






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

UpperCamelCase__     =	256

class     __SCREAMING_SNAKE_CASE   (       _a    ):
							snake_case       : Union[str, Any]         =    ["""melgan"""]






							def __init__(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	):
														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					,	__lowerCAmelCase					,	__lowerCAmelCase=(-1.0, 1.0)					,	__lowerCAmelCase=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					,	__lowerCAmelCase					,	__lowerCAmelCase=(-1.0, 1.0)					,	__lowerCAmelCase=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					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														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					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														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					,	__lowerCAmelCase					,	__lowerCAmelCase = None					,	__lowerCAmelCase = 100					,	__lowerCAmelCase = True					,	__lowerCAmelCase = "numpy"					,	__lowerCAmelCase = None					,	__lowerCAmelCase = 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					)











import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


UpperCamelCase__     =	logging.get_logger(__name__)

class     __SCREAMING_SNAKE_CASE   (       _a    ):
							snake_case       : List[Any]         =    """upernet"""






							def __init__(  self					,	__lowerCAmelCase=None					,	__lowerCAmelCase=512					,	__lowerCAmelCase=0.02					,	__lowerCAmelCase=[1, 2, 3, 6]					,	__lowerCAmelCase=True					,	__lowerCAmelCase=0.4					,	__lowerCAmelCase=384					,	__lowerCAmelCase=256					,	__lowerCAmelCase=1					,	__lowerCAmelCase=False					,	__lowerCAmelCase=255					,	**__lowerCAmelCase					,	):
														super().__init__(**__lowerCAmelCase					)

														if backbone_config is None:
																					logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""					)
																					UpperCamelCase__			       =     CONFIG_MAPPING["""resnet"""](out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]					)
														elif isinstance(__lowerCAmelCase					,	__lowerCAmelCase					):
																					UpperCamelCase__			       =     backbone_config.get("""model_type"""					)
																					UpperCamelCase__			       =     CONFIG_MAPPING[backbone_model_type]
																					UpperCamelCase__			       =     config_class.from_dict(__lowerCAmelCase					)

														UpperCamelCase__			       =     backbone_config
														UpperCamelCase__			       =     hidden_size
														UpperCamelCase__			       =     initializer_range
														UpperCamelCase__			       =     pool_scales
														UpperCamelCase__			       =     use_auxiliary_head
														UpperCamelCase__			       =     auxiliary_loss_weight
														UpperCamelCase__			       =     auxiliary_in_channels
														UpperCamelCase__			       =     auxiliary_channels
														UpperCamelCase__			       =     auxiliary_num_convs
														UpperCamelCase__			       =     auxiliary_concat_input
														UpperCamelCase__			       =     loss_ignore_index







							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     copy.deepcopy(self.__dict__					)
														UpperCamelCase__			       =     self.backbone_config.to_dict()
														UpperCamelCase__			       =     self.__class__.model_type
														return output




