


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__   (       __SCREAMING_SNAKE_CASE   ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__      			=			ShapEImgaImgPipeline
     SCREAMING_SNAKE_CASE__      			=			['''image''']
     SCREAMING_SNAKE_CASE__      			=			['''image''']
     SCREAMING_SNAKE_CASE__      			=			[
         '''num_images_per_prompt''',
         '''num_inference_steps''',
         '''generator''',
         '''latents''',
         '''guidance_scale''',
         '''frame_size''',
         '''output_type''',
         '''return_dict''',
     ]
     SCREAMING_SNAKE_CASE__      			=			False





     @property
     def      __A					(self       )     ->   Optional[Any]:
      return 3_2





     @property
     def      __A					(self       )     ->   int:
      return 3_2





     @property
     def      __A					(self       )     ->   Dict:
      return self.time_input_dim * 4





     @property
     def      __A					(self       )     ->   Optional[Any]:
      return 8





     @property
     def      __A					(self       )     ->   List[Any]:
      torch.manual_seed(0       )
      _lowercase							      =CLIPVisionConfig(
          hidden_size=self.text_embedder_hidden_size		,     image_size=6_4		,     projection_dim=self.text_embedder_hidden_size		,     intermediate_size=3_7		,     num_attention_heads=4		,     num_channels=3		,     num_hidden_layers=5		,     patch_size=1		,     )

      _lowercase							      =CLIPVisionModel(UpperCAmelCase_       )
      return model





     @property
     def      __A					(self       )     ->   Tuple:
      _lowercase							      =CLIPImageProcessor(
          crop_size=2_2_4		,     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_2_4		,     )

      return image_processor





     @property
     def      __A					(self       )     ->   int:
      torch.manual_seed(0       )

      _lowercase							      ={
          "num_attention_heads": 2,
          "attention_head_dim": 1_6,
          "embedding_dim": self.time_input_dim,
          "num_embeddings": 3_2,
          "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(**UpperCAmelCase_       )
      return model





     @property
     def      __A					(self       )     ->   List[Any]:
      torch.manual_seed(0       )

      _lowercase							      ={
          "param_shapes": (
              (self.renderer_dim, 9_3),
              (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": 1_2,
          "background": (
              0.1,
              0.1,
              0.1,
          ),
      }
      _lowercase							      =ShapERenderer(**UpperCAmelCase_       )
      return model





     def      __A					(self       )     ->   List[str]:
      _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_0_2_4		,     prediction_type='''sample'''		,     use_karras_sigmas=UpperCAmelCase_		,     clip_sample=UpperCAmelCase_		,     clip_sample_range=1.0		,     )
      _lowercase							      ={
          "prior": prior,
          "image_encoder": image_encoder,
          "image_processor": image_processor,
          "renderer": renderer,
          "scheduler": scheduler,
      }

      return components





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase=0       )     ->   Optional[Any]:
      _lowercase							      =floats_tensor((1, 3, 6_4, 6_4)		,     rng=random.Random(UpperCAmelCase_       )       ).to(UpperCAmelCase_       )

      if str(UpperCAmelCase_       ).startswith('''mps'''       ):
       _lowercase							      =torch.manual_seed(UpperCAmelCase_       )
      else:
       _lowercase							      =torch.Generator(device=UpperCAmelCase_       ).manual_seed(UpperCAmelCase_       )
      _lowercase							      ={
          "image": input_image,
          "generator": generator,
          "num_inference_steps": 1,
          "frame_size": 3_2,
          "output_type": "np",
      }
      return inputs





     def      __A					(self       )     ->   Optional[Any]:
      _lowercase							      ="cpu"

      _lowercase							      =self.get_dummy_components()

      _lowercase							      =self.pipeline_class(**UpperCAmelCase_       )
      _lowercase							      =pipe.to(UpperCAmelCase_       )

      pipe.set_progress_bar_config(disable=UpperCAmelCase_       )

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

      assert image.shape == (2_0, 3_2, 3_2, 3)

      _lowercase							      =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      __A					(self       )     ->   Optional[int]:
      self._test_inference_batch_consistent(batch_sizes=[1, 2]       )





     def      __A					(self       )     ->   Dict:
      _lowercase							      =torch_device == "cpu"
      _lowercase							      =True
      self._test_inference_batch_single_identical(
          batch_size=2		,     test_max_difference=UpperCAmelCase_		,     relax_max_difference=UpperCAmelCase_		,     )





     def      __A					(self       )     ->   Optional[Any]:
      _lowercase							      =self.get_dummy_components()
      _lowercase							      =self.pipeline_class(**UpperCAmelCase_       )
      _lowercase							      =pipe.to(UpperCAmelCase_       )
      pipe.set_progress_bar_config(disable=UpperCAmelCase_       )

      _lowercase							      =1
      _lowercase							      =2

      _lowercase							      =self.get_dummy_inputs(UpperCAmelCase_       )

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

      _lowercase							      =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      __A					(self       )     ->   Dict:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()





     def      __A					(self       )     ->   Union[str, Any]:
      _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(UpperCAmelCase_       )
      pipe.set_progress_bar_config(disable=UpperCAmelCase_       )

      _lowercase							      =torch.Generator(device=UpperCAmelCase_       ).manual_seed(0       )

      _lowercase							      =pipe(
          UpperCAmelCase_		,     generator=UpperCAmelCase_		,     guidance_scale=3.0		,     num_inference_steps=6_4		,     frame_size=6_4		,     output_type='''np'''		,     ).images[0]

      assert images.shape == (2_0, 6_4, 6_4, 3)

      assert_mean_pixel_difference(UpperCAmelCase_		,     UpperCAmelCase_       )








from math import ceil, sqrt





def      lowerCAmelCase_ (       __a = 1000000		)     ->		int:







  """simple docstring"""





  lowerCamelCase__:      Optional[int]			  =0

  for outer_width in range(3	,    (limit // 4) + 2		):
    if outer_width**2 > limit:
      lowerCamelCase__:      Dict			  =max(ceil(sqrt(outer_width**2 - limit		)		)	,    1		)
    else:
      lowerCamelCase__:      str			  =1
    if (outer_width - hole_width_lower_bound) % 2:
      hole_width_lower_bound += 1

    answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

  return answer


if __name__ == "__main__":
     print(f'{solution() = }')

