

'''simple docstring'''

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  (   A ,      unittest.TestCase ):
       lowerCAmelCase_												=			ShapEImgaImgPipeline
       lowerCAmelCase_												=			["image"]
       lowerCAmelCase_												=			["image"]
       lowerCAmelCase_												=			[
           "num_images_per_prompt",
           "num_inference_steps",
           "generator",
           "latents",
           "guidance_scale",
           "frame_size",
           "output_type",
           "return_dict",
       ]
       lowerCAmelCase_												=			False







       @property
       def       snake_case (				self      :			str   ):







            """simple docstring"""







            return 32







       @property
       def       snake_case (				self      :			Tuple   ):







            """simple docstring"""







            return 32







       @property
       def       snake_case (				self      :			Any   ):







            """simple docstring"""







            return self.time_input_dim * 4







       @property
       def       snake_case (				self      :			List[str]   ):







            """simple docstring"""







            return 8







       @property
       def       snake_case (				self      :			str   ):







            """simple docstring"""







            torch.manual_seed(0   )
            __lowercase      		=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    ,   )

            __lowercase      		=CLIPVisionModel(__lowercase   )
            return model







       @property
       def       snake_case (				self      :			Optional[Any]   ):







            """simple docstring"""







            __lowercase      		=CLIPImageProcessor(
                crop_size=224    ,   do_center_crop=__lowercase    ,   do_normalize=__lowercase    ,   do_resize=__lowercase    ,   image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]    ,   image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]    ,   resample=3    ,   size=224    ,   )

            return image_processor







       @property
       def       snake_case (				self      :			Tuple   ):







            """simple docstring"""







            torch.manual_seed(0   )

            __lowercase      		={
                '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,
            }

            __lowercase      		=PriorTransformer(**__lowercase   )
            return model







       @property
       def       snake_case (				self      :			int   ):







            """simple docstring"""







            torch.manual_seed(0   )

            __lowercase      		={
                '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,
                ),
            }
            __lowercase      		=ShapERenderer(**__lowercase   )
            return model







       def       snake_case (				self      :			Any   ):







            """simple docstring"""







            __lowercase      		=self.dummy_prior
            __lowercase      		=self.dummy_image_encoder
            __lowercase      		=self.dummy_image_processor
            __lowercase      		=self.dummy_renderer

            __lowercase      		=HeunDiscreteScheduler(
                beta_schedule='exp'    ,   num_train_timesteps=1024    ,   prediction_type='sample'    ,   use_karras_sigmas=__lowercase    ,   clip_sample=__lowercase    ,   clip_sample_range=1.0    ,   )
            __lowercase      		={
                'prior': prior,
                'image_encoder': image_encoder,
                'image_processor': image_processor,
                'renderer': renderer,
                'scheduler': scheduler,
            }

            return components







       def       snake_case (				self      :			Union[str, Any]    ,   __lowercase      :			Tuple    ,   __lowercase      :			int=0   ):







            """simple docstring"""







            __lowercase      		=floats_tensor((1, 3, 64, 64)    ,   rng=random.Random(__lowercase   )   ).to(__lowercase   )

            if str(__lowercase   ).startswith('mps'   ):
                 __lowercase      		=torch.manual_seed(__lowercase   )
            else:
                 __lowercase      		=torch.Generator(device=__lowercase   ).manual_seed(__lowercase   )
            __lowercase      		={
                'image': input_image,
                'generator': generator,
                'num_inference_steps': 1,
                'frame_size': 32,
                'output_type': 'np',
            }
            return inputs







       def       snake_case (				self      :			List[str]   ):







            """simple docstring"""







            __lowercase      		='cpu'

            __lowercase      		=self.get_dummy_components()

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

            pipe.set_progress_bar_config(disable=__lowercase   )

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

            assert image.shape == (20, 32, 32, 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       snake_case (				self      :			List[str]   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Optional[Any]   ):







            """simple docstring"""







            __lowercase      		=torch_device == 'cpu'
            __lowercase      		=True
            self._test_inference_batch_single_identical(
                batch_size=2    ,   test_max_difference=__lowercase    ,   relax_max_difference=__lowercase    ,   )







       def       snake_case (				self      :			Optional[Any]   ):







            """simple docstring"""







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

            __lowercase      		=1
            __lowercase      		=2

            __lowercase      		=self.get_dummy_inputs(__lowercase   )

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

            __lowercase      		=pipe(**__lowercase    ,   num_images_per_prompt=__lowercase   )[0]

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






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







       def       snake_case (				self      :			Tuple   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Dict   ):







            """simple docstring"""







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

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

            __lowercase      		=pipe(
                __lowercase    ,   generator=__lowercase    ,   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(__lowercase    ,   __lowercase   )





'''simple docstring'''

from __future__ import annotations

def 					__UpperCamelCase				(						lowercase__    :	float,       lowercase__    :	float,       lowercase__    :	float			):






     '''simple docstring'''





     if (voltage, current, resistance).count(0			) != 1:
          raise ValueError('One and only one argument must be 0'			)
     if resistance < 0:
          raise ValueError('Resistance cannot be negative'			)
     if voltage == 0:
          return {"voltage": float(current * resistance			)}
     elif current == 0:
          return {"current": voltage / resistance}
     elif resistance == 0:
          return {"resistance": voltage / current}
     else:
          raise ValueError('Exactly one argument must be 0'			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


