"""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__  :  int						     =	256
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = ["melgan"]







					def __init__(				self   :	List[str]    ,lowerCamelCase__   :	SpectrogramNotesEncoder    ,lowerCamelCase__   :	SpectrogramContEncoder    ,lowerCamelCase__   :	TaFilmDecoder    ,lowerCamelCase__   :	DDPMScheduler    ,lowerCamelCase__   :	OnnxRuntimeModel if is_onnx_available() else Any    ,):
									super().__init__()

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

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







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

									UpperCAmelCase__      ,	UpperCAmelCase__             =							self.continuous_encoder(
									    encoder_inputs=lowerCamelCase__    ,encoder_inputs_mask=lowerCamelCase__			)

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







					def 		__lowerCAmelCase						(				self   :	Any    ,lowerCamelCase__   :	Any    ,lowerCamelCase__   :	Tuple    ,lowerCamelCase__   :	Tuple			):
									UpperCAmelCase__             =							noise_time
									if not torch.is_tensor(lowerCamelCase__			):
													UpperCAmelCase__             =							torch.tensor([timesteps]    ,dtype=torch.long    ,device=input_tokens.device			)
									elif torch.is_tensor(lowerCamelCase__			) and len(timesteps.shape			) == 0:
													UpperCAmelCase__             =							timesteps[None].to(input_tokens.device			)

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

									UpperCAmelCase__             =							self.decoder(
									    encodings_and_masks=lowerCamelCase__    ,decoder_input_tokens=lowerCamelCase__    ,decoder_noise_time=lowerCamelCase__			)
									return logits







					@torch.no_grad()
					def __call__(				self   :	Any    ,lowerCamelCase__   :	List[List[int]]    ,lowerCamelCase__   :	Optional[torch.Generator] = None    ,lowerCamelCase__   :	int = 100    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	str = "numpy"    ,lowerCamelCase__   :	Optional[Callable[[int, int, torch.FloatTensor], None]] = None    ,lowerCamelCase__   :	int = 1    ,):
									if (callback_steps is None) or (
									    callback_steps is not None and (not isinstance(lowerCamelCase__    ,lowerCamelCase__			) or callback_steps <= 0)
									):
													raise ValueError(
													    f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
													    f''' {type(lowerCamelCase__			)}.'''			)

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

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

													UpperCAmelCase__             =							self.scale_features(
													    lowerCamelCase__    ,output_range=[-1.0, 1.0]    ,clip=lowerCamelCase__			)

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

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

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

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

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

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

													UpperCAmelCase__             =							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(lowerCamelCase__    ,lowerCamelCase__			)

													logger.info('Generated segment'    ,lowerCamelCase__			)

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

									if not return_dict:
													return (output,)

									return AudioPipelineOutput(audios=lowerCamelCase__			)




"""simple docstring"""






def   a_						(					lowerCamelCase       ):
				return str(lowerCamelCase       ) == str(lowerCamelCase       )[::-1]



def   a_						(					lowerCamelCase       ):
				return int(lowerCamelCase       ) + int(str(lowerCamelCase       )[::-1]       )



def   a_						(					lowerCamelCase = 1_0_0_0_0       ):
				UpperCAmelCase__             =							[]
				for num in range(1					,			lowerCamelCase       ):
								UpperCAmelCase__             =							0
								UpperCAmelCase__             =							num
								while iterations < 5_0:
												UpperCAmelCase__             =							sum_reverse(lowerCamelCase       )
												iterations += 1
												if is_palindrome(lowerCamelCase       ):
																break
								else:
												lychrel_nums.append(lowerCamelCase       )
				return len(lowerCamelCase       )


if __name__ == "__main__":
						print(F"""{solution() = }""")



