
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
						from .features import FeatureType


_snake_case      :							Any     	=    False, False, False





@dataclass
class   UpperCamelCase_      :







						'''simple docstring'''



						UpperCamelCase      :  Dict				   =					None
						UpperCamelCase      :  Union[str, Any]				   =					True
						UpperCamelCase      :  Any				   =					True
						UpperCamelCase      :  Tuple				   =					None
						# Automatically constructed
						UpperCamelCase      :  Dict				   =					'''dict'''
						UpperCamelCase      :  Optional[int]				   =					pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}		)
						UpperCamelCase      :  Tuple				   =					field(default='''Audio'''		,    init=_lowercase		,    repr=_lowercase		)





						def __call__(			self							:Dict     )  ->str:
										return self.pa_type





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:Union[str, bytes, dict]     )  ->dict:
										try:
														import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
										except ImportError as err:
														raise ImportError("To support encoding audio data, please install 'soundfile'."     ) from err
										if isinstance(_snake_case		,    _snake_case     ):
														return {"bytes": None, "path": value}
										elif isinstance(_snake_case		,    _snake_case     ):
														return {"bytes": value, "path": None}
										elif "array" in value:
														# convert the audio array to wav bytes
														lowercase         =      BytesIO()
														sf.write(_snake_case		,    value["array"]		,    value["sampling_rate"]		,    format="wav"     )
														return {"bytes": buffer.getvalue(), "path": None}
										elif value.get("path"     ) is not None and os.path.isfile(value["path"]     ):
														# we set "bytes": None to not duplicate the data if they're already available locally
														if value["path"].endswith("pcm"     ):
																		# "PCM" only has raw audio bytes
																		if value.get("sampling_rate"     ) is None:
																						# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
																						raise KeyError("To use PCM files, please specify a 'sampling_rate' in Audio object"     )
																		if value.get("bytes"     ):
																						# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
																						lowercase         =      np.frombuffer(value["bytes"]		,    dtype=np.intaa     ).astype(np.floataa     ) / 32767
																		else:
																						lowercase         =      np.memmap(value["path"]		,    dtype="h"		,    mode="r"     ).astype(np.floataa     ) / 32767

																		lowercase         =      BytesIO(bytes()     )
																		sf.write(_snake_case		,    _snake_case		,    value["sampling_rate"]		,    format="wav"     )
																		return {"bytes": buffer.getvalue(), "path": None}
														else:
																		return {"bytes": None, "path": value.get("path"     )}
										elif value.get("bytes"     ) is not None or value.get("path"     ) is not None:
														# store the audio bytes, and path is used to infer the audio format using the file extension
														return {"bytes": value.get("bytes"     ), "path": value.get("path"     )}
										else:
														raise ValueError(
														    F'''An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'''     )





						def        SCREAMING_SNAKE_CASE(			self							:Any		,    lowerCAmelCase__							:dict		,    lowerCAmelCase__							:Optional[Dict[str, Union[str, bool, None]]] = None     )  ->dict:
										if not self.decode:
														raise RuntimeError("Decoding is disabled for this feature. Please use Audio(decode=True) instead."     )

										lowercase     ,	lowercase         =      (value["path"], BytesIO(value["bytes"]     )) if value["bytes"] is not None else (value["path"], None)
										if path is None and file is None:
														raise ValueError(F'''An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.'''     )

										try:
														import librosa
														import soundfile as sf
										except ImportError as err:
														raise ImportError("To support decoding audio files, please install 'librosa' and 'soundfile'."     ) from err

										lowercase         =      xsplitext(_snake_case     )[1][1:].lower() if path is not None else None
										if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
														raise RuntimeError(
														    "Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, "
														    "You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. "     )
										elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
														raise RuntimeError(
														    "Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, "
														    "You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. "     )

										if file is None:
														lowercase         =      token_per_repo_id or {}
														lowercase         =      path.split("::"     )[-1]
														try:
																		lowercase         =      string_to_dict(_snake_case		,    config.HUB_DATASETS_URL     )["repo_id"]
																		lowercase         =      token_per_repo_id[repo_id]
														except (ValueError, KeyError):
																		lowercase         =      None

														with xopen(_snake_case		,    "rb"		,    use_auth_token=_snake_case     ) as f:
																		lowercase     ,	lowercase         =      sf.read(_snake_case     )

										else:
														lowercase     ,	lowercase         =      sf.read(_snake_case     )

										lowercase         =      array.T
										if self.mono:
														lowercase         =      librosa.to_mono(_snake_case     )
										if self.sampling_rate and self.sampling_rate != sampling_rate:
														lowercase         =      librosa.resample(_snake_case		,    orig_sr=_snake_case		,    target_sr=self.sampling_rate     )
														lowercase         =      self.sampling_rate

										return {"path": path, "array": array, "sampling_rate": sampling_rate}





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->Union["FeatureType", Dict[str, "FeatureType"]]:
										from .features import Value

										if self.decode:
														raise ValueError("Cannot flatten a decoded Audio feature."     )
										return {
										    "bytes": Value("binary"     ),
										    "path": Value("string"     ),
										}





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    lowerCAmelCase__							:Union[pa.StringArray, pa.StructArray]     )  ->pa.StructArray:
										if pa.types.is_string(storage.type     ):
														lowercase         =      pa.array([None] * len(_snake_case     )		,    type=pa.binary()     )
														lowercase         =      pa.StructArray.from_arrays([bytes_array, storage]		,    ["bytes", "path"]		,    mask=storage.is_null()     )
										elif pa.types.is_binary(storage.type     ):
														lowercase         =      pa.array([None] * len(_snake_case     )		,    type=pa.string()     )
														lowercase         =      pa.StructArray.from_arrays([storage, path_array]		,    ["bytes", "path"]		,    mask=storage.is_null()     )
										elif pa.types.is_struct(storage.type     ) and storage.type.get_all_field_indices("array"     ):
														lowercase         =      pa.array([Audio().encode_example(_snake_case     ) if x is not None else None for x in storage.to_pylist()]     )
										elif pa.types.is_struct(storage.type     ):
														if storage.type.get_field_index("bytes"     ) >= 0:
																		lowercase         =      storage.field("bytes"     )
														else:
																		lowercase         =      pa.array([None] * len(_snake_case     )		,    type=pa.binary()     )
														if storage.type.get_field_index("path"     ) >= 0:
																		lowercase         =      storage.field("path"     )
														else:
																		lowercase         =      pa.array([None] * len(_snake_case     )		,    type=pa.string()     )
														lowercase         =      pa.StructArray.from_arrays([bytes_array, path_array]		,    ["bytes", "path"]		,    mask=storage.is_null()     )
										return array_cast(_snake_case		,    self.pa_type     )





						def        SCREAMING_SNAKE_CASE(			self							:Tuple		,    lowerCAmelCase__							:pa.StructArray     )  ->pa.StructArray:

										@no_op_if_value_is_null
										def path_to_bytes(lowerCAmelCase__							:List[Any]     ):
														with xopen(_snake_case		,    "rb"     ) as f:
																		lowercase         =      f.read()
														return bytes_

										lowercase         =      pa.array(
										    [
										        (path_to_bytes(x["path"]     ) if x["bytes"] is None else x["bytes"]) if x is not None else None
										        for x in storage.to_pylist()
										    ]		,    type=pa.binary()		,    )
										lowercase         =      pa.array(
										    [os.path.basename(_snake_case     ) if path is not None else None for path in storage.field("path"     ).to_pylist()]		,    type=pa.string()		,    )
										lowercase         =      pa.StructArray.from_arrays([bytes_array, path_array]		,    ["bytes", "path"]		,    mask=bytes_array.is_null()     )
										return array_cast(_snake_case		,    self.pa_type     )


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

UpperCamelCase_       :    List[Any]						      =			256



class __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







			snake_case						   =					["melgan"]




			def __init__(					self    :       Dict							, _snake_case    :       SpectrogramNotesEncoder							, _snake_case    :       SpectrogramContEncoder							, _snake_case    :       TaFilmDecoder							, _snake_case    :       DDPMScheduler							, _snake_case    :       OnnxRuntimeModel if is_onnx_available() else Any							, )   ->			None:







								"""simple docstring"""
								super().__init__()

								# From MELGAN
								A_								=    math.log(1e-5						)  # Matches MelGAN training.
								A_								=    4.0  # Largest value for most examples
								A_								=    128

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




			def   lowerCamelCase__  (					self    :       Union[str, Any]							, _snake_case    :       Tuple							, _snake_case    :       str=(-1.0, 1.0)							, _snake_case    :       int=False						)   ->			str:







								"""simple docstring"""
								A_			,     A_								=    output_range
								if clip:
													A_								=    torch.clip(_snake_case							, self.min_value							, self.max_value						)
								# Scale to [0, 1].
								A_								=    (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    :       Tuple							, _snake_case    :       Optional[Any]=(-1.0, 1.0)							, _snake_case    :       List[str]=False						)   ->			List[str]:







								"""simple docstring"""
								A_			,     A_								=    input_range
								A_								=    torch.clip(_snake_case							, _snake_case							, _snake_case						) if clip else outputs
								# Scale to [0, 1].
								A_								=    (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    :       Union[str, Any]							, _snake_case    :       str							, _snake_case    :       List[Any]							, _snake_case    :       Union[str, Any]						)   ->			List[str]:







								"""simple docstring"""
								A_								=    input_tokens > 0
								A_			,     A_								=    self.notes_encoder(
								    encoder_input_tokens=_snake_case							, encoder_inputs_mask=_snake_case						)

								A_			,     A_								=    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    :       int							, _snake_case    :       Tuple						)   ->			Optional[int]:







								"""simple docstring"""
								A_								=    noise_time
								if not torch.is_tensor(_snake_case						):
													A_								=    torch.tensor([timesteps]							, dtype=torch.long							, device=input_tokens.device						)
								elif torch.is_tensor(_snake_case						) and len(timesteps.shape						) == 0:
													A_								=    timesteps[None].to(input_tokens.device						)

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

								A_								=    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    :       List[Any]							, _snake_case    :       List[List[int]]							, _snake_case    :       Optional[torch.Generator] = None							, _snake_case    :       int = 100							, _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]:







								"""simple docstring"""
								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						)}.'						)

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

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

													A_								=    self.scale_features(
													    _snake_case							, output_range=[-1.0, 1.0]							, clip=_snake_case						)

													A_								=    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
													A_								=    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						)						):
																		A_								=    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
																		A_								=    self.scheduler.step(_snake_case							, _snake_case							, _snake_case							, generator=_snake_case						).prev_sample

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

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

								if not return_dict:
													return (output,)

								return AudioPipelineOutput(audios=_snake_case						)




