



from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
UpperCAmelCase							 =					299_792_458

# Symbols
UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase							 =					symbols("""ct x y z""")

def 			A_      (	__a     :				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 			A_      (	__a     :				float		):

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

def 			A_      (	__a     :				float		):

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

def 			A_      (	__a     :				float ,		__a     :				np.ndarray | None = None		):

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

				return transformation_matrix(__a		) @ event


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	# Example of symbolic vector:
	UpperCAmelCase							 =					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
	UpperCAmelCase							 =					{ct: c, x: 1, y: 1, z: 1}
	UpperCAmelCase							 =					[four_vector[i].subs(sub_dict) for i in range(4)]

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






import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class  __snake_case      (   unittest.TestCase):







							'''simple docstring'''






							def _a      (							self		):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()


							@property
							def _a      (							self		):
											torch.manual_seed(0		)
											a__				      =		UNetaDModel(
											    sample_size=(32, 64)  ,  in_channels=1  ,  out_channels=1  ,  layers_per_block=2  ,  block_out_channels=(128, 128)  ,  down_block_types=("""AttnDownBlock2D""", """DownBlock2D""")  ,  up_block_types=("""UpBlock2D""", """AttnUpBlock2D""")  ,  )
											return model


							@property
							def _a      (							self		):
											torch.manual_seed(0		)
											a__				      =		UNetaDConditionModel(
											    sample_size=(64, 32)  ,  in_channels=1  ,  out_channels=1  ,  layers_per_block=2  ,  block_out_channels=(128, 128)  ,  down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""")  ,  up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""")  ,  cross_attention_dim=10  ,  )
											return model


							@property
							def _a      (							self		):
											torch.manual_seed(0		)
											a__				      =		AutoencoderKL(
											    sample_size=(128, 64)  ,  in_channels=1  ,  out_channels=1  ,  latent_channels=1  ,  layers_per_block=2  ,  block_out_channels=(128, 128)  ,  down_block_types=("""DownEncoderBlock2D""", """DownEncoderBlock2D""")  ,  up_block_types=("""UpDecoderBlock2D""", """UpDecoderBlock2D""")  ,  )
											a__				      =		UNetaDModel(
											    sample_size=(64, 32)  ,  in_channels=1  ,  out_channels=1  ,  layers_per_block=2  ,  block_out_channels=(128, 128)  ,  down_block_types=("""AttnDownBlock2D""", """DownBlock2D""")  ,  up_block_types=("""UpBlock2D""", """AttnUpBlock2D""")  ,  )
											return vqvae, unet







							@slow
							def _a      (							self		):
											a__				      =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
											a__				      =		Mel(
											    x_res=self.dummy_unet.config.sample_size[1]  ,  y_res=self.dummy_unet.config.sample_size[0]  ,  )

											a__				      =		DDPMScheduler()
											a__				      =		AudioDiffusionPipeline(vqvae=a_  ,  unet=self.dummy_unet  ,  mel=a_  ,  scheduler=a_		)
											a__				      =		pipe.to(a_		)
											pipe.set_progress_bar_config(disable=a_		)

											a__				      =		torch.Generator(device=a_		).manual_seed(42		)
											a__				      =		pipe(generator=a_  ,  steps=4		)
											a__				      =		output.audios[0]
											a__				      =		output.images[0]

											a__				      =		torch.Generator(device=a_		).manual_seed(42		)
											a__				      =		pipe(generator=a_  ,  steps=4  ,  return_dict=a_		)
											a__				      =		output[0][0]

											assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
											assert (
											    image.height == self.dummy_unet.config.sample_size[0]
											    and image.width == self.dummy_unet.config.sample_size[1]
											)
											a__				      =		np.frombuffer(image.tobytes()  ,  dtype="""uint8"""		)[:10]
											a__				      =		np.frombuffer(image_from_tuple.tobytes()  ,  dtype="""uint8"""		)[:10]
											a__				      =		np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]		)

											assert np.abs(image_slice.flatten() - expected_slice		).max() == 0
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() == 0

											a__				      =		Mel(
											    x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]  ,  y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]  ,  )

											a__				      =		DDIMScheduler()
											a__				      =		self.dummy_vqvae_and_unet
											a__				      =		AudioDiffusionPipeline(
											    vqvae=self.dummy_vqvae_and_unet[0]  ,  unet=dummy_vqvae_and_unet[1]  ,  mel=a_  ,  scheduler=a_		)
											a__				      =		pipe.to(a_		)
											pipe.set_progress_bar_config(disable=a_		)

											np.random.seed(0		)
											a__				      =		np.random.uniform(-1  ,  1  ,  ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)		)
											a__				      =		torch.Generator(device=a_		).manual_seed(42		)
											a__				      =		pipe(raw_audio=a_  ,  generator=a_  ,  start_step=5  ,  steps=10		)
											a__				      =		output.images[0]

											assert (
											    image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
											    and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
											)
											a__				      =		np.frombuffer(image.tobytes()  ,  dtype="""uint8"""		)[:10]
											a__				      =		np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]		)

											assert np.abs(image_slice.flatten() - expected_slice		).max() == 0

											a__				      =		self.dummy_unet_condition
											a__				      =		AudioDiffusionPipeline(
											    vqvae=self.dummy_vqvae_and_unet[0]  ,  unet=a_  ,  mel=a_  ,  scheduler=a_		)
											a__				      =		pipe.to(a_		)
											pipe.set_progress_bar_config(disable=a_		)

											np.random.seed(0		)
											a__				      =		torch.rand((1, 1, 10)		)
											a__				      =		pipe(generator=a_  ,  encoding=a_		)
											a__				      =		output.images[0]
											a__				      =		np.frombuffer(image.tobytes()  ,  dtype="""uint8"""		)[:10]
											a__				      =		np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]		)

											assert np.abs(image_slice.flatten() - expected_slice		).max() == 0




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







							'''simple docstring'''






							def _a      (							self		):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()







							def _a      (							self		):
											a__				      =		torch_device

											a__				      =		DiffusionPipeline.from_pretrained("""teticio/audio-diffusion-ddim-256"""		)
											a__				      =		pipe.to(a_		)
											pipe.set_progress_bar_config(disable=a_		)

											a__				      =		torch.Generator(device=a_		).manual_seed(42		)
											a__				      =		pipe(generator=a_		)
											a__				      =		output.audios[0]
											a__				      =		output.images[0]

											assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
											assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
											a__				      =		np.frombuffer(image.tobytes()  ,  dtype="""uint8"""		)[:10]
											a__				      =		np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]		)

											assert np.abs(image_slice.flatten() - expected_slice		).max() == 0

