






import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


a__       =     logging.get_logger(__name__)


class    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



				_lowercase				:   Optional[int]      =  ['''input_values''', '''attention_mask''']



				def __init__(      self    :    Tuple			,		UpperCamelCase__    :    int = 1			,		UpperCamelCase__    :    int = 1_6_0_0_0			,		UpperCamelCase__    :    float = 0.0			,		UpperCamelCase__    :    bool = False			,		UpperCamelCase__    :    int = 8_0			,		UpperCamelCase__    :    int = 1_6			,		UpperCamelCase__    :    int = 6_4			,		UpperCamelCase__    :    str = "hann_window"			,		UpperCamelCase__    :    float = 1.0			,		UpperCamelCase__    :    float = 8_0			,		UpperCamelCase__    :    float = 7_6_0_0			,		UpperCamelCase__    :    float = 1E-10			,		UpperCamelCase__    :    int = 2			,		UpperCamelCase__    :    bool = True			,		**UpperCamelCase__    :    Optional[int]			,		):






											'''simple docstring'''







											super().__init__(feature_size=UpperCamelCase__			,		sampling_rate=UpperCamelCase__			,		padding_value=UpperCamelCase__			,		**UpperCamelCase__)
											snake_case__	  =       do_normalize
											snake_case__	  =       return_attention_mask

											snake_case__	  =       num_mel_bins
											snake_case__	  =       hop_length
											snake_case__	  =       win_length
											snake_case__	  =       win_function
											snake_case__	  =       frame_signal_scale
											snake_case__	  =       fmin
											snake_case__	  =       fmax
											snake_case__	  =       mel_floor
											snake_case__	  =       reduction_factor

											snake_case__	  =       win_length * sampling_rate // 1_0_0_0
											snake_case__	  =       hop_length * sampling_rate // 1_0_0_0
											snake_case__	  =       optimal_fft_length(self.sample_size)
											snake_case__	  =       (self.n_fft // 2) + 1

											snake_case__	  =       window_function(window_length=self.sample_size			,		name=self.win_function			,		periodic=UpperCamelCase__)

											snake_case__	  =       mel_filter_bank(
											    num_frequency_bins=self.n_freqs			,		num_mel_filters=self.num_mel_bins			,		min_frequency=self.fmin			,		max_frequency=self.fmax			,		sampling_rate=self.sampling_rate			,		norm="""slaney"""			,		mel_scale="""slaney"""			,		)

											if frame_signal_scale != 1.0:
																		warnings.warn(
																		    """The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"""			,		UpperCamelCase__			,		)
											if reduction_factor != 2.0:
																		warnings.warn(
																		    """The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"""			,		UpperCamelCase__			,		)



				@staticmethod
				# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
				def 					__magic_name__							(      UpperCamelCase__    :    List[np.ndarray]			,		UpperCamelCase__    :    List[np.ndarray]			,		UpperCamelCase__    :    float = 0.0):






											'''simple docstring'''







											if attention_mask is not None:
																		snake_case__	  =       np.array(UpperCamelCase__			,		np.intaa)
																		snake_case__	  =       []

																		for vector, length in zip(UpperCamelCase__			,		attention_mask.sum(-1)):
																									snake_case__	  =       (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7)
																									if length < normed_slice.shape[0]:
																																snake_case__	  =       padding_value

																									normed_input_values.append(UpperCamelCase__)
											else:
																		snake_case__	  =       [(x - x.mean()) / np.sqrt(x.var() + 1E-7) for x in input_values]

											return normed_input_values



				def 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    np.ndarray			,		):






											'''simple docstring'''







											snake_case__	  =       spectrogram(
											    UpperCamelCase__			,		window=self.window			,		frame_length=self.sample_size			,		hop_length=self.sample_stride			,		fft_length=self.n_fft			,		mel_filters=self.mel_filters			,		mel_floor=self.mel_floor			,		log_mel="""log10"""			,		)
											return log_mel_spec.T



				def __call__(      self    :    List[str]			,		UpperCamelCase__    :    Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None			,		UpperCamelCase__    :    Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None			,		UpperCamelCase__    :    Union[bool, str, PaddingStrategy] = False			,		UpperCamelCase__    :    Optional[int] = None			,		UpperCamelCase__    :    bool = False			,		UpperCamelCase__    :    Optional[int] = None			,		UpperCamelCase__    :    Optional[bool] = None			,		UpperCamelCase__    :    Optional[Union[str, TensorType]] = None			,		UpperCamelCase__    :    Optional[int] = None			,		**UpperCamelCase__    :    List[str]			,		):






											'''simple docstring'''







											if audio is None and audio_target is None:
																		raise ValueError("""You must provide either `audio` or `audio_target` values.""")

											if sampling_rate is not None:
																		if sampling_rate != self.sampling_rate:
																									raise ValueError(
																									    F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
																									    F''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
																									    F''' {self.sampling_rate} and not {sampling_rate}.''')
											else:
																		logger.warning(
																		    """It is strongly recommended to pass the ``sampling_rate`` argument to this function. """
																		    """Failing to do so can result in silent errors that might be hard to debug.""")

											if audio is not None:
																		snake_case__	  =       self._process_audio(
																		    UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		**UpperCamelCase__			,		)
											else:
																		snake_case__	  =       None

											if audio_target is not None:
																		snake_case__	  =       self._process_audio(
																		    UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		**UpperCamelCase__			,		)

																		if inputs is None:
																									return inputs_target
																		else:
																									snake_case__	  =       inputs_target["""input_values"""]
																									snake_case__	  =       inputs_target.get("""attention_mask""")
																									if decoder_attention_mask is not None:
																																snake_case__	  =       decoder_attention_mask

											return inputs



				def 					__magic_name__							(      self    :    Optional[int]			,		UpperCamelCase__    :    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,		UpperCamelCase__    :    bool = False			,		UpperCamelCase__    :    Union[bool, str, PaddingStrategy] = False			,		UpperCamelCase__    :    Optional[int] = None			,		UpperCamelCase__    :    bool = False			,		UpperCamelCase__    :    Optional[int] = None			,		UpperCamelCase__    :    Optional[bool] = None			,		UpperCamelCase__    :    Optional[Union[str, TensorType]] = None			,		**UpperCamelCase__    :    List[str]			,		):






											'''simple docstring'''







											snake_case__	  =       isinstance(UpperCamelCase__			,		np.ndarray) and len(speech.shape) > 1
											if is_batched_numpy and len(speech.shape) > 2:
																		raise ValueError(F'''Only mono-channel audio is supported for input to {self}''')
											snake_case__	  =       is_batched_numpy or (
											    isinstance(UpperCamelCase__			,		(list, tuple)) and (isinstance(speech[0]			,		(np.ndarray, tuple, list)))
											)

											if is_batched:
																		snake_case__	  =       [np.asarray(UpperCamelCase__			,		dtype=np.floataa) for speech in speech]
											elif not is_batched and not isinstance(UpperCamelCase__			,		np.ndarray):
																		snake_case__	  =       np.asarray(UpperCamelCase__			,		dtype=np.floataa)
											elif isinstance(UpperCamelCase__			,		np.ndarray) and speech.dtype is np.dtype(np.floataa):
																		snake_case__	  =       speech.astype(np.floataa)

											# always return batch
											if not is_batched:
																		snake_case__	  =       [speech]

											# needed to make pad() work on spectrogram inputs
											snake_case__	  =       self.feature_size

											# convert into correct format for padding
											if is_target:
																		snake_case__	  =       [self._extract_mel_features(UpperCamelCase__) for waveform in speech]
																		snake_case__	  =       BatchFeature({"""input_values""": features})
																		snake_case__	  =       self.num_mel_bins
											else:
																		snake_case__	  =       BatchFeature({"""input_values""": speech})

											snake_case__	  =       self.pad(
											    UpperCamelCase__			,		padding=UpperCamelCase__			,		max_length=UpperCamelCase__			,		truncation=UpperCamelCase__			,		pad_to_multiple_of=UpperCamelCase__			,		return_attention_mask=UpperCamelCase__			,		**UpperCamelCase__			,		)

											snake_case__	  =       feature_size_hack

											# convert input values to correct format
											snake_case__	  =       padded_inputs["""input_values"""]
											if not isinstance(input_values[0]			,		np.ndarray):
																		snake_case__	  =       [np.asarray(UpperCamelCase__			,		dtype=np.floataa) for array in input_values]
											elif (
											    not isinstance(UpperCamelCase__			,		np.ndarray)
											    and isinstance(input_values[0]			,		np.ndarray)
											    and input_values[0].dtype is np.dtype(np.floataa)
											):
																		snake_case__	  =       [array.astype(np.floataa) for array in input_values]
											elif isinstance(UpperCamelCase__			,		np.ndarray) and input_values.dtype is np.dtype(np.floataa):
																		snake_case__	  =       input_values.astype(np.floataa)

											# convert attention_mask to correct format
											snake_case__	  =       padded_inputs.get("""attention_mask""")
											if attention_mask is not None:
																		snake_case__	  =       [np.asarray(UpperCamelCase__			,		dtype=np.intaa) for array in attention_mask]

											# zero-mean and unit-variance normalization
											if not is_target and self.do_normalize:
																		snake_case__	  =       (
																		    attention_mask
																		    if self._get_padding_strategies(UpperCamelCase__			,		max_length=UpperCamelCase__) is not PaddingStrategy.DO_NOT_PAD
																		    else None
																		)
																		snake_case__	  =       self.zero_mean_unit_var_norm(
																		    padded_inputs["""input_values"""]			,		attention_mask=UpperCamelCase__			,		padding_value=self.padding_value)

											if return_tensors is not None:
																		snake_case__	  =       padded_inputs.convert_to_tensors(UpperCamelCase__)

											return padded_inputs



				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											snake_case__	  =       super().to_dict()

											# Don't serialize these as they are derived from the other properties.
											snake_case__	  =       ["""window""", """mel_filters""", """sample_size""", """sample_stride""", """n_fft""", """n_freqs"""]
											for name in names:
																		if name in output:
																									del output[name]

											return output










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


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

a__       =     2_5_6


class    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



				_lowercase				:   str      =  ['''melgan''']



				def __init__(      self    :    int			,		UpperCamelCase__    :    SpectrogramNotesEncoder			,		UpperCamelCase__    :    SpectrogramContEncoder			,		UpperCamelCase__    :    TaFilmDecoder			,		UpperCamelCase__    :    DDPMScheduler			,		UpperCamelCase__    :    OnnxRuntimeModel if is_onnx_available() else Any			,		):






											'''simple docstring'''







											super().__init__()

											# From MELGAN
											snake_case__	  =       math.log(1E-5)  # Matches MelGAN training.
											snake_case__	  =       4.0  # Largest value for most examples
											snake_case__	  =       1_2_8

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



				def 					__magic_name__							(      self    :    Tuple			,		UpperCamelCase__    :    Any			,		UpperCamelCase__    :    int=(-1.0, 1.0)			,		UpperCamelCase__    :    Tuple=False):






											'''simple docstring'''







											snake_case__  , snake_case__	  =       output_range
											if clip:
																		snake_case__	  =       torch.clip(UpperCamelCase__			,		self.min_value			,		self.max_value)
											# Scale to [0, 1].
											snake_case__	  =       (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 					__magic_name__							(      self    :    Optional[Any]			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    List[str]=(-1.0, 1.0)			,		UpperCamelCase__    :    Optional[Any]=False):






											'''simple docstring'''







											snake_case__  , snake_case__	  =       input_range
											snake_case__	  =       torch.clip(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__) if clip else outputs
											# Scale to [0, 1].
											snake_case__	  =       (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 					__magic_name__							(      self    :    Optional[int]			,		UpperCamelCase__    :    Union[str, Any]			,		UpperCamelCase__    :    Optional[int]			,		UpperCamelCase__    :    Any):






											'''simple docstring'''







											snake_case__	  =       input_tokens > 0
											snake_case__  , snake_case__	  =       self.notes_encoder(
											    encoder_input_tokens=UpperCamelCase__			,		encoder_inputs_mask=UpperCamelCase__)

											snake_case__  , snake_case__	  =       self.continuous_encoder(
											    encoder_inputs=UpperCamelCase__			,		encoder_inputs_mask=UpperCamelCase__)

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



				def 					__magic_name__							(      self    :    List[Any]			,		UpperCamelCase__    :    Dict			,		UpperCamelCase__    :    Any			,		UpperCamelCase__    :    int):






											'''simple docstring'''







											snake_case__	  =       noise_time
											if not torch.is_tensor(UpperCamelCase__):
																		snake_case__	  =       torch.tensor([timesteps]			,		dtype=torch.long			,		device=input_tokens.device)
											elif torch.is_tensor(UpperCamelCase__) and len(timesteps.shape) == 0:
																		snake_case__	  =       timesteps[None].to(input_tokens.device)

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

											snake_case__	  =       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 = 1_0_0			,		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__)}.''')

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

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

																		snake_case__	  =       self.scale_features(
																		    UpperCamelCase__			,		output_range=[-1.0, 1.0]			,		clip=UpperCamelCase__)

																		snake_case__	  =       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
																		snake_case__	  =       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)):
																									snake_case__	  =       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
																									snake_case__	  =       self.scheduler.step(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		generator=UpperCamelCase__).prev_sample

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

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

											if not return_dict:
																		return (output,)

											return AudioPipelineOutput(audios=UpperCamelCase__)
