



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        lowerCAmelCase__					(  _lowerCamelCase ,				unittest.TestCase       ):
				A_   :     List[str]								=     ShapEPipeline
				A_   :     Dict								=     ['prompt']
				A_   :     Dict								=     ['prompt']
				A_   :     Union[str, Any]								=     [
				    'num_images_per_prompt',
				    'num_inference_steps',
				    'generator',
				    'latents',
				    'guidance_scale',
				    'frame_size',
				    'output_type',
				    'return_dict',
				]
				A_   :     List[Any]								=     False


				@property
				def __UpperCamelCase						(							self      :  int       )		->     str:
											return 32


				@property
				def __UpperCamelCase						(							self      :  Tuple       )		->     Optional[int]:
											return 32


				@property
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Optional[int]:
											return self.time_input_dim * 4


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


				@property
				def __UpperCamelCase						(							self      :  List[str]       )		->     Dict:
											A      						=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )
											return tokenizer


				@property
				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     Dict:
											torch.manual_seed(0       )
											A      						=    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(__UpperCamelCase       )


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

											A      						=    {
											    '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,
											}

											A      						=    PriorTransformer(**__UpperCamelCase       )
											return model


				@property
				def __UpperCamelCase						(							self      :  Any       )		->     str:
											torch.manual_seed(0       )

											A      						=    {
											    '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      						=    ShapERenderer(**__UpperCamelCase       )
											return model


				def __UpperCamelCase						(							self      :  List[str]       )		->     int:
											A      						=    self.dummy_prior
											A      						=    self.dummy_text_encoder
											A      						=    self.dummy_tokenizer
											A      						=    self.dummy_renderer

											A      						=    HeunDiscreteScheduler(
											    beta_schedule='exp'						, num_train_timesteps=1_024						, prediction_type='sample'						, use_karras_sigmas=__UpperCamelCase						, clip_sample=__UpperCamelCase						, clip_sample_range=1.0						, )
											A      						=    {
											    'prior': prior,
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											    'renderer': renderer,
											    'scheduler': scheduler,
											}

											return components


				def __UpperCamelCase						(							self      :  Optional[Any]						, __UpperCamelCase      :  Tuple						, __UpperCamelCase      :  Dict=0       )		->     Any:
											if str(__UpperCamelCase       ).startswith('mps'       ):
																		A      						=    torch.manual_seed(__UpperCamelCase       )
											else:
																		A      						=    torch.Generator(device=__UpperCamelCase       ).manual_seed(__UpperCamelCase       )
											A      						=    {
											    'prompt': 'horse',
											    'generator': generator,
											    'num_inference_steps': 1,
											    'frame_size': 32,
											    'output_type': 'np',
											}
											return inputs


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Optional[Any]:
											A      						=    'cpu'

											A      						=    self.get_dummy_components()

											A      						=    self.pipeline_class(**__UpperCamelCase       )
											A      						=    pipe.to(__UpperCamelCase       )

											pipe.set_progress_bar_config(disable=__UpperCamelCase       )

											A      						=    pipe(**self.get_dummy_inputs(__UpperCamelCase       )       )
											A      						=    output.images[0]
											A      						=    image[0, -3:, -3:, -1]

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

											A      						=    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 __UpperCamelCase						(							self      :  Tuple       )		->     Optional[int]:
											# 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      :  List[Any]       )		->     str:
											A      						=    torch_device == 'cpu'
											A      						=    True

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


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[str]:
											A      						=    self.get_dummy_components()
											A      						=    self.pipeline_class(**__UpperCamelCase       )
											A      						=    pipe.to(__UpperCamelCase       )
											pipe.set_progress_bar_config(disable=__UpperCamelCase       )

											A      						=    1
											A      						=    2

											A      						=    self.get_dummy_inputs(__UpperCamelCase       )

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

											A      						=    pipe(**__UpperCamelCase						, num_images_per_prompt=__UpperCamelCase       )[0]

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





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


				def __UpperCamelCase						(							self      :  str       )		->     Tuple:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()


				def __UpperCamelCase						(							self      :  Any       )		->     int:
											A      						=    load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/shap_e/test_shap_e_np_out.npy'       )
											A      						=    ShapEPipeline.from_pretrained('openai/shap-e'       )
											A      						=    pipe.to(__UpperCamelCase       )
											pipe.set_progress_bar_config(disable=__UpperCamelCase       )

											A      						=    torch.Generator(device=__UpperCamelCase       ).manual_seed(0       )

											A      						=    pipe(
											    'a shark'						, generator=__UpperCamelCase						, guidance_scale=1_5.0						, num_inference_steps=64						, frame_size=64						, output_type='np'						, ).images[0]

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

											assert_mean_pixel_difference(__UpperCamelCase						, __UpperCamelCase       )


'''simple docstring'''





def 				_lowerCAmelCase			(   __magic_name__						:     int = 600851475143						)				->  int:

						try:
												lowercase :							Any    		=int(__magic_name__						)
						except (TypeError, ValueError):
												raise TypeError('''Parameter n must be int or castable to int.'''						)
						if n <= 0:
												raise ValueError('''Parameter n must be greater than or equal to one.'''						)
						lowercase :							Optional[Any]    		=2
						lowercase :							Dict    		=0
						if n == 2:
												return 2
						while n > 2:
												while n % i != 0:
																		i += 1
												lowercase :							Union[str, Any]    		=i
												while n % i == 0:
																		lowercase :							Optional[int]    		=n // i
												i += 1
						return int(__magic_name__						)


if __name__ == "__main__":
							print(f'''{solution() = }''')





