


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				):



					'''simple docstring'''







					__UpperCAmelCase            =				ShapEPipeline
					__UpperCAmelCase            =				["prompt"]
					__UpperCAmelCase            =				["prompt"]
					__UpperCAmelCase            =				[
					    "num_images_per_prompt",
					    "num_inference_steps",
					    "generator",
					    "latents",
					    "guidance_scale",
					    "frame_size",
					    "output_type",
					    "return_dict",
					]
					__UpperCAmelCase            =				False






					@property
					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								return 3_2






					@property
					def A      (				self						)   ->			Tuple:







								'''simple docstring'''

								return 3_2






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







								'''simple docstring'''

								return self.time_input_dim * 4






					@property
					def A      (				self						)   ->			List[str]:







								'''simple docstring'''

								return 8






					@property
					def A      (				self						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)
								return tokenizer






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







								'''simple docstring'''

								torch.manual_seed(0						)
								__lowercase 					=   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=3_7 ,       layer_norm_eps=1e-0_5 ,       num_attention_heads=4 ,       num_hidden_layers=5 ,       pad_token_id=1 ,       vocab_size=1_0_0_0 ,       )
								return CLIPTextModelWithProjection(snake_case_						)






					@property
					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								torch.manual_seed(0						)

								__lowercase 					=   {
								    '''num_attention_heads''': 2,
								    '''attention_head_dim''': 1_6,
								    '''embedding_dim''': self.time_input_dim,
								    '''num_embeddings''': 3_2,
								    '''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,
								}

								__lowercase 					=   PriorTransformer(**snake_case_						)
								return model






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







								'''simple docstring'''

								torch.manual_seed(0						)

								__lowercase 					=   {
								    '''param_shapes''': (
								        (self.renderer_dim, 9_3),
								        (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''': 1_2,
								    '''background''': (
								        0.1,
								        0.1,
								        0.1,
								    ),
								}
								__lowercase 					=   ShapERenderer(**snake_case_						)
								return model






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								__lowercase 					=   self.dummy_prior
								__lowercase 					=   self.dummy_text_encoder
								__lowercase 					=   self.dummy_tokenizer
								__lowercase 					=   self.dummy_renderer

								__lowercase 					=   HeunDiscreteScheduler(
								    beta_schedule='''exp''' ,       num_train_timesteps=1_0_2_4 ,       prediction_type='''sample''' ,       use_karras_sigmas=snake_case_ ,       clip_sample=snake_case_ ,       clip_sample_range=1.0 ,       )
								__lowercase 					=   {
								    '''prior''': prior,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''renderer''': renderer,
								    '''scheduler''': scheduler,
								}

								return components






					def A      (				self ,       snake_case_ ,       snake_case_=0						)   ->			int:







								'''simple docstring'''

								if str(snake_case_						).startswith('''mps'''						):
											__lowercase 					=   torch.manual_seed(snake_case_						)
								else:
											__lowercase 					=   torch.Generator(device=snake_case_						).manual_seed(snake_case_						)
								__lowercase 					=   {
								    '''prompt''': '''horse''',
								    '''generator''': generator,
								    '''num_inference_steps''': 1,
								    '''frame_size''': 3_2,
								    '''output_type''': '''np''',
								}
								return inputs






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







								'''simple docstring'''

								__lowercase 					=   '''cpu'''

								__lowercase 					=   self.get_dummy_components()

								__lowercase 					=   self.pipeline_class(**snake_case_						)
								__lowercase 					=   pipe.to(snake_case_						)

								pipe.set_progress_bar_config(disable=snake_case_						)

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

								assert image.shape == (2_0, 3_2, 3_2, 3)

								__lowercase 					=   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						)   ->			Optional[int]:







								'''simple docstring'''

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






					def A      (				self						)   ->			Tuple:







								'''simple docstring'''

								__lowercase 					=   torch_device == '''cpu'''
								__lowercase 					=   True

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






					def A      (				self						)   ->			Tuple:







								'''simple docstring'''

								__lowercase 					=   self.get_dummy_components()
								__lowercase 					=   self.pipeline_class(**snake_case_						)
								__lowercase 					=   pipe.to(snake_case_						)
								pipe.set_progress_bar_config(disable=snake_case_						)

								__lowercase 					=   1
								__lowercase 					=   2

								__lowercase 					=   self.get_dummy_inputs(snake_case_						)

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

								__lowercase 					=   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 	lowerCamelCase_       (				unittest.TestCase				):



					'''simple docstring'''







					def A      (				self						)   ->			Dict:







								'''simple docstring'''

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






					def A      (				self						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/shap_e/test_shap_e_np_out.npy'''						)
								__lowercase 					=   ShapEPipeline.from_pretrained('''openai/shap-e'''						)
								__lowercase 					=   pipe.to(snake_case_						)
								pipe.set_progress_bar_config(disable=snake_case_						)

								__lowercase 					=   torch.Generator(device=snake_case_						).manual_seed(0						)

								__lowercase 					=   pipe(
								    '''a shark''' ,       generator=snake_case_ ,       guidance_scale=1_5.0 ,       num_inference_steps=6_4 ,       frame_size=6_4 ,       output_type='''np''' ,       ).images[0]

								assert images.shape == (2_0, 6_4, 6_4, 3)

								assert_mean_pixel_difference(snake_case_ ,       snake_case_						)






def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			return price * (1 + tax_rate)


if __name__ == "__main__":
	print(f'''{price_plus_tax(100, 0.25) = }''')
	print(f'''{price_plus_tax(125.50, 0.05) = }''')


