


'''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__			(			UpperCAmelCase__    ,       unittest.TestCase	):
			lowerCAmelCase :	Optional[int]          =     ShapEImgaImgPipeline
			lowerCAmelCase :	int          =     ["image"]
			lowerCAmelCase :	Optional[int]          =     ["image"]
			lowerCAmelCase :	str          =     [
			    "num_images_per_prompt",
			    "num_inference_steps",
			    "generator",
			    "latents",
			    "guidance_scale",
			    "frame_size",
			    "output_type",
			    "return_dict",
			]
			lowerCAmelCase :	Optional[Any]          =     False




			@property
			def 							lowerCAmelCase__    (		self  :     Any )   ->List[str]:



					'''simple docstring'''







					return 32




			@property
			def 							lowerCAmelCase__    (		self  :     str )   ->str:



					'''simple docstring'''







					return 32




			@property
			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->Optional[Any]:



					'''simple docstring'''







					return self.time_input_dim * 4




			@property
			def 							lowerCAmelCase__    (		self  :     Tuple )   ->Optional[int]:



					'''simple docstring'''







					return 8




			@property
			def 							lowerCAmelCase__    (		self  :     Tuple )   ->Any:



					'''simple docstring'''







					torch.manual_seed(0 )
					_UpperCAmelCase		:       Union[str, Any]								=      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		:       List[str]								=      CLIPVisionModel(lowerCamelCase__ )
					return model




			@property
			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      CLIPImageProcessor(
					    crop_size=2_24	,  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=2_24	,  )

					return image_processor




			@property
			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->List[str]:



					'''simple docstring'''







					torch.manual_seed(0 )

					_UpperCAmelCase		:       List[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		:       Tuple								=      PriorTransformer(**lowerCamelCase__ )
					return model




			@property
			def 							lowerCAmelCase__    (		self  :     List[Any] )   ->Optional[Any]:



					'''simple docstring'''







					torch.manual_seed(0 )

					_UpperCAmelCase		:       Union[str, 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		:       Dict								=      ShapERenderer(**lowerCamelCase__ )
					return model




			def 							lowerCAmelCase__    (		self  :     int )   ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      self.dummy_prior
					_UpperCAmelCase		:       str								=      self.dummy_image_encoder
					_UpperCAmelCase		:       Any								=      self.dummy_image_processor
					_UpperCAmelCase		:       Union[str, Any]								=      self.dummy_renderer

					_UpperCAmelCase		:       str								=      HeunDiscreteScheduler(
					    beta_schedule="exp"	,  num_train_timesteps=10_24	,  prediction_type="sample"	,  use_karras_sigmas=lowerCamelCase__	,  clip_sample=lowerCamelCase__	,  clip_sample_range=1.0	,  )
					_UpperCAmelCase		:       List[Any]								=      {
					    "prior": prior,
					    "image_encoder": image_encoder,
					    "image_processor": image_processor,
					    "renderer": renderer,
					    "scheduler": scheduler,
					}

					return components




			def 							lowerCAmelCase__    (		self  :     List[str]	,  lowerCamelCase__  :     Optional[Any]	,  lowerCamelCase__  :     Optional[int]=0 )   ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Tuple								=      floats_tensor((1, 3, 64, 64)	,  rng=random.Random(lowerCamelCase__ ) ).to(lowerCamelCase__ )

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




			def 							lowerCAmelCase__    (		self  :     List[str] )   ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:       List[str]								=      "cpu"

					_UpperCAmelCase		:       int								=      self.get_dummy_components()

					_UpperCAmelCase		:       List[str]								=      self.pipeline_class(**lowerCamelCase__ )
					_UpperCAmelCase		:       Dict								=      pipe.to(lowerCamelCase__ )

					pipe.set_progress_bar_config(disable=lowerCamelCase__ )

					_UpperCAmelCase		:       Tuple								=      pipe(**self.get_dummy_inputs(lowerCamelCase__ ) )
					_UpperCAmelCase		:       Optional[int]								=      output.images[0]
					_UpperCAmelCase		:       Optional[Any]								=      image[0, -3:, -3:, -1]

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

					_UpperCAmelCase		:       Dict								=      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 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->str:



					'''simple docstring'''







					self._test_inference_batch_consistent(batch_sizes=[1, 2] )




			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       int								=      torch_device == "cpu"
					_UpperCAmelCase		:       Dict								=      True
					self._test_inference_batch_single_identical(
					    batch_size=2	,  test_max_difference=lowerCamelCase__	,  relax_max_difference=lowerCamelCase__	,  )






			def 							lowerCAmelCase__    (		self  :     int )   ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      self.get_dummy_components()
					_UpperCAmelCase		:       List[str]								=      self.pipeline_class(**lowerCamelCase__ )
					_UpperCAmelCase		:       Tuple								=      pipe.to(lowerCamelCase__ )
					pipe.set_progress_bar_config(disable=lowerCamelCase__ )

					_UpperCAmelCase		:       Tuple								=      1
					_UpperCAmelCase		:       Tuple								=      2

					_UpperCAmelCase		:       List[str]								=      self.get_dummy_inputs(lowerCamelCase__ )

					for key in inputs.keys():
							if key in self.batch_params:
									_UpperCAmelCase		:       str								=      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 	lowerCAmelCase__			(			unittest.TestCase	):




			def 							lowerCAmelCase__    (		self  :     Union[str, Any] )   ->Optional[int]:



					'''simple docstring'''







					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/shap_e/corgi.png" )
					_UpperCAmelCase		:       List[Any]								=      load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
					    "/shap_e/test_shap_e_img2img_out.npy" )
					_UpperCAmelCase		:       str								=      ShapEImgaImgPipeline.from_pretrained("openai/shap-e-img2img" )
					_UpperCAmelCase		:       str								=      pipe.to(lowerCamelCase__ )
					pipe.set_progress_bar_config(disable=lowerCamelCase__ )

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

					_UpperCAmelCase		:       Tuple								=      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 __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):
		return math.sqrt(sum(pow(a - b      , 2   ) for a, b in zip(__lowerCAmelCase      , __lowerCAmelCase   )   )   )






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):

		if dataset.ndim != value_array.ndim:
				_UpperCAmelCase		:       Optional[Any]								=      (
				    "Wrong input data's dimensions... "
				    F"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
				)
				raise ValueError(__lowerCAmelCase   )

		try:
				if dataset.shape[1] != value_array.shape[1]:
						_UpperCAmelCase		:       Optional[int]								=      (
						    "Wrong input data's shape... "
						    F"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
						)
						raise ValueError(__lowerCAmelCase   )
		except IndexError:
				if dataset.ndim != value_array.ndim:
						raise TypeError("Wrong shape"   )

		if dataset.dtype != value_array.dtype:
				_UpperCAmelCase		:       Union[str, Any]								=      (
				    "Input data have different datatype... "
				    F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
				)
				raise TypeError(__lowerCAmelCase   )

		_UpperCAmelCase		:       Optional[int]								=      []

		for value in value_array:
				_UpperCAmelCase		:       List[str]								=      euclidean(__lowerCAmelCase      , dataset[0]   )
				_UpperCAmelCase		:       Dict								=      dataset[0].tolist()

				for dataset_value in dataset[1:]:
						_UpperCAmelCase		:       int								=      euclidean(__lowerCAmelCase      , __lowerCAmelCase   )

						if dist > temp_dist:
								_UpperCAmelCase		:       Tuple								=      temp_dist
								_UpperCAmelCase		:       Dict								=      dataset_value.tolist()

				answer.append([vector, dist]   )

		return answer






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):
		return np.dot(__lowerCAmelCase      , __lowerCAmelCase   ) / (norm(__lowerCAmelCase   ) * norm(__lowerCAmelCase   ))


if __name__ == "__main__":
						import doctest

						doctest.testmod()



