






"""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


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

__lowerCamelCase							=  2_56



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				["melgan"]

					def __init__(			self  :      str     ,		__snake_case  :      SpectrogramNotesEncoder     ,		__snake_case  :      SpectrogramContEncoder     ,		__snake_case  :      TaFilmDecoder     ,		__snake_case  :      DDPMScheduler     ,		__snake_case  :      OnnxRuntimeModel if is_onnx_available() else Any     ,		)			->							None:
											super().__init__()

											# From MELGAN
											__magic_name__:	Optional[Any]							=       math.log(1E-5					)  # Matches MelGAN training.
											__magic_name__:	Union[str, Any]							=       4.0  # Largest value for most examples
											__magic_name__:	List[Any]							=       1_2_8

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

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

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

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

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

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

											__magic_name__:	Union[str, Any]							=       self.decoder(
											    encodings_and_masks=__snake_case     ,		decoder_input_tokens=__snake_case     ,		decoder_noise_time=__snake_case					)
											return logits






					@torch.no_grad()
					def __call__(			self  :      str     ,		__snake_case  :      List[List[int]]     ,		__snake_case  :      Optional[torch.Generator] = None     ,		__snake_case  :      int = 1_0_0     ,		__snake_case  :      bool = True     ,		__snake_case  :      str = "numpy"     ,		__snake_case  :      Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,		__snake_case  :      int = 1     ,		)			->							Union[AudioPipelineOutput, Tuple]:
											if (callback_steps is None) or (
											    callback_steps is not None and (not isinstance(__snake_case     ,		__snake_case					) or callback_steps <= 0)
											):
																	raise ValueError(
																	    F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
																	    F' {type(__snake_case					)}.'					)

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

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

																	__magic_name__:	Union[str, Any]							=       self.scale_features(
																	    __snake_case     ,		output_range=[-1.0, 1.0]     ,		clip=__snake_case					)

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

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

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

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

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

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

																	__magic_name__:	Dict							=       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(__snake_case     ,		__snake_case					)

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

											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":
																	__magic_name__:	Optional[int]							=       self.melgan(input_features=full_pred_mel.astype(np.floataa					)					)
											else:
																	__magic_name__:	Union[str, Any]							=       full_pred_mel

											if not return_dict:
																	return (output,)

											return AudioPipelineOutput(audios=__snake_case					)











"""simple docstring"""





import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np



def   a    (     __UpperCAmelCase      :    bytes      ,   __UpperCAmelCase      :    int  )    ->							np.array:
						__magic_name__:	Optional[int]							=       f'{sampling_rate}'
						__magic_name__:	Union[str, Any]							=       """1"""
						__magic_name__:	Tuple							=       """f32le"""
						__magic_name__:	Optional[Any]							=       [
						    """ffmpeg""",
						    """-i""",
						    """pipe:0""",
						    """-ac""",
						    ac,
						    """-ar""",
						    ar,
						    """-f""",
						    format_for_conversion,
						    """-hide_banner""",
						    """-loglevel""",
						    """quiet""",
						    """pipe:1""",
						]

						try:
												with subprocess.Popen(__UpperCAmelCase      ,   stdin=subprocess.PIPE      ,   stdout=subprocess.PIPE  ) as ffmpeg_process:
																		__magic_name__:	Any							=       ffmpeg_process.communicate(__UpperCAmelCase  )
						except FileNotFoundError as error:
												raise ValueError("""ffmpeg was not found but is required to load audio files from filename"""  ) from error
						__magic_name__:	Optional[Any]							=       output_stream[0]
						__magic_name__:	Optional[int]							=       np.frombuffer(__UpperCAmelCase      ,   np.floataa  )
						if audio.shape[0] == 0:
												raise ValueError("""Malformed soundfile"""  )
						return audio



def   a    (     __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    float      ,   __UpperCAmelCase      :    str = "f32le"      ,   )    ->							Optional[int]:
						__magic_name__:	List[Any]							=       f'{sampling_rate}'
						__magic_name__:	Optional[int]							=       """1"""
						if format_for_conversion == "s16le":
												__magic_name__:	Dict							=       2
						elif format_for_conversion == "f32le":
												__magic_name__:	List[str]							=       4
						else:
												raise ValueError(f'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'  )

						__magic_name__:	str							=       platform.system()
						if system == "Linux":
												__magic_name__:	Optional[Any]							=       """alsa"""
												__magic_name__:	Optional[Any]							=       """default"""
						elif system == "Darwin":
												__magic_name__:	Union[str, Any]							=       """avfoundation"""
												__magic_name__:	Dict							=       """:0"""
						elif system == "Windows":
												__magic_name__:	Any							=       """dshow"""
												__magic_name__:	Union[str, Any]							=       """default"""

						__magic_name__:	Union[str, Any]							=       [
						    """ffmpeg""",
						    """-f""",
						    format_,
						    """-i""",
						    input_,
						    """-ac""",
						    ac,
						    """-ar""",
						    ar,
						    """-f""",
						    format_for_conversion,
						    """-fflags""",
						    """nobuffer""",
						    """-hide_banner""",
						    """-loglevel""",
						    """quiet""",
						    """pipe:1""",
						]
						__magic_name__:	Tuple							=       int(round(sampling_rate * chunk_length_s  )  ) * size_of_sample
						__magic_name__:	Union[str, Any]							=       _ffmpeg_stream(__UpperCAmelCase      ,   __UpperCAmelCase  )
						for item in iterator:
												yield item



def   a    (     __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    float      ,   __UpperCAmelCase      :    Optional[int] = None      ,   __UpperCAmelCase      :    Optional[Union[Tuple[float, float], float]] = None      ,   __UpperCAmelCase      :    str = "f32le"      ,   )    ->							List[Any]:
						if stream_chunk_s is not None:
												__magic_name__:	Optional[int]							=       stream_chunk_s
						else:
												__magic_name__:	List[str]							=       chunk_length_s

						__magic_name__:	Optional[int]							=       ffmpeg_microphone(__UpperCAmelCase      ,   __UpperCAmelCase      ,   format_for_conversion=__UpperCAmelCase  )
						if format_for_conversion == "s16le":
												__magic_name__:	str							=       np.intaa
												__magic_name__:	str							=       2
						elif format_for_conversion == "f32le":
												__magic_name__:	Optional[Any]							=       np.floataa
												__magic_name__:	Dict							=       4
						else:
												raise ValueError(f'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'  )

						if stride_length_s is None:
												__magic_name__:	List[Any]							=       chunk_length_s / 6
						__magic_name__:	Optional[Any]							=       int(round(sampling_rate * chunk_length_s  )  ) * size_of_sample
						if isinstance(__UpperCAmelCase      ,   (int, float)  ):
												__magic_name__:	Union[str, Any]							=       [stride_length_s, stride_length_s]

						__magic_name__:	List[str]							=       int(round(sampling_rate * stride_length_s[0]  )  ) * size_of_sample
						__magic_name__:	List[Any]							=       int(round(sampling_rate * stride_length_s[1]  )  ) * size_of_sample
						__magic_name__:	List[str]							=       datetime.datetime.now()
						__magic_name__:	Optional[Any]							=       datetime.timedelta(seconds=__UpperCAmelCase  )
						for item in chunk_bytes_iter(__UpperCAmelCase      ,   __UpperCAmelCase      ,   stride=(stride_left, stride_right)      ,   stream=__UpperCAmelCase  ):
												# Put everything back in numpy scale
												__magic_name__:	int							=       np.frombuffer(item["""raw"""]      ,   dtype=__UpperCAmelCase  )
												__magic_name__:	int							=       (
												    item["""stride"""][0] // size_of_sample,
												    item["""stride"""][1] // size_of_sample,
												)
												__magic_name__:	Union[str, Any]							=       sampling_rate
												audio_time += delta
												if datetime.datetime.now() > audio_time + 1_0 * delta:
																		# We're late !! SKIP
																		continue
												yield item



def   a    (     __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    Tuple[int, int]      ,   __UpperCAmelCase      :    bool = False  )    ->							Union[str, Any]:
						__magic_name__:	Tuple							=       B""""""
						__magic_name__,			__magic_name__:	int							=       stride
						if stride_left + stride_right >= chunk_len:
												raise ValueError(
												    f'Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}'  )
						__magic_name__:	int							=       0
						for raw in iterator:
												acc += raw
												if stream and len(__UpperCAmelCase  ) < chunk_len:
																		__magic_name__:	Optional[int]							=       (_stride_left, 0)
																		yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
												else:
																		while len(__UpperCAmelCase  ) >= chunk_len:
																								# We are flushing the accumulator
																								__magic_name__:	Union[str, Any]							=       (_stride_left, stride_right)
																								__magic_name__:	Any							=       {"""raw""": acc[:chunk_len], """stride""": stride}
																								if stream:
																														__magic_name__:	str							=       False
																								yield item
																								__magic_name__:	str							=       stride_left
																								__magic_name__:	Optional[Any]							=       acc[chunk_len - stride_left - stride_right :]
    # Last chunk
						if len(__UpperCAmelCase  ) > stride_left:
												__magic_name__:	List[Any]							=       {"""raw""": acc, """stride""": (_stride_left, 0)}
												if stream:
																		__magic_name__:	Union[str, Any]							=       False
												yield item



def   a    (     __UpperCAmelCase      :    Tuple      ,   __UpperCAmelCase      :    int  )    ->							List[Any]:
						__magic_name__:	str							=       2**2_4  # 16Mo
						try:
												with subprocess.Popen(__UpperCAmelCase      ,   stdout=subprocess.PIPE      ,   bufsize=__UpperCAmelCase  ) as ffmpeg_process:
																		while True:
																								__magic_name__:	Dict							=       ffmpeg_process.stdout.read(__UpperCAmelCase  )
																								if raw == b"":
																														break
																								yield raw
						except FileNotFoundError as error:
												raise ValueError("""ffmpeg was not found but is required to stream audio files from filename"""  ) from error



