"""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


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

__lowerCAmelCase    :							str													=						2_56

class       a_      (     __UpperCamelCase			):
					UpperCamelCase_ :	Tuple			=    ["""melgan"""]




					def __init__(	self						:     Optional[int]  ,						snake_case__						:     SpectrogramNotesEncoder  ,						snake_case__						:     SpectrogramContEncoder  ,						snake_case__						:     TaFilmDecoder  ,						snake_case__						:     DDPMScheduler  ,						snake_case__						:     OnnxRuntimeModel if is_onnx_available() else Any  ,						):
							super().__init__()

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

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     Tuple  ,						snake_case__						:     Dict=(-1.0, 1.0)  ,						snake_case__						:     Dict=False     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   output_range
							if clip:
									lowerCAmelCase__        =   torch.clip(snake_case__  ,						self.min_value  ,						self.max_value     )
							# Scale to [0, 1].
							lowerCAmelCase__        =   (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 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     int=(-1.0, 1.0)  ,						snake_case__						:     Optional[Any]=False     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   input_range
							lowerCAmelCase__        =   torch.clip(snake_case__  ,						snake_case__  ,						snake_case__     ) if clip else outputs
							# Scale to [0, 1].
							lowerCAmelCase__        =   (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 						_SCREAMING_SNAKE_CASE						(	self						:     Any  ,						snake_case__						:     List[str]  ,						snake_case__						:     Tuple  ,						snake_case__						:     Tuple     ):
							lowerCAmelCase__        =   input_tokens > 0
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.notes_encoder(
							    encoder_input_tokens=snake_case__  ,						encoder_inputs_mask=snake_case__     )

							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.continuous_encoder(
							    encoder_inputs=snake_case__  ,						encoder_inputs_mask=snake_case__     )

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     Any  ,						snake_case__						:     Dict     ):
							lowerCAmelCase__        =   noise_time
							if not torch.is_tensor(snake_case__     ):
									lowerCAmelCase__        =   torch.tensor([timesteps]  ,						dtype=torch.long  ,						device=input_tokens.device     )
							elif torch.is_tensor(snake_case__     ) and len(timesteps.shape     ) == 0:
									lowerCAmelCase__        =   timesteps[None].to(input_tokens.device     )

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

							lowerCAmelCase__        =   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						:     Tuple  ,						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  ,						):

							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__     )}."""     )

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

							for i, encoder_input_tokens in enumerate(snake_case__     ):
									if i == 0:
											lowerCAmelCase__        =   torch.from_numpy(pred_mel[:1].copy()     ).to(
											    device=self.device  ,						dtype=self.decoder.dtype     )
											# The first chunk has no previous context.
											lowerCAmelCase__        =   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.
											lowerCAmelCase__        =   ones

									lowerCAmelCase__        =   self.scale_features(
									    snake_case__  ,						output_range=[-1.0, 1.0]  ,						clip=snake_case__     )

									lowerCAmelCase__        =   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
									lowerCAmelCase__        =   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     )     ):
											lowerCAmelCase__        =   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
											lowerCAmelCase__        =   self.scheduler.step(snake_case__  ,						snake_case__  ,						snake_case__  ,						generator=snake_case__     ).prev_sample

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

									lowerCAmelCase__        =   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":
									lowerCAmelCase__        =   self.melgan(input_features=full_pred_mel.astype(np.floataa     )     )
							else:
									lowerCAmelCase__        =   full_pred_mel

							if not return_dict:
									return (output,)

							return AudioPipelineOutput(audios=snake_case__     )




"""simple docstring"""




def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		assert isinstance(lowerCamelCase__			,  lowerCamelCase__     ), f"""The input value of [n={number}] is not an integer"""

		if number == 1:
				return 2
		elif number < 1:
				lowerCAmelCase__        =   f"""The input value of [n={number}] has to be > 0"""
				raise ValueError(lowerCamelCase__     )
		else:
				lowerCAmelCase__        =   sylvester(number - 1     )
				lowerCAmelCase__        =   num - 1
				lowerCAmelCase__        =   num
				return lower * upper + 1


if __name__ == "__main__":
			print(F"The 8th number in Sylvester's sequence: {sylvester(8)}")




