

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

UpperCamelCase_							=					256





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):
				lowerCamelCase_							=						['melgan']







				def __init__(					self    :		Tuple		,				UpperCAmelCase__    :		SpectrogramNotesEncoder		,				UpperCAmelCase__    :		SpectrogramContEncoder		,				UpperCAmelCase__    :		TaFilmDecoder		,				UpperCAmelCase__    :		DDPMScheduler		,				UpperCAmelCase__    :		OnnxRuntimeModel if is_onnx_available() else Any		,				):
										'''simple docstring'''
										super().__init__()

										# From MELGAN
										lowercase   :       List[str]								=math.log(1E-5      )  # Matches MelGAN training.
										lowercase   :       Any								=4.0  # Largest value for most examples
										lowercase   :       str								=128

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







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

										lowercase		,  lowercase   :       Union[str, Any]								=self.continuous_encoder(
										    encoder_inputs=UpperCAmelCase__		,				encoder_inputs_mask=UpperCAmelCase__      )

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







				def    lowerCamelCase_  (					self    :		Any		,				UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		Union[str, Any]		,				UpperCAmelCase__    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								=noise_time
										if not torch.is_tensor(UpperCAmelCase__      ):
																lowercase   :       Dict								=torch.tensor([timesteps]		,				dtype=torch.long		,				device=input_tokens.device      )
										elif torch.is_tensor(UpperCAmelCase__      ) and len(timesteps.shape      ) == 0:
																lowercase   :       Any								=timesteps[None].to(input_tokens.device      )

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

										lowercase   :       Optional[Any]								=self.decoder(
										    encodings_and_masks=UpperCAmelCase__		,				decoder_input_tokens=UpperCAmelCase__		,				decoder_noise_time=UpperCAmelCase__      )
										return logits







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

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

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

																lowercase   :       List[str]								=self.scale_features(
																    UpperCAmelCase__		,				output_range=[-1.0, 1.0]		,				clip=UpperCAmelCase__      )

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

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

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

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

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

																lowercase   :       Union[str, Any]								=self.scale_to_features(UpperCAmelCase__		,				input_range=[-1.0, 1.0]      )
																lowercase   :       str								=mel[:1]
																lowercase   :       List[Any]								=mel.cpu().float().numpy()

																lowercase   :       Any								=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(UpperCAmelCase__		,				UpperCAmelCase__      )

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

										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":
																lowercase   :       Dict								=self.melgan(input_features=full_pred_mel.astype(np.floataa      )      )
										else:
																lowercase   :       Tuple								=full_pred_mel

										if not return_dict:
																return (output,)

										return AudioPipelineOutput(audios=UpperCAmelCase__      )








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_							=					{"""configuration_plbart""": ["""PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PLBartConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_							=					["""PLBartTokenizer"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_							=					[
							    """PLBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """PLBartForCausalLM""",
							    """PLBartForConditionalGeneration""",
							    """PLBartForSequenceClassification""",
							    """PLBartModel""",
							    """PLBartPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_plbart import PLBartTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_plbart import (
														    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PLBartForCausalLM,
														    PLBartForConditionalGeneration,
														    PLBartForSequenceClassification,
														    PLBartModel,
														    PLBartPreTrainedModel,
														)


else:
							import sys

							UpperCamelCase_							=					_LazyModule(__name__, globals()["""__file__"""], _import_structure)





