


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   __a      (   __UpperCamelCase	,	unittest.TestCase				):
 __lowercase			:							Optional[int]       					=			ShapEImgaImgPipeline
 __lowercase			:							Dict       					=			['image']
 __lowercase			:							Optional[int]       					=			['image']
 __lowercase			:							Optional[int]       					=			[
     'num_images_per_prompt',
     'num_inference_steps',
     'generator',
     'latents',
     'guidance_scale',
     'frame_size',
     'output_type',
     'return_dict',
 ]
 __lowercase			:							Union[str, Any]       					=			False
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        return 32
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        return 32
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        return self.time_input_dim * 4
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        return 8
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        torch.manual_seed(0			)
        lowercase__:       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    ,			)

        lowercase__:       str        =       CLIPVisionModel(lowerCAmelCase__			)
        return model
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       CLIPImageProcessor(
            crop_size=224    ,			do_center_crop=lowerCAmelCase__    ,			do_normalize=lowerCAmelCase__    ,			do_resize=lowerCAmelCase__    ,			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 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        torch.manual_seed(0			)

        lowercase__:       Optional[int]        =       {
            '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__:       List[str]        =       PriorTransformer(**lowerCAmelCase__			)
        return model
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        torch.manual_seed(0			)

        lowercase__:       Tuple        =       {
            '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__:       str        =       ShapERenderer(**lowerCAmelCase__			)
        return model
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       self.dummy_prior
        lowercase__:       List[str]        =       self.dummy_image_encoder
        lowercase__:       int        =       self.dummy_image_processor
        lowercase__:       Any        =       self.dummy_renderer

        lowercase__:       Tuple        =       HeunDiscreteScheduler(
            beta_schedule='exp'    ,			num_train_timesteps=1_024    ,			prediction_type='sample'    ,			use_karras_sigmas=lowerCAmelCase__    ,			clip_sample=lowerCAmelCase__    ,			clip_sample_range=1.0    ,			)
        lowercase__:       Union[str, Any]        =       {
            'prior': prior,
            'image_encoder': image_encoder,
            'image_processor': image_processor,
            'renderer': renderer,
            'scheduler': scheduler,
        }

        return components
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=0			) ->    Any:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       floats_tensor((1, 3, 64, 64)    ,			rng=random.Random(lowerCAmelCase__			)			).to(lowerCAmelCase__			)

        if str(lowerCAmelCase__			).startswith('mps'			):
               lowercase__:       Optional[Any]        =       torch.manual_seed(lowerCAmelCase__			)
        else:
               lowercase__:       str        =       torch.Generator(device=lowerCAmelCase__			).manual_seed(lowerCAmelCase__			)
        lowercase__:       Dict        =       {
            'image': input_image,
            'generator': generator,
            'num_inference_steps': 1,
            'frame_size': 32,
            'output_type': 'np',
        }
        return inputs
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       List[str]        =       'cpu'

        lowercase__:       str        =       self.get_dummy_components()

        lowercase__:       Union[str, Any]        =       self.pipeline_class(**lowerCAmelCase__			)
        lowercase__:       int        =       pipe.to(lowerCAmelCase__			)

        pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

        lowercase__:       List[Any]        =       pipe(**self.get_dummy_inputs(lowerCAmelCase__			)			)
        lowercase__:       Dict        =       output.images[0]
        lowercase__:       str        =       image[0, -3:, -3:, -1]

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

        lowercase__:       Union[str, Any]        =       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 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
        self._test_inference_batch_consistent(batch_sizes=[1, 2]			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       torch_device == 'cpu'
        lowercase__:       List[Any]        =       True
        self._test_inference_batch_single_identical(
            batch_size=2    ,			test_max_difference=lowerCAmelCase__    ,			relax_max_difference=lowerCAmelCase__    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       Tuple        =       self.get_dummy_components()
        lowercase__:       Optional[int]        =       self.pipeline_class(**lowerCAmelCase__			)
        lowercase__:       List[str]        =       pipe.to(lowerCAmelCase__			)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

        lowercase__:       int        =       1
        lowercase__:       Optional[Any]        =       2

        lowercase__:       Optional[int]        =       self.get_dummy_inputs(lowerCAmelCase__			)

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

        lowercase__:       Dict        =       pipe(**lowerCAmelCase__    ,			num_images_per_prompt=lowerCAmelCase__			)[0]

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





@slow
@require_torch_gpu
class   __a      (   unittest.TestCase				):
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




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

        lowercase__:       str        =       torch.Generator(device=lowerCAmelCase__			).manual_seed(0			)

        lowercase__:       str        =       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__			)






from math import pow




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							)  ->    tuple[int, int]:
       if current_sum == needed_sum:
              # If the sum of the powers is equal to needed_sum, then we have a solution.
              solutions_count += 1
              return current_sum, solutions_count

       lowercase__:       int        =       int(pow(snake_case      ,							snake_case					)					)
       if current_sum + i_to_n <= needed_sum:
              # If the sum of the powers is less than needed_sum, then continue adding powers.
              current_sum += i_to_n
              lowercase__      ,						lowercase__:       Any        =       backtrack(
                  snake_case      ,							snake_case      ,							current_number + 1      ,							snake_case      ,							snake_case					)
              current_sum -= i_to_n
       if i_to_n < needed_sum:
              # If the power of i is less than needed_sum, then try with the next power.
              lowercase__      ,						lowercase__:       Optional[int]        =       backtrack(
                  snake_case      ,							snake_case      ,							current_number + 1      ,							snake_case      ,							snake_case					)
       return current_sum, solutions_count




def        snake_case_					(  snake_case      ,							snake_case					)  ->    int:
       if not (1 <= needed_sum <= 10_00 and 2 <= power <= 10):
              raise ValueError(
                  'Invalid input\n'
                  'needed_sum must be between 1 and 1000, power between 2 and 10.'					)

       return backtrack(snake_case      ,							snake_case      ,							1      ,							0      ,							0					)[1]  # Return the solutions_count


if __name__ == "__main__":
 import doctest

 doctest.testmod()


