



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


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

_lowercase     							=						2_56
class 				__A					( A_      ):
			UpperCamelCase					:List[str]     =			['''melgan''']





			def __init__(self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    ):
						super().__init__()

						# From MELGAN
						lowerCamelCase__ :      Any								=		math.log(1E-5				)  # Matches MelGAN training.
						lowerCamelCase__ :      Optional[int]								=		4.0  # Largest value for most examples
						lowerCamelCase__ :      Dict								=		128

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





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

						lowerCamelCase__ :      Union[str, Any]								=		self.continuous_encoder(
						    encoder_inputs=__magic_name__							,    encoder_inputs_mask=__magic_name__				)

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





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

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

						lowerCamelCase__ :      Optional[int]								=		self.decoder(
						    encodings_and_masks=__magic_name__							,    decoder_input_tokens=__magic_name__							,    decoder_noise_time=__magic_name__				)
						return logits





			@torch.no_grad()
			def __call__(self							,    __magic_name__							,    __magic_name__ = None							,    __magic_name__ = 100							,    __magic_name__ = True							,    __magic_name__ = "numpy"							,    __magic_name__ = None							,    __magic_name__ = 1							,    ):
						if (callback_steps is None) or (
						    callback_steps is not None and (not isinstance(__magic_name__							,    __magic_name__				) or callback_steps <= 0)
						):
									raise ValueError(
									    f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
									    f" {type(__magic_name__				)}."				)

						lowerCamelCase__ :      Optional[Any]								=		np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]							,    dtype=np.floataa				)
						lowerCamelCase__ :      List[Any]								=		np.zeros([1, 0, self.n_dims]							,    np.floataa				)
						lowerCamelCase__ :      List[Any]								=		torch.ones((1, TARGET_FEATURE_LENGTH)							,    dtype=__magic_name__							,    device=self.device				)

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

									lowerCamelCase__ :      List[Any]								=		self.scale_features(
									    __magic_name__							,    output_range=[-1.0, 1.0]							,    clip=__magic_name__				)

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

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

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

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

												# Compute previous output: x_t -> x_t-1
												lowerCamelCase__ :      List[str]								=		self.scheduler.step(__magic_name__							,    __magic_name__							,    __magic_name__							,    generator=__magic_name__				).prev_sample

									lowerCamelCase__ :      Any								=		self.scale_to_features(__magic_name__							,    input_range=[-1.0, 1.0]				)
									lowerCamelCase__ :      Optional[Any]								=		mel[:1]
									lowerCamelCase__ :      List[Any]								=		mel.cpu().float().numpy()

									lowerCamelCase__ :      Optional[int]								=		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(__magic_name__							,    __magic_name__				)

									logger.info("""Generated segment"""							,    __magic_name__				)

						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__ :      Optional[int]								=		self.melgan(input_features=full_pred_mel.astype(np.floataa				)				)
						else:
									lowerCamelCase__ :      Any								=		full_pred_mel

						if not return_dict:
									return (output,)

						return AudioPipelineOutput(audios=__magic_name__				)








def      _A				(UpperCamelCase		:      List[Any]      ,			UpperCamelCase		:      Dict )  ->List[str]:



			'''simple docstring'''







			lowerCamelCase__ :      int								=		[1]
			for i in range(2      ,			UpperCamelCase ):
						factorials.append(factorials[-1] * i )
			assert 0 <= k < factorials[-1] * n, "k out of bounds"

			lowerCamelCase__ :      Union[str, Any]								=		[]
			lowerCamelCase__ :      Optional[Any]								=		list(range(UpperCamelCase ) )

			# Find permutation
			while factorials:
						lowerCamelCase__ :      Union[str, Any]								=		factorials.pop()
						lowerCamelCase__							,lowerCamelCase__ :      Union[str, Any]								=		divmod(UpperCamelCase      ,			UpperCamelCase )
						permutation.append(elements[number] )
						elements.remove(elements[number] )
			permutation.append(elements[0] )

			return permutation


if __name__ == "__main__":
				import doctest

				doctest.testmod()



