






"""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   :					Tuple		)		->     int:
				return 32





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





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





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Optional[int]:
				return 8





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     str:
				torch.manual_seed(0		)
				UpperCAmelCase_	:					Tuple     	=   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_	:					Union[str, Any]     	=   CLIPVisionModel(lowerCAmelCase_		)
				return model





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

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





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Union[str, Any]:
				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_	:					List[Any]     	=   ShapERenderer(**lowerCAmelCase_		)
				return model





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

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

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





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

				UpperCAmelCase_	:					str     	=   self.get_dummy_components()

				UpperCAmelCase_	:					List[str]     	=   self.pipeline_class(**lowerCAmelCase_		)
				UpperCAmelCase_	:					int     	=   pipe.to(lowerCAmelCase_		)

				pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

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

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

				UpperCAmelCase_	:					str     	=   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		)		->     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   :					Dict		)		->     Union[str, Any]:
				UpperCAmelCase_	:					Optional[int]     	=   torch_device == "cpu"
				UpperCAmelCase_	:					List[str]     	=   True
				self._test_inference_batch_single_identical(
				    batch_size=2      ,      test_max_difference=lowerCAmelCase_      ,      relax_max_difference=lowerCAmelCase_      ,      )





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

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

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

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

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





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

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

				UpperCAmelCase_	:					Optional[int]     	=   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"""
def 							snake_case (  A__		):
			UpperCAmelCase_	:					int     	=   [0 for i in range(len(A__		)		)]

			# initialize interval's left pointer and right pointer
			UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   0, 0

			for i in range(1      ,len(A__		)		):
						# case when current index is inside the interval
						if i <= right_pointer:
									UpperCAmelCase_	:					Any     	=   min(right_pointer - i + 1      ,z_result[i - left_pointer]		)
									UpperCAmelCase_	:					Union[str, Any]     	=   min_edge

						while go_next(A__      ,A__      ,A__		):
									z_result[i] += 1

						# if new index's result gives us more right interval,
						# we've to update left_pointer and right_pointer
						if i + z_result[i] - 1 > right_pointer:
									UpperCAmelCase_	,      UpperCAmelCase_	:					Union[str, Any]     	=   i, i + z_result[i] - 1

			return z_result

def 							snake_case (  A__      ,A__      ,A__		):
			return i + z_result[i] < len(A__		) and s[z_result[i]] == s[i + z_result[i]]

def 							snake_case (  A__      ,A__		):
			UpperCAmelCase_	:					Optional[int]     	=   0
			# concatenate 'pattern' and 'input_str' and call z_function
			# with concatenated string
			UpperCAmelCase_	:					Tuple     	=   z_function(pattern + input_str		)

			for val in z_result:
						# if value is greater then length of the pattern string
						# that means this index is starting position of substring
						# which is equal to pattern string
						if val >= len(A__		):
									answer += 1

			return answer


if __name__ == "__main__":
			import doctest

			doctest.testmod()



