


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							(      __SCREAMING_SNAKE_CASE					,     unittest.TestCase      ):
       _lowercase     :      List[Any]  				=			ShapEImgaImgPipeline
       _lowercase     :      int  				=			['''image''']
       _lowercase     :      Union[str, Any]  				=			['''image''']
       _lowercase     :      List[Any]  				=			[
           '''num_images_per_prompt''',
           '''num_inference_steps''',
           '''generator''',
           '''latents''',
           '''guidance_scale''',
           '''frame_size''',
           '''output_type''',
           '''return_dict''',
       ]
       _lowercase     :      List[str]  				=			False

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





         """simple docstring"""







         return 32

       @property
       def 				lowerCamelCase_			(							self:     Tuple							)      ->       str:





         """simple docstring"""







         return 32

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





         """simple docstring"""







         return self.time_input_dim * 4

       @property
       def 				lowerCamelCase_			(							self:     Dict							)      ->       str:





         """simple docstring"""







         return 8

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





         """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(_SCREAMING_SNAKE_CASE							)
         return model

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





         """simple docstring"""







         lowercase__						=				CLIPImageProcessor(
             crop_size=224   ,						do_center_crop=_SCREAMING_SNAKE_CASE   ,						do_normalize=_SCREAMING_SNAKE_CASE   ,						do_resize=_SCREAMING_SNAKE_CASE   ,						image_mean=[0.48145466, 0.4578275, 0.40821073]   ,						image_std=[0.26862954, 0.26130258, 0.27577711]   ,						resample=3   ,						size=224   ,						)

         return image_processor

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





         """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(**_SCREAMING_SNAKE_CASE							)
         return model

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





         """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(**_SCREAMING_SNAKE_CASE							)
         return model

       def 				lowerCamelCase_			(							self:     List[str]							)      ->       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=1_024   ,						prediction_type='''sample'''   ,						use_karras_sigmas=_SCREAMING_SNAKE_CASE   ,						clip_sample=_SCREAMING_SNAKE_CASE   ,						clip_sample_range=1.0   ,						)
         lowercase__						=				{
             "prior": prior,
             "image_encoder": image_encoder,
             "image_processor": image_processor,
             "renderer": renderer,
             "scheduler": scheduler,
         }

         return components

       def 				lowerCamelCase_			(							self:     Optional[int]   ,						UpperCamelCase_:     Optional[Any]   ,						UpperCamelCase_:     Dict=0							)      ->       Dict:





         """simple docstring"""







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

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

       def 				lowerCamelCase_			(							self:     str							)      ->       Optional[Any]:





         """simple docstring"""







         lowercase__						=				"cpu"

         lowercase__						=				self.get_dummy_components()

         lowercase__						=				self.pipeline_class(**_SCREAMING_SNAKE_CASE							)
         lowercase__						=				pipe.to(_SCREAMING_SNAKE_CASE							)

         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE							)

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

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

         lowercase__						=				np.array(
             [
                 0.00039216,
                 0.00039216,
                 0.00039216,
                 0.00039216,
                 0.00039216,
                 0.00039216,
                 0.00039216,
                 0.00039216,
                 0.00039216,
             ]							)

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

       def 				lowerCamelCase_			(							self:     Dict							)      ->       Any:





         """simple docstring"""







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

       def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       int:





         """simple docstring"""







         lowercase__						=				torch_device == "cpu"
         lowercase__						=				True
         self._test_inference_batch_single_identical(
             batch_size=2   ,						test_max_difference=_SCREAMING_SNAKE_CASE   ,						relax_max_difference=_SCREAMING_SNAKE_CASE   ,						)


       def 				lowerCamelCase_			(							self:     Tuple							)      ->       List[Any]:





         """simple docstring"""







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

         lowercase__						=				1
         lowercase__						=				2

         lowercase__						=				self.get_dummy_inputs(_SCREAMING_SNAKE_CASE							)

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

         lowercase__						=				pipe(**_SCREAMING_SNAKE_CASE   ,						num_images_per_prompt=_SCREAMING_SNAKE_CASE							)[0]

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


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

       def 				lowerCamelCase_			(							self:     Any							)      ->       str:





         """simple docstring"""







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


       def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       List[str]:





         """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(_SCREAMING_SNAKE_CASE							)
         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE							)

         lowercase__						=				torch.Generator(device=_SCREAMING_SNAKE_CASE							).manual_seed(0							)

         lowercase__						=				pipe(
             _SCREAMING_SNAKE_CASE   ,						generator=_SCREAMING_SNAKE_CASE   ,						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(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							)






from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass


@dataclass
class 	_a							:
							_lowercase     :      int
							_lowercase     :      TreeNode | None  				=			None
							_lowercase     :      TreeNode | None  				=			None
lowerCAmelCase	=							namedtuple('CoinsDistribResult', 'moves excess')
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		if root is None:
				return 0

		# Validation
		def count_nodes(SCREAMING_SNAKE_CASE					) -> int:
				if node is None:
						return 0

				return count_nodes(node.left					) + count_nodes(node.right					) + 1

		def count_coins(SCREAMING_SNAKE_CASE					) -> int:
				if node is None:
						return 0

				return count_coins(node.left					) + count_coins(node.right					) + node.data

		if count_nodes(SCREAMING_SNAKE_CASE					) != count_coins(SCREAMING_SNAKE_CASE					):
				raise ValueError('''The nodes number should be same as the number of coins'''					)

		# Main calculation
		def get_distrib(SCREAMING_SNAKE_CASE					) -> CoinsDistribResult:

				if node is None:
						return CoinsDistribResult(0						,    1					)

				lowercase__					,		lowercase__						=				get_distrib(node.left					)
				lowercase__					,		lowercase__						=				get_distrib(node.right					)

				lowercase__						=				1 - left_distrib_excess
				lowercase__						=				1 - right_distrib_excess

				lowercase__						=				(
				    left_distrib_moves
				    + right_distrib_moves
				    + abs(SCREAMING_SNAKE_CASE					)
				    + abs(SCREAMING_SNAKE_CASE					)
				)
				lowercase__						=				node.data - coins_to_left - coins_to_right

				return CoinsDistribResult(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)

		return get_distrib(SCREAMING_SNAKE_CASE					)[0]


if __name__ == "__main__":
			import doctest

			doctest.testmod()


