





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



	@property
	def 	_lowerCamelCase     ( self						):
			return 32



	@property
	def 	_lowerCamelCase     ( self						):
			return 32



	@property
	def 	_lowerCamelCase     ( self						):
			return self.time_input_dim * 4



	@property
	def 	_lowerCamelCase     ( self						):
			return 8



	@property
	def 	_lowerCamelCase     ( self						):
			torch.manual_seed(0						)
			lowerCAmelCase  :     int       						= 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		,   )

			lowerCAmelCase  :     Optional[int]       						= CLIPVisionModel(UpperCamelCase_						)
			return model



	@property
	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= CLIPImageProcessor(
			    crop_size=224		,   do_center_crop=UpperCamelCase_		,   do_normalize=UpperCamelCase_		,   do_resize=UpperCamelCase_		,   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						):
			torch.manual_seed(0						)

			lowerCAmelCase  :     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,
			}

			lowerCAmelCase  :     Union[str, Any]       						= PriorTransformer(**UpperCamelCase_						)
			return model



	@property
	def 	_lowerCamelCase     ( self						):
			torch.manual_seed(0						)

			lowerCAmelCase  :     Any       						= {
			    "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,
			    ),
			}
			lowerCAmelCase  :     Tuple       						= ShapERenderer(**UpperCamelCase_						)
			return model



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     str       						= self.dummy_prior
			lowerCAmelCase  :     Tuple       						= self.dummy_image_encoder
			lowerCAmelCase  :     Union[str, Any]       						= self.dummy_image_processor
			lowerCAmelCase  :     Optional[Any]       						= self.dummy_renderer

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

			return components



	def 	_lowerCamelCase     ( self		,   a_		,   a_=0						):
			lowerCAmelCase  :     str       						= floats_tensor((1, 3, 64, 64)		,   rng=random.Random(UpperCamelCase_						)						).to(UpperCamelCase_						)

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



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Dict       						= "cpu"

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

			lowerCAmelCase  :     Tuple       						= self.pipeline_class(**UpperCamelCase_						)
			lowerCAmelCase  :     Any       						= pipe.to(UpperCamelCase_						)

			pipe.set_progress_bar_config(disable=UpperCamelCase_						)

			lowerCAmelCase  :     str       						= pipe(**self.get_dummy_inputs(UpperCamelCase_						)						)
			lowerCAmelCase  :     Tuple       						= output.images[0]
			lowerCAmelCase  :     List[Any]       						= image[0, -3:, -3:, -1]

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

			lowerCAmelCase  :     List[Any]       						= 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						):
			self._test_inference_batch_consistent(batch_sizes=[1, 2]						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Dict       						= torch_device == "cpu"
			lowerCAmelCase  :     List[str]       						= True
			self._test_inference_batch_single_identical(
			    batch_size=2		,   test_max_difference=UpperCamelCase_		,   relax_max_difference=UpperCamelCase_		,   )





	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Optional[Any]       						= self.get_dummy_components()
			lowerCAmelCase  :     Dict       						= self.pipeline_class(**UpperCamelCase_						)
			lowerCAmelCase  :     List[Any]       						= pipe.to(UpperCamelCase_						)
			pipe.set_progress_bar_config(disable=UpperCamelCase_						)

			lowerCAmelCase  :     str       						= 1
			lowerCAmelCase  :     Optional[int]       						= 2

			lowerCAmelCase  :     Dict       						= self.get_dummy_inputs(UpperCamelCase_						)

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

			lowerCAmelCase  :     List[Any]       						= 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 	_lowerCamelCase     ( self						):
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()





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

			lowerCAmelCase  :     Any       						= torch.Generator(device=UpperCamelCase_						).manual_seed(0						)

			lowerCAmelCase  :     int       						= pipe(
			    UpperCamelCase_		,   generator=UpperCamelCase_		,   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(UpperCamelCase_		,   UpperCamelCase_						)











def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[Any]     )    ->   Any:





    """simple docstring"""

    stooge(__lowercase      ,					0      ,					len(__lowercase     ) - 1     )
    return arr



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      Dict      ,					__lowercase :      Dict     )    ->   int:





    """simple docstring"""

    if i >= h:
        return

    # If first element is smaller than the last then swap them
    if arr[i] > arr[h]:
        __A     ,						__A       =						arr[h], arr[i]

    # If there are more than 2 elements in the array
    if h - i + 1 > 2:
        __A       =						(int)((h - i + 1) / 3     )

        # Recursively sort first 2/3 elements
        stooge(__lowercase      ,					__lowercase      ,					(h - t)     )

        # Recursively sort last 2/3 elements
        stooge(__lowercase      ,					i + t      ,					(__lowercase)     )

        # Recursively sort first 2/3 elements
        stooge(__lowercase      ,					__lowercase      ,					(h - t)     )


if __name__ == "__main__":
  __a   :							List[Any]						= input("Enter numbers separated by a comma:\n").strip()
  __a   :							List[str]						= [int(item) for item in user_input.split(",")]
  print(stooge_sort(unsorted))




