


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

_UpperCAmelCase				: Any							    =				2_5_6


class       a__				( lowercase_      ):







				"""simple docstring"""



				__UpperCamelCase    :		Optional[Any]	=	['''melgan''']




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

										# From MELGAN
										__lowerCAmelCase							       =				math.log(1e-5		)  # Matches MelGAN training.
										__lowerCAmelCase							       =				4.0  # Largest value for most examples
										__lowerCAmelCase							       =				1_28

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




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase=(-1.0, 1.0)       ,			__lowercase=False		):
										__lowerCAmelCase       ,					__lowerCAmelCase							       =				output_range
										if clip:
																__lowerCAmelCase							       =				torch.clip(__lowercase       ,			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 					_snake_case  (self       ,			__lowercase       ,			__lowercase=(-1.0, 1.0)       ,			__lowercase=False		):
										__lowerCAmelCase       ,					__lowerCAmelCase							       =				input_range
										__lowerCAmelCase							       =				torch.clip(__lowercase       ,			__lowercase       ,			__lowercase		) 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 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase		):
										__lowerCAmelCase							       =				input_tokens > 0
										__lowerCAmelCase       ,					__lowerCAmelCase							       =				self.notes_encoder(
										    encoder_input_tokens=__lowercase       ,			encoder_inputs_mask=__lowercase		)

										__lowerCAmelCase       ,					__lowerCAmelCase							       =				self.continuous_encoder(
										    encoder_inputs=__lowercase       ,			encoder_inputs_mask=__lowercase		)

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




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase		):
										__lowerCAmelCase							       =				noise_time
										if not torch.is_tensor(__lowercase		):
																__lowerCAmelCase							       =				torch.tensor([timesteps]       ,			dtype=torch.long       ,			device=input_tokens.device		)
										elif torch.is_tensor(__lowercase		) 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=__lowercase       ,			decoder_input_tokens=__lowercase       ,			decoder_noise_time=__lowercase		)
										return logits




				@torch.no_grad()
				def __call__(self       ,			__lowercase       ,			__lowercase = None       ,			__lowercase = 1_00       ,			__lowercase = True       ,			__lowercase = "numpy"       ,			__lowercase = None       ,			__lowercase = 1       ,			):
										if (callback_steps is None) or (
										    callback_steps is not None and (not isinstance(__lowercase       ,			__lowercase		) or callback_steps <= 0)
										):
																raise ValueError(
																    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
																    F""" {type(__lowercase		)}."""		)

										__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=__lowercase       ,			device=self.device		)

										for i, encoder_input_tokens in enumerate(__lowercase		):
																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=__lowercase       ,			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(
																    __lowercase       ,			output_range=[-1.0, 1.0]       ,			clip=__lowercase		)

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

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

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

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

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

																__lowerCAmelCase							       =				self.scale_to_features(__lowercase       ,			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(__lowercase       ,			__lowercase		)

																logger.info('''Generated segment'''       ,			__lowercase		)

										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=__lowercase		)





'''simple docstring'''

from torch import nn

def 							__magic_name__(						lowerCamelCase):
						if act_fn in ["swish", "silu"]:
												return nn.SiLU()
						elif act_fn == "mish":
												return nn.Mish()
						elif act_fn == "gelu":
												return nn.GELU()
						else:
												raise ValueError(F"""Unsupported activation function: {act_fn}""")

