'''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 						A   (   UpperCAmelCase       ,	unittest.TestCase   ):
							a_        =      ShapEImgaImgPipeline
							a_        =      ['''image''']
							a_        =      ['''image''']
							a_        =      [
							    '''num_images_per_prompt''',
							    '''num_inference_steps''',
							    '''generator''',
							    '''latents''',
							    '''guidance_scale''',
							    '''frame_size''',
							    '''output_type''',
							    '''return_dict''',
							]
							a_        =      False





							@property
							def       snake_case__						(      self				:							Tuple	)							->	List[str]:
								return 3_2





							@property
							def       snake_case__						(      self				:							Optional[Any]	)							->	Optional[int]:
								return 3_2





							@property
							def       snake_case__						(      self				:							Optional[int]	)							->	str:
								return self.time_input_dim * 4





							@property
							def       snake_case__						(      self				:							List[Any]	)							->	int:
								return 8





							@property
							def       snake_case__						(      self				:							Optional[int]	)							->	str:
								torch.manual_seed(0	)
								__UpperCAmelCase							=			CLIPVisionConfig(
								    hidden_size=self.text_embedder_hidden_size     ,		image_size=6_4     ,		projection_dim=self.text_embedder_hidden_size     ,		intermediate_size=3_7     ,		num_attention_heads=4     ,		num_channels=3     ,		num_hidden_layers=5     ,		patch_size=1     ,		)

								__UpperCAmelCase							=			CLIPVisionModel(__a	)
								return model





							@property
							def       snake_case__						(      self				:							int	)							->	Union[str, Any]:
								__UpperCAmelCase							=			CLIPImageProcessor(
								    crop_size=2_2_4     ,		do_center_crop=__a     ,		do_normalize=__a     ,		do_resize=__a     ,		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_2_4     ,		)

								return image_processor





							@property
							def       snake_case__						(      self				:							List[str]	)							->	Dict:
								torch.manual_seed(0	)

								__UpperCAmelCase							=			{
								    '''num_attention_heads''': 2,
								    '''attention_head_dim''': 1_6,
								    '''embedding_dim''': self.time_input_dim,
								    '''num_embeddings''': 3_2,
								    '''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(**__a	)
								return model





							@property
							def       snake_case__						(      self				:							Dict	)							->	Dict:
								torch.manual_seed(0	)

								__UpperCAmelCase							=			{
								    '''param_shapes''': (
								        (self.renderer_dim, 9_3),
								        (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''': 1_2,
								    '''background''': (
								        0.1,
								        0.1,
								        0.1,
								    ),
								}
								__UpperCAmelCase							=			ShapERenderer(**__a	)
								return model





							def       snake_case__						(      self				:							Optional[int]	)							->	Optional[int]:
								__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=1_0_2_4     ,		prediction_type='''sample'''     ,		use_karras_sigmas=__a     ,		clip_sample=__a     ,		clip_sample_range=1.0     ,		)
								__UpperCAmelCase							=			{
								    '''prior''': prior,
								    '''image_encoder''': image_encoder,
								    '''image_processor''': image_processor,
								    '''renderer''': renderer,
								    '''scheduler''': scheduler,
								}

								return components





							def       snake_case__						(      self				:							Tuple     ,		__a				:							Dict     ,		__a				:							Union[str, Any]=0	)							->	List[str]:
								__UpperCAmelCase							=			floats_tensor((1, 3, 6_4, 6_4)     ,		rng=random.Random(__a	)	).to(__a	)

								if str(__a	).startswith('''mps'''	):
									__UpperCAmelCase							=			torch.manual_seed(__a	)
								else:
									__UpperCAmelCase							=			torch.Generator(device=__a	).manual_seed(__a	)
								__UpperCAmelCase							=			{
								    '''image''': input_image,
								    '''generator''': generator,
								    '''num_inference_steps''': 1,
								    '''frame_size''': 3_2,
								    '''output_type''': '''np''',
								}
								return inputs





							def       snake_case__						(      self				:							Optional[int]	)							->	Union[str, Any]:
								__UpperCAmelCase							=			'''cpu'''

								__UpperCAmelCase							=			self.get_dummy_components()

								__UpperCAmelCase							=			self.pipeline_class(**__a	)
								__UpperCAmelCase							=			pipe.to(__a	)

								pipe.set_progress_bar_config(disable=__a	)

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

								assert image.shape == (2_0, 3_2, 3_2, 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       snake_case__						(      self				:							str	)							->	List[str]:
								# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
								self._test_inference_batch_consistent(batch_sizes=[1, 2]	)





							def       snake_case__						(      self				:							Optional[Any]	)							->	Optional[Any]:
								__UpperCAmelCase							=			torch_device == '''cpu'''
								__UpperCAmelCase							=			True
								self._test_inference_batch_single_identical(
								    batch_size=2     ,		test_max_difference=__a     ,		relax_max_difference=__a     ,		)






							def       snake_case__						(      self				:							Dict	)							->	List[str]:
								__UpperCAmelCase							=			self.get_dummy_components()
								__UpperCAmelCase							=			self.pipeline_class(**__a	)
								__UpperCAmelCase							=			pipe.to(__a	)
								pipe.set_progress_bar_config(disable=__a	)

								__UpperCAmelCase							=			1
								__UpperCAmelCase							=			2

								__UpperCAmelCase							=			self.get_dummy_inputs(__a	)

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

								__UpperCAmelCase							=			pipe(**__a     ,		num_images_per_prompt=__a	)[0]

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






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





							def       snake_case__						(      self				:							Any	)							->	int:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()






							def       snake_case__						(      self				:							Dict	)							->	Any:
								__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(__a	)
								pipe.set_progress_bar_config(disable=__a	)

								__UpperCAmelCase							=			torch.Generator(device=__a	).manual_seed(0	)

								__UpperCAmelCase							=			pipe(
								    __a     ,		generator=__a     ,		guidance_scale=3.0     ,		num_inference_steps=6_4     ,		frame_size=6_4     ,		output_type='''np'''     ,		).images[0]

								assert images.shape == (2_0, 6_4, 6_4, 3)

								assert_mean_pixel_difference(__a     ,		__a	)

'''simple docstring'''


from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
__lowerCAmelCase	:       str						       =	299_792_458

# Symbols
__lowerCAmelCase       ,	__lowerCAmelCase       ,	__lowerCAmelCase       ,	__lowerCAmelCase	:       Any						       =	symbols("ct x y z")





def       lowerCAmelCase					(		UpperCamelCase__				:   float       ):


	"""simple docstring"""
	if velocity > c:
		raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!'''       )
	elif velocity < 1:
		# Usually the speed should be much higher than 1 (c order of magnitude)
		raise ValueError('''Speed must be greater than or equal to 1!'''       )

	return velocity / c





def       lowerCAmelCase					(		UpperCamelCase__				:   float       ):


	"""simple docstring"""
	return 1 / sqrt(1 - beta(UpperCamelCase__       ) ** 2       )





def       lowerCAmelCase					(		UpperCamelCase__				:   float       ):


	"""simple docstring"""
	return np.array(
	    [
	        [gamma(UpperCamelCase__       ), -gamma(UpperCamelCase__       ) * beta(UpperCamelCase__       ), 0, 0],
	        [-gamma(UpperCamelCase__       ) * beta(UpperCamelCase__       ), gamma(UpperCamelCase__       ), 0, 0],
	        [0, 0, 1, 0],
	        [0, 0, 0, 1],
	    ]       )





def       lowerCAmelCase					(		UpperCamelCase__				:   float    ,					UpperCamelCase__				:   np.ndarray | None = None       ):


	"""simple docstring"""
	# Ensure event is not empty
	if event is None:
		__UpperCAmelCase							=			np.array([ct, x, y, z]       )  # Symbolic four vector
	else:
		event[0] *= c  # x0 is ct (speed of light * time)

	return transformation_matrix(UpperCamelCase__       ) @ event


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					# Example of symbolic vector:
					__lowerCAmelCase	:       Dict						       =	transform(29_979_245)
					print("Example of four vector: ")
					print(F"""ct' = {four_vector[0]}""")
					print(F"""x' = {four_vector[1]}""")
					print(F"""y' = {four_vector[2]}""")
					print(F"""z' = {four_vector[3]}""")

					# Substitute symbols with numerical values
					__lowerCAmelCase	:       Union[str, Any]						       =	{ct: c, x: 1, y: 1, z: 1}
					__lowerCAmelCase	:       Optional[int]						       =	[four_vector[i].subs(sub_dict) for i in range(4)]

					print(F"""\n{numerical_vector}""")
