummy_inputs(a__						)

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

							_lowerCAmelCase		: Tuple						    =  pipe(**a__       ,  num_images_per_prompt=a__						)[0]

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







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






					def 					__A						(	self						):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






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

							_lowerCAmelCase		: Optional[int]						    =  torch.Generator(device=a__						).manual_seed(0						)

							_lowerCAmelCase		: Any						    =  pipe(
							    """a shark"""       ,  generator=a__       ,  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(a__       ,  a__						)





"""simple docstring"""




import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionInpaintPipeline` instead.'
)




