


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	(_lowerCAmelCase  ,       unittest.TestCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Optional[Any]  						=						ShapEPipeline
							__UpperCAmelCase					:							Tuple  						=						["prompt"]
							__UpperCAmelCase					:							str  						=						["prompt"]
							__UpperCAmelCase					:							Dict  						=						[
							    "num_images_per_prompt",
							    "num_inference_steps",
							    "generator",
							    "latents",
							    "guidance_scale",
							    "frame_size",
							    "output_type",
							    "return_dict",
							]
							__UpperCAmelCase					:							Optional[int]  						=						False

							@property
							def 					__snake_case      (							self :						List[str]       )			->					List[Any]:
														return 32

							@property
							def 					__snake_case      (							self :						Dict       )			->					Optional[Any]:
														return 32

							@property
							def 					__snake_case      (							self :						str       )			->					Any:
														return self.time_input_dim * 4

							@property
							def 					__snake_case      (							self :						Union[str, Any]       )			->					Tuple:
														return 8

							@property
							def 					__snake_case      (							self :						Tuple       )			->					Tuple:
														__snake_case	:    List[Any]											=     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )
														return tokenizer

							@property
							def 					__snake_case      (							self :						Tuple       )			->					Optional[Any]:
														torch.manual_seed(0       )
														__snake_case	:    Optional[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(lowerCamelCase       )

							@property
							def 					__snake_case      (							self :						Optional[Any]       )			->					Tuple:
														torch.manual_seed(0       )

														__snake_case	:    int											=     {
														    "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,
														}

														__snake_case	:    Tuple											=     PriorTransformer(**lowerCamelCase       )
														return model

							@property
							def 					__snake_case      (							self :						Optional[Any]       )			->					List[Any]:
														torch.manual_seed(0       )

														__snake_case	:    List[str]											=     {
														    "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,
														    ),
														}
														__snake_case	:    int											=     ShapERenderer(**lowerCamelCase       )
														return model

							def 					__snake_case      (							self :						str       )			->					Any:
														__snake_case	:    List[str]											=     self.dummy_prior
														__snake_case	:    Optional[int]											=     self.dummy_text_encoder
														__snake_case	:    Optional[Any]											=     self.dummy_tokenizer
														__snake_case	:    Any											=     self.dummy_renderer

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

														return components

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

							def 					__snake_case      (							self :						Tuple       )			->					Any:
														__snake_case	:    Union[str, Any]											=     "cpu"

														__snake_case	:    Any											=     self.get_dummy_components()

														__snake_case	:    Tuple											=     self.pipeline_class(**lowerCamelCase       )
														__snake_case	:    List[str]											=     pipe.to(lowerCamelCase       )

														pipe.set_progress_bar_config(disable=lowerCamelCase       )

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

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

														__snake_case	:    str											=     np.array(
														    [
														        0.00_03_92_16,
														        0.00_03_92_16,
														        0.00_03_92_16,
														        0.00_03_92_16,
														        0.00_03_92_16,
														        0.00_03_92_16,
														        0.00_03_92_16,
														        0.00_03_92_16,
														        0.00_03_92_16,
														    ]       )

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

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

							def 					__snake_case      (							self :						Dict       )			->					Any:
														__snake_case	:    Union[str, Any]											=     torch_device == "cpu"
														__snake_case	:    Optional[int]											=     True

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






							def 					__snake_case      (							self :						Optional[Any]       )			->					Dict:
														__snake_case	:    Any											=     self.get_dummy_components()
														__snake_case	:    List[Any]											=     self.pipeline_class(**lowerCamelCase       )
														__snake_case	:    Tuple											=     pipe.to(lowerCamelCase       )
														pipe.set_progress_bar_config(disable=lowerCamelCase       )

														__snake_case	:    Tuple											=     1
														__snake_case	:    Optional[Any]											=     2

														__snake_case	:    Tuple											=     self.get_dummy_inputs(lowerCamelCase       )

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

														__snake_case	:    List[str]											=     pipe(**lowerCamelCase					,			num_images_per_prompt=lowerCamelCase       )[0]

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

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




							"""simple docstring"""


							def 					__snake_case      (							self :						Union[str, Any]       )			->					Tuple:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()






							def 					__snake_case      (							self :						Dict       )			->					Any:
														__snake_case	:    List[Any]											=     load_numpy(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
														    "/shap_e/test_shap_e_np_out.npy"       )
														__snake_case	:    List[str]											=     ShapEPipeline.from_pretrained("openai/shap-e"       )
														__snake_case	:    List[Any]											=     pipe.to(lowerCamelCase       )
														pipe.set_progress_bar_config(disable=lowerCamelCase       )

														__snake_case	:    Dict											=     torch.Generator(device=lowerCamelCase       ).manual_seed(0       )

														__snake_case	:    Tuple											=     pipe(
														    "a shark"					,			generator=lowerCamelCase					,			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(lowerCamelCase					,			lowerCamelCase       )





from maths.prime_check import is_prime







def        lowerCAmelCase_  (      __lowerCamelCase			):
							if not isinstance(__lowerCamelCase							,			__lowerCamelCase			):
														__snake_case	:    List[str]											=     F'Input value of [number={number}] must be an integer'
														raise TypeError(__lowerCamelCase			)
							if is_prime(__lowerCamelCase			) and is_prime(number + 2			):
														return number + 2
							else:
														return -1


if __name__ == "__main__":
			import doctest

			doctest.testmod()

