






"""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     UpperCamelCase_ (__A       ,		unittest.TestCase  ):
 __magic_name__			   =       ShapEImgaImgPipeline
 __magic_name__			   =       ['''image''']
 __magic_name__			   =       ['''image''']
 __magic_name__			   =       [
     '''num_images_per_prompt''',
     '''num_inference_steps''',
     '''generator''',
     '''latents''',
     '''guidance_scale''',
     '''frame_size''',
     '''output_type''',
     '''return_dict''',
 ]
 __magic_name__			   =       False





 @property
 def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Dict:
    return 32





 @property
 def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Dict:
    return 32





 @property
 def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Dict:
    return self.time_input_dim * 4





 @property
 def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    Tuple:
    return 8





 @property
 def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    Dict:
    torch.manual_seed(0		)
    UpperCAmelCase_  :				str      =      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			,			)

    UpperCAmelCase_  :				Any      =      CLIPVisionModel(lowerCAmelCase_		)
    return model





 @property
 def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    Any:
    UpperCAmelCase_  :				Union[str, 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      :				List[Any]		)		->    Tuple:
    torch.manual_seed(0		)

    UpperCAmelCase_  :				List[str]      =      {
        "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,
    }

    UpperCAmelCase_  :				Union[str, Any]      =      PriorTransformer(**lowerCAmelCase_		)
    return model





 @property
 def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Union[str, Any]:
    torch.manual_seed(0		)

    UpperCAmelCase_  :				Optional[int]      =      {
        "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,
        ),
    }
    UpperCAmelCase_  :				Optional[Any]      =      ShapERenderer(**lowerCAmelCase_		)
    return model





 def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    int:
    UpperCAmelCase_  :				Dict      =      self.dummy_prior
    UpperCAmelCase_  :				str      =      self.dummy_image_encoder
    UpperCAmelCase_  :				List[str]      =      self.dummy_image_processor
    UpperCAmelCase_  :				List[str]      =      self.dummy_renderer

    UpperCAmelCase_  :				Any      =      HeunDiscreteScheduler(
        beta_schedule="exp"			,			num_train_timesteps=1_024			,			prediction_type="sample"			,			use_karras_sigmas=lowerCAmelCase_			,			clip_sample=lowerCAmelCase_			,			clip_sample_range=1.0			,			)
    UpperCAmelCase_  :				Optional[Any]      =      {
        "prior": prior,
        "image_encoder": image_encoder,
        "image_processor": image_processor,
        "renderer": renderer,
        "scheduler": scheduler,
    }

    return components





 def   _SCREAMING_SNAKE_CASE   (   self      :				int			,			lowerCAmelCase_      :				List[Any]			,			lowerCAmelCase_      :				Union[str, Any]=0		)		->    str:
    UpperCAmelCase_  :				Tuple      =      floats_tensor((1, 3, 64, 64)			,			rng=random.Random(lowerCAmelCase_		)		).to(lowerCAmelCase_		)

    if str(lowerCAmelCase_		).startswith("mps"		):
       UpperCAmelCase_  :				Optional[int]      =      torch.manual_seed(lowerCAmelCase_		)
    else:
       UpperCAmelCase_  :				Optional[Any]      =      torch.Generator(device=lowerCAmelCase_		).manual_seed(lowerCAmelCase_		)
    UpperCAmelCase_  :				Tuple      =      {
        "image": input_image,
        "generator": generator,
        "num_inference_steps": 1,
        "frame_size": 32,
        "output_type": "np",
    }
    return inputs





 def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    str:
    UpperCAmelCase_  :				Optional[int]      =      "cpu"

    UpperCAmelCase_  :				List[Any]      =      self.get_dummy_components()

    UpperCAmelCase_  :				Optional[Any]      =      self.pipeline_class(**lowerCAmelCase_		)
    UpperCAmelCase_  :				Optional[Any]      =      pipe.to(lowerCAmelCase_		)

    pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

    UpperCAmelCase_  :				Dict      =      pipe(**self.get_dummy_inputs(lowerCAmelCase_		)		)
    UpperCAmelCase_  :				List[Any]      =      output.images[0]
    UpperCAmelCase_  :				Optional[int]      =      image[0, -3:, -3:, -1]

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

    UpperCAmelCase_  :				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      :				int		)		->    int:
    # 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      :				Optional[Any]		)		->    Optional[int]:
    UpperCAmelCase_  :				Tuple      =      torch_device == "cpu"
    UpperCAmelCase_  :				Optional[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[Any]		)		->    List[Any]:
    UpperCAmelCase_  :				Dict      =      self.get_dummy_components()
    UpperCAmelCase_  :				Optional[int]      =      self.pipeline_class(**lowerCAmelCase_		)
    UpperCAmelCase_  :				Optional[int]      =      pipe.to(lowerCAmelCase_		)
    pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

    UpperCAmelCase_  :				Optional[Any]      =      1
    UpperCAmelCase_  :				Any      =      2

    UpperCAmelCase_  :				Any      =      self.get_dummy_inputs(lowerCAmelCase_		)

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

    UpperCAmelCase_  :				Union[str, Any]      =      pipe(**lowerCAmelCase_			,			num_images_per_prompt=lowerCAmelCase_		)[0]

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







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





 def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Dict:
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()





 def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    str:
    UpperCAmelCase_  :				Dict      =      load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/shap_e/corgi.png"		)
    UpperCAmelCase_  :				Any      =      load_numpy(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/shap_e/test_shap_e_img2img_out.npy"		)
    UpperCAmelCase_  :				List[str]      =      ShapEImgaImgPipeline.from_pretrained("openai/shap-e-img2img"		)
    UpperCAmelCase_  :				Optional[int]      =      pipe.to(lowerCAmelCase_		)
    pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

    UpperCAmelCase_  :				Tuple      =      torch.Generator(device=lowerCAmelCase_		).manual_seed(0		)

    UpperCAmelCase_  :				List[Any]      =      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_		)











"""simple docstring"""
from itertools import count

def        snake_case   (     A__ = 50		):
			UpperCAmelCase_  :				Any      =      [1] * min_block_length

			for n in count(A__		):
						fill_count_functions.append(1		)

						for block_length in range(A__			,n + 1		):
									for block_start in range(n - block_length		):
												fill_count_functions[n] += fill_count_functions[
												    n - block_start - block_length - 1
												]

									fill_count_functions[n] += 1

						if fill_count_functions[n] > 1_00_00_00:
									break

			return n


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



