

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 			a					(		unittest.TestCase			):


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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


						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										torch.manual_seed(0		)
										_UpperCAmelCase    :						Optional[int]							=      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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										torch.manual_seed(0		)
										_UpperCAmelCase    :						Optional[int]							=      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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										torch.manual_seed(0		)
										_UpperCAmelCase    :						List[str]							=      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")  ,   )
										_UpperCAmelCase    :						Optional[int]							=      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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      "cpu"  # ensure determinism for the device-dependent torch.Generator
										_UpperCAmelCase    :						Optional[Any]							=      Mel(
										    x_res=self.dummy_unet.config.sample_size[1]  ,   y_res=self.dummy_unet.config.sample_size[0]  ,   )

										_UpperCAmelCase    :						Optional[Any]							=      DDPMScheduler()
										_UpperCAmelCase    :						Optional[int]							=      AudioDiffusionPipeline(vqvae=A_  ,   unet=self.dummy_unet  ,   mel=A_  ,   scheduler=A_		)
										_UpperCAmelCase    :						List[str]							=      pipe.to(A_		)
										pipe.set_progress_bar_config(disable=A_		)

										_UpperCAmelCase    :						Dict							=      torch.Generator(device=A_		).manual_seed(42		)
										_UpperCAmelCase    :						Tuple							=      pipe(generator=A_  ,   steps=4		)
										_UpperCAmelCase    :						List[str]							=      output.audios[0]
										_UpperCAmelCase    :						int							=      output.images[0]

										_UpperCAmelCase    :						Tuple							=      torch.Generator(device=A_		).manual_seed(42		)
										_UpperCAmelCase    :						int							=      pipe(generator=A_  ,   steps=4  ,   return_dict=A_		)
										_UpperCAmelCase    :						Any							=      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]
										)
										_UpperCAmelCase    :						Tuple							=      np.frombuffer(image.tobytes()  ,   dtype="uint8"		)[:10]
										_UpperCAmelCase    :						Any							=      np.frombuffer(image_from_tuple.tobytes()  ,   dtype="uint8"		)[:10]
										_UpperCAmelCase    :						List[str]							=      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

										_UpperCAmelCase    :						Dict							=      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]  ,   )

										_UpperCAmelCase    :						List[Any]							=      DDIMScheduler()
										_UpperCAmelCase    :						Union[str, Any]							=      self.dummy_vqvae_and_unet
										_UpperCAmelCase    :						List[str]							=      AudioDiffusionPipeline(
										    vqvae=self.dummy_vqvae_and_unet[0]  ,   unet=dummy_vqvae_and_unet[1]  ,   mel=A_  ,   scheduler=A_		)
										_UpperCAmelCase    :						str							=      pipe.to(A_		)
										pipe.set_progress_bar_config(disable=A_		)

										np.random.seed(0		)
										_UpperCAmelCase    :						Any							=      np.random.uniform(-1  ,   1  ,   ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)		)
										_UpperCAmelCase    :						Any							=      torch.Generator(device=A_		).manual_seed(42		)
										_UpperCAmelCase    :						List[str]							=      pipe(raw_audio=A_  ,   generator=A_  ,   start_step=5  ,   steps=10		)
										_UpperCAmelCase    :						Union[str, Any]							=      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]
										)
										_UpperCAmelCase    :						List[str]							=      np.frombuffer(image.tobytes()  ,   dtype="uint8"		)[:10]
										_UpperCAmelCase    :						Union[str, Any]							=      np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]		)

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

										_UpperCAmelCase    :						int							=      self.dummy_unet_condition
										_UpperCAmelCase    :						Optional[Any]							=      AudioDiffusionPipeline(
										    vqvae=self.dummy_vqvae_and_unet[0]  ,   unet=A_  ,   mel=A_  ,   scheduler=A_		)
										_UpperCAmelCase    :						str							=      pipe.to(A_		)
										pipe.set_progress_bar_config(disable=A_		)

										np.random.seed(0		)
										_UpperCAmelCase    :						int							=      torch.rand((1, 1, 10)		)
										_UpperCAmelCase    :						Optional[int]							=      pipe(generator=A_  ,   encoding=A_		)
										_UpperCAmelCase    :						Optional[int]							=      output.images[0]
										_UpperCAmelCase    :						Tuple							=      np.frombuffer(image.tobytes()  ,   dtype="uint8"		)[:10]
										_UpperCAmelCase    :						Optional[Any]							=      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 			a					(		unittest.TestCase			):


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      torch_device

										_UpperCAmelCase    :						Union[str, Any]							=      DiffusionPipeline.from_pretrained("teticio/audio-diffusion-ddim-256"		)
										_UpperCAmelCase    :						Dict							=      pipe.to(A_		)
										pipe.set_progress_bar_config(disable=A_		)

										_UpperCAmelCase    :						Tuple							=      torch.Generator(device=A_		).manual_seed(42		)
										_UpperCAmelCase    :						str							=      pipe(generator=A_		)
										_UpperCAmelCase    :						str							=      output.audios[0]
										_UpperCAmelCase    :						int							=      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]
										_UpperCAmelCase    :						List[Any]							=      np.frombuffer(image.tobytes()  ,   dtype="uint8"		)[:10]
										_UpperCAmelCase    :						int							=      np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]		)

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





from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str					, lowerCAmelCase:					float | Decimal					, lowerCAmelCase:					float = 10**-10	)				->    float:
				_UpperCAmelCase    :						Optional[int]							=      a
				while True:
								_UpperCAmelCase    :						Tuple							=      Decimal(lowerCAmelCase	) - (
								    Decimal(eval(lowerCAmelCase	)	) / Decimal(eval(str(diff(lowerCAmelCase	)	)	)	)  # noqa: S307
								)
								# This number dictates the accuracy of the answer
								if abs(eval(lowerCAmelCase	)	) < precision:  # noqa: S307
												return float(lowerCAmelCase	)


# Let's Execute
if __name__ == "__main__":
					# Find root of trigonometric function
					# Find value of pi
					print(F'''The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}''')
					# Find root of polynomial
					print(F'''The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}''')
					# Find Square Root of 5
					print(F'''The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}''')
					# Exponential Roots
					print(F'''The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}''')


