




"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


a      =							logging.get_logger(__name__)  # pylint: disable=invalid-name






class       SCREAMING_SNAKE_CASE__					(					_a					):


						def __init__(							self				:			str						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			Optional[Any]					):
										super().__init__()
										self.register_modules(unet=lowerCAmelCase						,						scheduler=lowerCAmelCase					)




						@torch.no_grad()
						def __call__(							self				:			List[Any]						,						lowerCAmelCase				:			int = 1						,						lowerCAmelCase				:			int = 100						,						lowerCAmelCase				:			Optional[Union[torch.Generator, List[torch.Generator]]] = None						,						lowerCAmelCase				:			Optional[float] = None						,						lowerCAmelCase				:			bool = True						,						):

										if audio_length_in_s is None:
														lowerCAmelCase  =				self.unet.config.sample_size / self.unet.config.sample_rate

										lowerCAmelCase  =				audio_length_in_s * self.unet.config.sample_rate

										lowerCAmelCase  =				2 ** len(self.unet.up_blocks					)
										if sample_size < 3 * down_scale_factor:
														raise ValueError(
														    f'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
														    f''' {3 * down_scale_factor / self.unet.config.sample_rate}.'''					)

										lowerCAmelCase  =				int(lowerCAmelCase					)
										if sample_size % down_scale_factor != 0:
														lowerCAmelCase  =				(
														    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
														) * down_scale_factor
														logger.info(
														    f'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
														    f''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
														    """ process."""					)
										lowerCAmelCase  =				int(lowerCAmelCase					)

										lowerCAmelCase  =				next(iter(self.unet.parameters()					)					).dtype
										lowerCAmelCase  =				(batch_size, self.unet.config.in_channels, sample_size)
										if isinstance(lowerCAmelCase						,						lowerCAmelCase					) and len(lowerCAmelCase					) != batch_size:
														raise ValueError(
														    f'''You have passed a list of generators of length {len(lowerCAmelCase					)}, but requested an effective batch'''
														    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''					)

										lowerCAmelCase  =				randn_tensor(lowerCAmelCase						,						generator=lowerCAmelCase						,						device=self.device						,						dtype=lowerCAmelCase					)

										# set step values
										self.scheduler.set_timesteps(lowerCAmelCase						,						device=audio.device					)
										lowerCAmelCase  =				self.scheduler.timesteps.to(lowerCAmelCase					)

										for t in self.progress_bar(self.scheduler.timesteps					):
														# 1. predict noise model_output
														lowerCAmelCase  =				self.unet(lowerCAmelCase						,						lowerCAmelCase					).sample

														# 2. compute previous image: x_t -> t_t-1
														lowerCAmelCase  =				self.scheduler.step(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					).prev_sample

										lowerCAmelCase  =				audio.clamp(-1						,						1					).float().cpu().numpy()

										lowerCAmelCase  =				audio[:, :, :original_sample_size]

										if not return_dict:
														return (audio,)

										return AudioPipelineOutput(audios=lowerCAmelCase					)











"""simple docstring"""







from __future__ import annotations

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

from sympy import diff





def 							lowercase					(snake_case__   : str      ,       snake_case__   : float | Decimal      ,       snake_case__   : float = 10**-10 )     ->  float:


				'''simple docstring'''






				lowerCAmelCase  =				a
				while True:
								lowerCAmelCase  =				Decimal(snake_case__ ) - (
								    Decimal(eval(snake_case__ ) ) / Decimal(eval(str(diff(snake_case__ ) ) ) )  # noqa: S307
								)
								# This number dictates the accuracy of the answer
								if abs(eval(snake_case__ ) ) < precision:  # noqa: S307
												return float(snake_case__ )


# 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)}""")





