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 SCREAMING_SNAKE_CASE_						(	__UpperCAmelCase   ,			unittest.TestCase						):
   __lowerCAmelCase      =							ShapEImgaImgPipeline
   __lowerCAmelCase      =							["""image"""]
   __lowerCAmelCase      =							["""image"""]
   __lowerCAmelCase      =							[
       """num_images_per_prompt""",
       """num_inference_steps""",
       """generator""",
       """latents""",
       """guidance_scale""",
       """frame_size""",
       """output_type""",
       """return_dict""",
   ]
   __lowerCAmelCase      =							False







   @property
   def 		lowerCamelCase_     (			self		:		List[str]							):





    """simple docstring"""


    return 32







   @property
   def 		lowerCamelCase_     (			self		:		str							):





    """simple docstring"""


    return 32







   @property
   def 		lowerCamelCase_     (			self		:		int							):





    """simple docstring"""


    return self.time_input_dim * 4







   @property
   def 		lowerCamelCase_     (			self		:		List[Any]							):





    """simple docstring"""


    return 8







   @property
   def 		lowerCamelCase_     (			self		:		Optional[int]							):





    """simple docstring"""


    torch.manual_seed(0							)
    UpperCamelCase          =	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          =	CLIPVisionModel(lowerCamelCase__							)
    return model







   @property
   def 		lowerCamelCase_     (			self		:		Optional[Any]							):





    """simple docstring"""


    UpperCamelCase          =	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 		lowerCamelCase_     (			self		:		str							):





    """simple docstring"""


    torch.manual_seed(0							)

    UpperCamelCase          =	{
        """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          =	PriorTransformer(**lowerCamelCase__							)
    return model







   @property
   def 		lowerCamelCase_     (			self		:		Tuple							):





    """simple docstring"""


    torch.manual_seed(0							)

    UpperCamelCase          =	{
        """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          =	ShapERenderer(**lowerCamelCase__							)
    return model







   def 		lowerCamelCase_     (			self		:		Any							):





    """simple docstring"""


    UpperCamelCase          =	self.dummy_prior
    UpperCamelCase          =	self.dummy_image_encoder
    UpperCamelCase          =	self.dummy_image_processor
    UpperCamelCase          =	self.dummy_renderer

    UpperCamelCase          =	HeunDiscreteScheduler(
        beta_schedule="""exp"""					,					num_train_timesteps=1024					,					prediction_type="""sample"""					,					use_karras_sigmas=lowerCamelCase__					,					clip_sample=lowerCamelCase__					,					clip_sample_range=1.0					,					)
    UpperCamelCase          =	{
        """prior""": prior,
        """image_encoder""": image_encoder,
        """image_processor""": image_processor,
        """renderer""": renderer,
        """scheduler""": scheduler,
    }

    return components







   def 		lowerCamelCase_     (			self		:		Optional[int]					,					lowerCamelCase_		:		Any					,					lowerCamelCase_		:		str=0							):





    """simple docstring"""


    UpperCamelCase          =	floats_tensor((1, 3, 64, 64)					,					rng=random.Random(lowerCamelCase__							)							).to(lowerCamelCase__							)

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







   def 		lowerCamelCase_     (			self		:		Optional[int]							):





    """simple docstring"""


    UpperCamelCase          =	"""cpu"""

    UpperCamelCase          =	self.get_dummy_components()

    UpperCamelCase          =	self.pipeline_class(**lowerCamelCase__							)
    UpperCamelCase          =	pipe.to(lowerCamelCase__							)

    pipe.set_progress_bar_config(disable=lowerCamelCase__							)

    UpperCamelCase          =	pipe(**self.get_dummy_inputs(lowerCamelCase__							)							)
    UpperCamelCase          =	output.images[0]
    UpperCamelCase          =	image[0, -3:, -3:, -1]

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

    UpperCamelCase          =	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		:		Tuple							):





    """simple docstring"""


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







   def 		lowerCamelCase_     (			self		:		Tuple							):





    """simple docstring"""


    UpperCamelCase          =	torch_device == """cpu"""
    UpperCamelCase          =	True
    self._test_inference_batch_single_identical(
        batch_size=2					,					test_max_difference=lowerCamelCase__					,					relax_max_difference=lowerCamelCase__					,					)







   def 		lowerCamelCase_     (			self		:		List[Any]							):





    """simple docstring"""


    UpperCamelCase          =	self.get_dummy_components()
    UpperCamelCase          =	self.pipeline_class(**lowerCamelCase__							)
    UpperCamelCase          =	pipe.to(lowerCamelCase__							)
    pipe.set_progress_bar_config(disable=lowerCamelCase__							)

    UpperCamelCase          =	1
    UpperCamelCase          =	2

    UpperCamelCase          =	self.get_dummy_inputs(lowerCamelCase__							)

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

    UpperCamelCase          =	pipe(**lowerCamelCase__					,					num_images_per_prompt=lowerCamelCase__							)[0]

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





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







   def 		lowerCamelCase_     (			self		:		int							):





    """simple docstring"""


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







   def 		lowerCamelCase_     (			self		:		Any							):





    """simple docstring"""


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

    UpperCamelCase          =	torch.Generator(device=lowerCamelCase__							).manual_seed(0							)

    UpperCamelCase          =	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"""






import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize



def   a_						(					lowerCamelCase       ):
				return np.dot(lowerCamelCase					,			lowerCamelCase       )
class 				snake_case       :




					"""simple docstring"""

					def __init__(				self   :	int    ,*,
					    lowerCamelCase__   :	float = np.inf    ,lowerCamelCase__   :	str = "linear"    ,lowerCamelCase__   :	float = 0.0    ,):
									UpperCAmelCase__             =							regularization
									UpperCAmelCase__             =							gamma
									if kernel == "linear":
													UpperCAmelCase__             =							self.__linear
									elif kernel == "rbf":
													if self.gamma == 0:
																	raise ValueError('rbf kernel requires gamma'			)
													if not isinstance(self.gamma    ,(float, int)			):
																	raise ValueError('gamma must be float or int'			)
													if not self.gamma > 0:
																	raise ValueError('gamma must be > 0'			)
													UpperCAmelCase__             =							self.__rbf
													# in the future, there could be a default value like in sklearn
													# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
													# previously it was 1/(n_features)
									else:
													UpperCAmelCase__             =							f'''Unknown kernel: {kernel}'''
													raise ValueError(lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,lowerCamelCase__   :	ndarray    ,lowerCamelCase__   :	ndarray			):
									return np.dot(lowerCamelCase__    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Any    ,lowerCamelCase__   :	ndarray    ,lowerCamelCase__   :	ndarray			):
									return np.exp(-(self.gamma * norm_squared(vectora - vectora			))			)







					def 		__lowerCAmelCase						(				self   :	Tuple    ,lowerCamelCase__   :	list[ndarray]    ,lowerCamelCase__   :	ndarray			):
									UpperCAmelCase__             =							observations
									UpperCAmelCase__             =							classes

									# using Wolfe's Dual to calculate w.
									# Primal problem: minimize 1/2*norm_squared(w)
									#   constraint: yn(w . xn + b) >= 1
									#
									# With l a vector
									# Dual problem: maximize sum_n(ln) -
									#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
									#   constraint: self.C >= ln >= 0
									#           and sum_n(ln*yn) = 0
									# Then we get w using w = sum_n(ln*yn*xn)
									# At the end we can get b ~= mean(yn - w . xn)
									#
									# Since we use kernels, we only need l_star to calculate b
									# and to classify observations

									((UpperCAmelCase__)      ,	)             =							np.shape(lowerCamelCase__			)

									def to_minimize(lowerCamelCase__   :	ndarray			) -> float:
													UpperCAmelCase__             =							0
													((UpperCAmelCase__)      ,	)             =							np.shape(lowerCamelCase__			)
													for i in range(lowerCamelCase__			):
																	for j in range(lowerCamelCase__			):
																					s += (
																					    candidate[i]
																					    * candidate[j]
																					    * classes[i]
																					    * classes[j]
																					    * self.kernel(observations[i]    ,observations[j]			)
																					)
													return 1 / 2 * s - sum(lowerCamelCase__			)

									UpperCAmelCase__             =							LinearConstraint(lowerCamelCase__    ,0    ,0			)
									UpperCAmelCase__             =							Bounds(0    ,self.regularization			)

									UpperCAmelCase__             =							minimize(
									    lowerCamelCase__    ,np.ones(lowerCamelCase__			)    ,bounds=lowerCamelCase__    ,constraints=[ly_contraint]			).x
									UpperCAmelCase__             =							l_star

									# calculating mean offset of separation plane to points
									UpperCAmelCase__             =							0
									for i in range(lowerCamelCase__			):
													for j in range(lowerCamelCase__			):
																	s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
																	    observations[i]    ,observations[j]			)
									UpperCAmelCase__             =							s / n







					def 		__lowerCAmelCase						(				self   :	int    ,lowerCamelCase__   :	ndarray			):
									UpperCAmelCase__             =							sum(
									    self.optimum[n]
									    * self.classes[n]
									    * self.kernel(self.observations[n]    ,lowerCamelCase__			)
									    for n in range(len(self.classes			)			)			)
									return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
						import doctest

						doctest.testmod()



