



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        lowerCamelCase__			(							lowercase_      ,				unittest.TestCase):




		'''simple docstring'''



		snake_case_	=ShapEImgaImgPipeline
		snake_case_	=["""image"""]
		snake_case_	=["""image"""]
		snake_case_	=[
		    """num_images_per_prompt""",
		    """num_inference_steps""",
		    """generator""",
		    """latents""",
		    """guidance_scale""",
		    """frame_size""",
		    """output_type""",
		    """return_dict""",
		]
		snake_case_	=False



		@property
		def 		lowerCAmelCase__      (self )     ->			Optional[int]:

						"""simple docstring"""






						return 32



		@property
		def 		lowerCAmelCase__      (self )     ->			Any:

						"""simple docstring"""






						return 32



		@property
		def 		lowerCAmelCase__      (self )     ->			Dict:

						"""simple docstring"""






						return self.time_input_dim * 4



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

						"""simple docstring"""






						return 8



		@property
		def 		lowerCAmelCase__      (self )     ->			int:

						"""simple docstring"""






						torch.manual_seed(0 )
						lowerCAmelCase__     :				Union[str, Any]												=     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__     :				List[Any]												=     CLIPVisionModel(__lowerCamelCase )
						return model



		@property
		def 		lowerCAmelCase__      (self )     ->			Dict:

						"""simple docstring"""






						lowerCAmelCase__     :				Dict												=     CLIPImageProcessor(
						    crop_size=2_24		,do_center_crop=__lowerCamelCase		,do_normalize=__lowerCamelCase		,do_resize=__lowerCamelCase		,image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]		,image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]		,resample=3		,size=2_24		,)

						return image_processor



		@property
		def 		lowerCAmelCase__      (self )     ->			Optional[int]:

						"""simple docstring"""






						torch.manual_seed(0 )

						lowerCAmelCase__     :				Any												=     {
						    '''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__     :				str												=     PriorTransformer(**__lowerCamelCase )
						return model



		@property
		def 		lowerCAmelCase__      (self )     ->			int:

						"""simple docstring"""






						torch.manual_seed(0 )

						lowerCAmelCase__     :				int												=     {
						    '''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__     :				List[Any]												=     ShapERenderer(**__lowerCamelCase )
						return model



		def 		lowerCAmelCase__      (self )     ->			Any:

						"""simple docstring"""






						lowerCAmelCase__     :				List[str]												=     self.dummy_prior
						lowerCAmelCase__     :				List[str]												=     self.dummy_image_encoder
						lowerCAmelCase__     :				str												=     self.dummy_image_processor
						lowerCAmelCase__     :				Tuple												=     self.dummy_renderer

						lowerCAmelCase__     :				Any												=     HeunDiscreteScheduler(
						    beta_schedule='''exp'''		,num_train_timesteps=10_24		,prediction_type='''sample'''		,use_karras_sigmas=__lowerCamelCase		,clip_sample=__lowerCamelCase		,clip_sample_range=1.0		,)
						lowerCAmelCase__     :				List[Any]												=     {
						    '''prior''': prior,
						    '''image_encoder''': image_encoder,
						    '''image_processor''': image_processor,
						    '''renderer''': renderer,
						    '''scheduler''': scheduler,
						}

						return components



		def 		lowerCAmelCase__      (self		,__lowerCamelCase		,__lowerCamelCase=0 )     ->			Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__     :				List[str]												=     floats_tensor((1, 3, 64, 64)		,rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )

						if str(__lowerCamelCase ).startswith('''mps''' ):
										lowerCAmelCase__     :				List[Any]												=     torch.manual_seed(__lowerCamelCase )
						else:
										lowerCAmelCase__     :				Union[str, Any]												=     torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
						lowerCAmelCase__     :				Optional[Any]												=     {
						    '''image''': input_image,
						    '''generator''': generator,
						    '''num_inference_steps''': 1,
						    '''frame_size''': 32,
						    '''output_type''': '''np''',
						}
						return inputs



		def 		lowerCAmelCase__      (self )     ->			Dict:

						"""simple docstring"""






						lowerCAmelCase__     :				int												=     '''cpu'''

						lowerCAmelCase__     :				Optional[Any]												=     self.get_dummy_components()

						lowerCAmelCase__     :				Any												=     self.pipeline_class(**__lowerCamelCase )
						lowerCAmelCase__     :				Tuple												=     pipe.to(__lowerCamelCase )

						pipe.set_progress_bar_config(disable=__lowerCamelCase )

						lowerCAmelCase__     :				str												=     pipe(**self.get_dummy_inputs(__lowerCamelCase ) )
						lowerCAmelCase__     :				Union[str, Any]												=     output.images[0]
						lowerCAmelCase__     :				Optional[int]												=     image[0, -3:, -3:, -1]

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

						lowerCAmelCase__     :				str												=     np.array(
						    [
						        0.0003_9216,
						        0.0003_9216,
						        0.0003_9216,
						        0.0003_9216,
						        0.0003_9216,
						        0.0003_9216,
						        0.0003_9216,
						        0.0003_9216,
						        0.0003_9216,
						    ] )

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



		def 		lowerCAmelCase__      (self )     ->			Any:

						"""simple docstring"""






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



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

						"""simple docstring"""






						lowerCAmelCase__     :				Optional[int]												=     torch_device == '''cpu'''
						lowerCAmelCase__     :				Dict												=     True
						self._test_inference_batch_single_identical(
						    batch_size=2		,test_max_difference=__lowerCamelCase		,relax_max_difference=__lowerCamelCase		,)



		def 		lowerCAmelCase__      (self )     ->			int:

						"""simple docstring"""






						lowerCAmelCase__     :				str												=     self.get_dummy_components()
						lowerCAmelCase__     :				Optional[Any]												=     self.pipeline_class(**__lowerCamelCase )
						lowerCAmelCase__     :				Tuple												=     pipe.to(__lowerCamelCase )
						pipe.set_progress_bar_config(disable=__lowerCamelCase )

						lowerCAmelCase__     :				Any												=     1
						lowerCAmelCase__     :				Any												=     2

						lowerCAmelCase__     :				List[Any]												=     self.get_dummy_inputs(__lowerCamelCase )

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

						lowerCAmelCase__     :				str												=     pipe(**__lowerCamelCase		,num_images_per_prompt=__lowerCamelCase )[0]

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



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




		'''simple docstring'''



		def 		lowerCAmelCase__      (self )     ->			str:

						"""simple docstring"""






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



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

						"""simple docstring"""






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

						lowerCAmelCase__     :				Tuple												=     torch.Generator(device=__lowerCamelCase ).manual_seed(0 )

						lowerCAmelCase__     :				Dict												=     pipe(
						    __lowerCamelCase		,generator=__lowerCamelCase		,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(__lowerCamelCase		,__lowerCamelCase )





'''simple docstring'''

from __future__ import annotations

def _SCREAMING_SNAKE_CASE      (A       )       ->       bool:






       """simple docstring"""







       return len(set(A       )       ) == len(A       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()




