



from __future__ import annotations

import math
from collections.abc import Callable

def 			A_      (	__a     :				Callable[[int | float], int | float] ,		__a     :				int | float ,		__a     :				int | float ,		__a     :				int = 100 ,		):

				"""simple docstring"""
				a__				      =		x_start
				a__				      =		fnc(__a		)
				a__				      =		0.0

				for _ in range(__a		):
								# Approximates curve as a sequence of linear lines and sums their length
								a__				      =		(x_end - x_start) / steps + xa
								a__				      =		fnc(__a		)
								length += math.hypot(xa - xa ,		fxa - fxa		)

								# Increment step
								a__				      =		xa
								a__				      =		fxa

				return length


if __name__ == "__main__":

	def 			A_      (	__a     :				Any		):

					"""simple docstring"""
					return math.sin(10 * x		)

	print("""f(x) = sin(10 * x)""")
	print("""The length of the curve from x = -10 to x = 10 is:""")
	UpperCAmelCase							 =					10
	while i <= 100_000:
		print(f"""With {i} steps: {line_length(f, -10, 10, i)}""")
		i *= 10






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

