






"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey

def        snake_case   (     A__			,A__			,A__			,A__			,A__			,A__		):

			# prepare kernel
			# the kernel size have to be odd
			if (ksize % 2) == 0:
						UpperCAmelCase_  :				List[Any]      =      ksize + 1
			UpperCAmelCase_  :				Optional[Any]      =      np.zeros((ksize, ksize)			,dtype=np.floataa		)

			# each value
			for y in range(A__		):
						for x in range(A__		):
									# distance from center
									UpperCAmelCase_  :				Tuple      =      x - ksize // 2
									UpperCAmelCase_  :				Any      =      y - ksize // 2

									# degree to radiant
									UpperCAmelCase_  :				int      =      theta / 1_80 * np.pi
									UpperCAmelCase_  :				Optional[int]      =      np.cos(_theta		)
									UpperCAmelCase_  :				Union[str, Any]      =      np.sin(_theta		)

									# get kernel x
									UpperCAmelCase_  :				Tuple      =      cos_theta * px + sin_theta * py

									# get kernel y
									UpperCAmelCase_  :				List[str]      =      -sin_theta * px + cos_theta * py

									# fill kernel
									UpperCAmelCase_  :				Dict      =      np.exp(
									    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)		) * np.cos(2 * np.pi * _x / lambd + psi		)

			return gabor


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			# read original image
			lowerCamelCase_       			=   imread('''../image_data/lena.jpg''')
			# turn image in gray scale value
			lowerCamelCase_       			=   cvtColor(img, COLOR_BGR2GRAY)

			# Apply multiple Kernel to detect edges
			lowerCamelCase_       			=   np.zeros(gray.shape[:2])
			for theta in [0, 30, 60, 90, 120, 150]:
						lowerCamelCase_       			=   gabor_filter_kernel(10, 8, theta, 10, 0, 0)
						out += filteraD(gray, CV_8UC3, kernel_aa)
			lowerCamelCase_       			=   out / out.max() * 255
			lowerCamelCase_       			=   out.astype(np.uinta)

			imshow('''Original''', gray)
			imshow('''Gabor filter with 20x20 mask and 6 directions''', out)

			waitKey(0)











"""simple docstring"""
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import 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__			   =       ShapEPipeline
	__magic_name__			   =       ['''prompt''']
	__magic_name__			   =       ['''prompt''']
	__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      :				Union[str, Any]		)		->    Dict:
				return 32





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Tuple:
				return 32





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





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    List[Any]:
				return 8





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    int:
				UpperCAmelCase_  :				str      =      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)
				return tokenizer





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    List[str]:
				torch.manual_seed(0		)
				UpperCAmelCase_  :				Dict      =      CLIPTextConfig(
				    bos_token_id=0			,			eos_token_id=2			,			hidden_size=self.text_embedder_hidden_size			,			projection_dim=self.text_embedder_hidden_size			,			intermediate_size=37			,			layer_norm_eps=1e-05			,			num_attention_heads=4			,			num_hidden_layers=5			,			pad_token_id=1			,			vocab_size=1_000			,			)
				return CLIPTextModelWithProjection(lowerCAmelCase_		)





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

				UpperCAmelCase_  :				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",
				    "encoder_hid_proj_type": None,
				    "added_emb_type": None,
				}

				UpperCAmelCase_  :				List[Any]      =      PriorTransformer(**lowerCAmelCase_		)
				return model





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

				UpperCAmelCase_  :				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,
				    ),
				}
				UpperCAmelCase_  :				Tuple      =      ShapERenderer(**lowerCAmelCase_		)
				return model





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Tuple:
				UpperCAmelCase_  :				str      =      self.dummy_prior
				UpperCAmelCase_  :				Dict      =      self.dummy_text_encoder
				UpperCAmelCase_  :				Any      =      self.dummy_tokenizer
				UpperCAmelCase_  :				int      =      self.dummy_renderer

				UpperCAmelCase_  :				Optional[int]      =      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_  :				str      =      {
				    "prior": prior,
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "renderer": renderer,
				    "scheduler": scheduler,
				}

				return components





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Any=0		)		->    Union[str, Any]:
				if str(lowerCAmelCase_		).startswith("mps"		):
							UpperCAmelCase_  :				str      =      torch.manual_seed(lowerCAmelCase_		)
				else:
							UpperCAmelCase_  :				Union[str, Any]      =      torch.Generator(device=lowerCAmelCase_		).manual_seed(lowerCAmelCase_		)
				UpperCAmelCase_  :				List[Any]      =      {
				    "prompt": "horse",
				    "generator": generator,
				    "num_inference_steps": 1,
				    "frame_size": 32,
				    "output_type": "np",
				}
				return inputs





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

				UpperCAmelCase_  :				Union[str, Any]      =      self.get_dummy_components()

				UpperCAmelCase_  :				Tuple      =      self.pipeline_class(**lowerCAmelCase_		)
				UpperCAmelCase_  :				Union[str, Any]      =      pipe.to(lowerCAmelCase_		)

				pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

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

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

				UpperCAmelCase_  :				Optional[int]      =      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]		)		->    Optional[Any]:
				# 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      :				int		)		->    List[str]:
				UpperCAmelCase_  :				Optional[Any]      =      torch_device == "cpu"
				UpperCAmelCase_  :				Dict      =      True

				self._test_inference_batch_single_identical(
				    batch_size=2			,			test_max_difference=lowerCAmelCase_			,			relax_max_difference=lowerCAmelCase_			,			)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    List[str]:
				UpperCAmelCase_  :				int      =      self.get_dummy_components()
				UpperCAmelCase_  :				Tuple      =      self.pipeline_class(**lowerCAmelCase_		)
				UpperCAmelCase_  :				List[str]      =      pipe.to(lowerCAmelCase_		)
				pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

				UpperCAmelCase_  :				List[str]      =      1
				UpperCAmelCase_  :				List[Any]      =      2

				UpperCAmelCase_  :				int      =      self.get_dummy_inputs(lowerCAmelCase_		)

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

				UpperCAmelCase_  :				int      =      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      :				Union[str, Any]		)		->    Any:
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    Union[str, Any]:
				UpperCAmelCase_  :				Any      =      load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/shap_e/test_shap_e_np_out.npy"		)
				UpperCAmelCase_  :				str      =      ShapEPipeline.from_pretrained("openai/shap-e"		)
				UpperCAmelCase_  :				Union[str, Any]      =      pipe.to(lowerCAmelCase_		)
				pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

				UpperCAmelCase_  :				Union[str, Any]      =      torch.Generator(device=lowerCAmelCase_		).manual_seed(0		)

				UpperCAmelCase_  :				Optional[int]      =      pipe(
				    "a shark"			,			generator=lowerCAmelCase_			,			guidance_scale=1_5.0			,			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_		)



