




from torch import nn




def 					lowerCamelCase__       (						A__       :					Any	):





       '''simple docstring'''



       if act_fn in ["swish", "silu"]:
              return nn.SiLU()
       elif act_fn == "mish":
              return nn.Mish()
       elif act_fn == "gelu":
              return nn.GELU()
       else:
              raise ValueError(f'Unsupported activation function: {act_fn}'	)







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__(					__lowerCamelCase ,     unittest.TestCase):
      UpperCAmelCase__      :	Tuple    		=   ShapEImgaImgPipeline
      UpperCAmelCase__      :	Optional[Any]    		=   ['image']
      UpperCAmelCase__      :	int    		=   ['image']
      UpperCAmelCase__      :	Any    		=   [
          'num_images_per_prompt',
          'num_inference_steps',
          'generator',
          'latents',
          'guidance_scale',
          'frame_size',
          'output_type',
          'return_dict',
      ]
      UpperCAmelCase__      :	int    		=   False
      @property
      def 			lowerCAmelCase__						(				self: int   ):
             return 32
      @property
      def 			lowerCAmelCase__						(				self: List[str]   ):
             return 32
      @property
      def 			lowerCAmelCase__						(				self: Any   ):
             return self.time_input_dim * 4
      @property
      def 			lowerCAmelCase__						(				self: Dict   ):
             return 8
      @property
      def 			lowerCAmelCase__						(				self: int   ):
             torch.manual_seed(0   )
             __lowerCamelCase   =						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   =						CLIPVisionModel(UpperCamelCase_   )
             return model
      @property
      def 			lowerCAmelCase__						(				self: Dict   ):
             __lowerCamelCase   =						CLIPImageProcessor(
                 crop_size=2_24    ,       do_center_crop=UpperCamelCase_    ,       do_normalize=UpperCamelCase_    ,       do_resize=UpperCamelCase_    ,       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: Tuple   ):
             torch.manual_seed(0   )

             __lowerCamelCase   =						{
                 """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   =						PriorTransformer(**UpperCamelCase_   )
             return model
      @property
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             torch.manual_seed(0   )

             __lowerCamelCase   =						{
                 """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   =						ShapERenderer(**UpperCamelCase_   )
             return model
      def 			lowerCAmelCase__						(				self: List[str]   ):
             __lowerCamelCase   =						self.dummy_prior
             __lowerCamelCase   =						self.dummy_image_encoder
             __lowerCamelCase   =						self.dummy_image_processor
             __lowerCamelCase   =						self.dummy_renderer

             __lowerCamelCase   =						HeunDiscreteScheduler(
                 beta_schedule="""exp"""    ,       num_train_timesteps=10_24    ,       prediction_type="""sample"""    ,       use_karras_sigmas=UpperCamelCase_    ,       clip_sample=UpperCamelCase_    ,       clip_sample_range=1.0    ,       )
             __lowerCamelCase   =						{
                 """prior""": prior,
                 """image_encoder""": image_encoder,
                 """image_processor""": image_processor,
                 """renderer""": renderer,
                 """scheduler""": scheduler,
             }

             return components
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: List[Any]    ,       UpperCamelCase_: Dict=0   ):
             __lowerCamelCase   =						floats_tensor((1, 3, 64, 64)    ,       rng=random.Random(UpperCamelCase_   )   ).to(UpperCamelCase_   )

             if str(UpperCamelCase_   ).startswith("""mps"""   ):
                    __lowerCamelCase   =						torch.manual_seed(UpperCamelCase_   )
             else:
                    __lowerCamelCase   =						torch.Generator(device=UpperCamelCase_   ).manual_seed(UpperCamelCase_   )
             __lowerCamelCase   =						{
                 """image""": input_image,
                 """generator""": generator,
                 """num_inference_steps""": 1,
                 """frame_size""": 32,
                 """output_type""": """np""",
             }
             return inputs
      def 			lowerCAmelCase__						(				self: Tuple   ):
             __lowerCamelCase   =						"""cpu"""

             __lowerCamelCase   =						self.get_dummy_components()

             __lowerCamelCase   =						self.pipeline_class(**UpperCamelCase_   )
             __lowerCamelCase   =						pipe.to(UpperCamelCase_   )

             pipe.set_progress_bar_config(disable=UpperCamelCase_   )

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

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

             __lowerCamelCase   =						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: List[str]   ):
             # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
             self._test_inference_batch_consistent(batch_sizes=[1, 2]   )
      def 			lowerCAmelCase__						(				self: Any   ):
             __lowerCamelCase   =						torch_device == """cpu"""
             __lowerCamelCase   =						True
             self._test_inference_batch_single_identical(
                 batch_size=2    ,       test_max_difference=UpperCamelCase_    ,       relax_max_difference=UpperCamelCase_    ,       )




      def 			lowerCAmelCase__						(				self: Any   ):
             __lowerCamelCase   =						self.get_dummy_components()
             __lowerCamelCase   =						self.pipeline_class(**UpperCamelCase_   )
             __lowerCamelCase   =						pipe.to(UpperCamelCase_   )
             pipe.set_progress_bar_config(disable=UpperCamelCase_   )

             __lowerCamelCase   =						1
             __lowerCamelCase   =						2

             __lowerCamelCase   =						self.get_dummy_inputs(UpperCamelCase_   )

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

             __lowerCamelCase   =						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 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()




      def 			lowerCAmelCase__						(				self: Any   ):
             __lowerCamelCase   =						load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png"""   )
             __lowerCamelCase   =						load_numpy(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                 """/shap_e/test_shap_e_img2img_out.npy"""   )
             __lowerCamelCase   =						ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img"""   )
             __lowerCamelCase   =						pipe.to(UpperCamelCase_   )
             pipe.set_progress_bar_config(disable=UpperCamelCase_   )

             __lowerCamelCase   =						torch.Generator(device=UpperCamelCase_   ).manual_seed(0   )

             __lowerCamelCase   =						pipe(
                 UpperCamelCase_    ,       generator=UpperCamelCase_    ,       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(UpperCamelCase_    ,       UpperCamelCase_   )

