
"""simple docstring"""





from maths.prime_check import is_prime
def 				A_  (__a					):

					'''simple docstring'''





					if not isinstance(__a					, __a					):
										A_								=    f'Input value of [number={number}] must be an integer'
										raise TypeError(__a					)
					if is_prime(__a					) and is_prime(number + 2					):
										return number + 2
					else:
										return -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()






"""simple docstring"""





import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
		from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


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

UpperCamelCase_       :    List[Any]						      =			256



class __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







			snake_case						   =					["melgan"]




			def __init__(					self    :       Dict							, _snake_case    :       SpectrogramNotesEncoder							, _snake_case    :       SpectrogramContEncoder							, _snake_case    :       TaFilmDecoder							, _snake_case    :       DDPMScheduler							, _snake_case    :       OnnxRuntimeModel if is_onnx_available() else Any							, )   ->			None:







								"""simple docstring"""
								super().__init__()

								# From MELGAN
								A_								=    math.log(1e-5						)  # Matches MelGAN training.
								A_								=    4.0  # Largest value for most examples
								A_								=    128

								self.register_modules(
								    notes_encoder=_snake_case							, continuous_encoder=_snake_case							, decoder=_snake_case							, scheduler=_snake_case							, melgan=_snake_case							, )




			def   lowerCamelCase__  (					self    :       Union[str, Any]							, _snake_case    :       Tuple							, _snake_case    :       str=(-1.0, 1.0)							, _snake_case    :       int=False						)   ->			str:







								"""simple docstring"""
								A_			,     A_								=    output_range
								if clip:
													A_								=    torch.clip(_snake_case							, self.min_value							, self.max_value						)
								# Scale to [0, 1].
								A_								=    (features - self.min_value) / (self.max_value - self.min_value)
								# Scale to [min_out, max_out].
								return zero_one * (max_out - min_out) + min_out




			def   lowerCamelCase__  (					self    :       Dict							, _snake_case    :       Tuple							, _snake_case    :       Optional[Any]=(-1.0, 1.0)							, _snake_case    :       List[str]=False						)   ->			List[str]:







								"""simple docstring"""
								A_			,     A_								=    input_range
								A_								=    torch.clip(_snake_case							, _snake_case							, _snake_case						) if clip else outputs
								# Scale to [0, 1].
								A_								=    (outputs - min_out) / (max_out - min_out)
								# Scale to [self.min_value, self.max_value].
								return zero_one * (self.max_value - self.min_value) + self.min_value




			def   lowerCamelCase__  (					self    :       Union[str, Any]							, _snake_case    :       str							, _snake_case    :       List[Any]							, _snake_case    :       Union[str, Any]						)   ->			List[str]:







								"""simple docstring"""
								A_								=    input_tokens > 0
								A_			,     A_								=    self.notes_encoder(
								    encoder_input_tokens=_snake_case							, encoder_inputs_mask=_snake_case						)

								A_			,     A_								=    self.continuous_encoder(
								    encoder_inputs=_snake_case							, encoder_inputs_mask=_snake_case						)

								return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]




			def   lowerCamelCase__  (					self    :       List[Any]							, _snake_case    :       List[Any]							, _snake_case    :       int							, _snake_case    :       Tuple						)   ->			Optional[int]:







								"""simple docstring"""
								A_								=    noise_time
								if not torch.is_tensor(_snake_case						):
													A_								=    torch.tensor([timesteps]							, dtype=torch.long							, device=input_tokens.device						)
								elif torch.is_tensor(_snake_case						) and len(timesteps.shape						) == 0:
													A_								=    timesteps[None].to(input_tokens.device						)

								# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
								A_								=    timesteps * torch.ones(input_tokens.shape[0]							, dtype=timesteps.dtype							, device=timesteps.device						)

								A_								=    self.decoder(
								    encodings_and_masks=_snake_case							, decoder_input_tokens=_snake_case							, decoder_noise_time=_snake_case						)
								return logits






			@torch.no_grad()
			def __call__(					self    :       List[Any]							, _snake_case    :       List[List[int]]							, _snake_case    :       Optional[torch.Generator] = None							, _snake_case    :       int = 100							, _snake_case    :       bool = True							, _snake_case    :       str = "numpy"							, _snake_case    :       Optional[Callable[[int, int, torch.FloatTensor], None]] = None							, _snake_case    :       int = 1							, )   ->			Union[AudioPipelineOutput, Tuple]:







								"""simple docstring"""
								if (callback_steps is None) or (
								    callback_steps is not None and (not isinstance(_snake_case							, _snake_case						) or callback_steps <= 0)
								):
													raise ValueError(
													    F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
													    F' {type(_snake_case						)}.'						)

								A_								=    np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]							, dtype=np.floataa						)
								A_								=    np.zeros([1, 0, self.n_dims]							, np.floataa						)
								A_								=    torch.ones((1, TARGET_FEATURE_LENGTH)							, dtype=_snake_case							, device=self.device						)

								for i, encoder_input_tokens in enumerate(_snake_case						):
													if i == 0:
																		A_								=    torch.from_numpy(pred_mel[:1].copy()						).to(
																		    device=self.device							, dtype=self.decoder.dtype						)
																		# The first chunk has no previous context.
																		A_								=    torch.zeros((1, TARGET_FEATURE_LENGTH)							, dtype=_snake_case							, device=self.device						)
													else:
																		# The full song pipeline does not feed in a context feature, so the mask
																		# will be all 0s after the feature converter. Because we know we're
																		# feeding in a full context chunk from the previous prediction, set it
																		# to all 1s.
																		A_								=    ones

													A_								=    self.scale_features(
													    _snake_case							, output_range=[-1.0, 1.0]							, clip=_snake_case						)

													A_								=    self.encode(
													    input_tokens=torch.IntTensor([encoder_input_tokens]						).to(device=self.device						)							, continuous_inputs=_snake_case							, continuous_mask=_snake_case							, )

													# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
													A_								=    randn_tensor(
													    shape=encoder_continuous_inputs.shape							, generator=_snake_case							, device=self.device							, dtype=self.decoder.dtype							, )

													# set step values
													self.scheduler.set_timesteps(_snake_case						)

													# Denoising diffusion loop
													for j, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
																		A_								=    self.decode(
																		    encodings_and_masks=_snake_case							, input_tokens=_snake_case							, noise_time=t / self.scheduler.config.num_train_timesteps							, )

																		# Compute previous output: x_t -> x_t-1
																		A_								=    self.scheduler.step(_snake_case							, _snake_case							, _snake_case							, generator=_snake_case						).prev_sample

													A_								=    self.scale_to_features(_snake_case							, input_range=[-1.0, 1.0]						)
													A_								=    mel[:1]
													A_								=    mel.cpu().float().numpy()

													A_								=    np.concatenate([full_pred_mel, pred_mel[:1]]							, axis=1						)

													# call the callback, if provided
													if callback is not None and i % callback_steps == 0:
																		callback(_snake_case							, _snake_case						)

													logger.info("Generated segment"							, _snake_case						)

								if output_type == "numpy" and not is_onnx_available():
													raise ValueError(
													    "Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'."						)
								elif output_type == "numpy" and self.melgan is None:
													raise ValueError(
													    "Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'."						)

								if output_type == "numpy":
													A_								=    self.melgan(input_features=full_pred_mel.astype(np.floataa						)						)
								else:
													A_								=    full_pred_mel

								if not return_dict:
													return (output,)

								return AudioPipelineOutput(audios=_snake_case						)




