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_			    =							2_5_6




class  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    Optional[Any]  			=   ['''melgan''']



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



    """simple docstring"""



    super().__init__()

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

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



   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  Optional[int]		,  _lowercase					:  Optional[int]=(-1.0, 1.0)		,  _lowercase					:  List[str]=False							):



    """simple docstring"""



    _UpperCamelCase	,							_UpperCamelCase:     List[str]							= output_range
    if clip:
     _UpperCamelCase:     Any							= torch.clip(_lowercase		,  self.min_value		,  self.max_value							)
    # Scale to [0, 1].
    _UpperCamelCase:     Union[str, Any]							= (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					:  str		,  _lowercase					:  Optional[int]		,  _lowercase					:  List[Any]=(-1.0, 1.0)		,  _lowercase					:  Optional[int]=False							):



    """simple docstring"""



    _UpperCamelCase	,							_UpperCamelCase:     int							= input_range
    _UpperCamelCase:     Optional[Any]							= torch.clip(_lowercase		,  _lowercase		,  _lowercase							) if clip else outputs
    # Scale to [0, 1].
    _UpperCamelCase:     List[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					:  Tuple		,  _lowercase					:  Tuple		,  _lowercase					:  Optional[int]		,  _lowercase					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     int							= input_tokens > 0
    _UpperCamelCase	,							_UpperCamelCase:     Union[str, Any]							= self.notes_encoder(
        encoder_input_tokens=_lowercase		,  encoder_inputs_mask=_lowercase							)

    _UpperCamelCase	,							_UpperCamelCase:     Optional[Any]							= self.continuous_encoder(
        encoder_inputs=_lowercase		,  encoder_inputs_mask=_lowercase							)

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



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  Tuple		,  _lowercase					:  Tuple		,  _lowercase					:  List[str]							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= noise_time
    if not torch.is_tensor(_lowercase							):
     _UpperCamelCase:     Optional[Any]							= torch.tensor([timesteps]		,  dtype=torch.long		,  device=input_tokens.device							)
    elif torch.is_tensor(_lowercase							) and len(timesteps.shape							) == 0:
     _UpperCamelCase:     Union[str, Any]							= timesteps[None].to(input_tokens.device							)

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

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



   @torch.no_grad()
   def __call__(	self					:  List[Any]		,  _lowercase					:  List[List[int]]		,  _lowercase					:  Optional[torch.Generator] = None		,  _lowercase					:  int = 100		,  _lowercase					:  bool = True		,  _lowercase					:  str = "numpy"		,  _lowercase					:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None		,  _lowercase					:  int = 1		,  ):



    """simple docstring"""



    if (callback_steps is None) or (
        callback_steps is not None and (not isinstance(_lowercase		,  _lowercase							) or callback_steps <= 0)
    ):
     raise ValueError(
         f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
         f""" {type(_lowercase							)}."""							)

    _UpperCamelCase:     List[Any]							= np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]		,  dtype=np.floataa							)
    _UpperCamelCase:     Union[str, Any]							= np.zeros([1, 0, self.n_dims]		,  np.floataa							)
    _UpperCamelCase:     Optional[int]							= torch.ones((1, TARGET_FEATURE_LENGTH)		,  dtype=_lowercase		,  device=self.device							)

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

     _UpperCamelCase:     str							= self.scale_features(
         _lowercase		,  output_range=[-1.0, 1.0]		,  clip=_lowercase							)

     _UpperCamelCase:     int							= self.encode(
         input_tokens=torch.IntTensor([encoder_input_tokens]							).to(device=self.device							)		,  continuous_inputs=_lowercase		,  continuous_mask=_lowercase		,  )

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

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

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

      # Compute previous output: x_t -> x_t-1
      _UpperCamelCase:     str							= self.scheduler.step(_lowercase		,  _lowercase		,  _lowercase		,  generator=_lowercase							).prev_sample

     _UpperCamelCase:     List[Any]							= self.scale_to_features(_lowercase		,  input_range=[-1.0, 1.0]							)
     _UpperCamelCase:     Optional[int]							= mel[:1]
     _UpperCamelCase:     Union[str, Any]							= mel.cpu().float().numpy()

     _UpperCamelCase:     Optional[int]							= 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(_lowercase		,  _lowercase							)

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

    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:     str							= self.melgan(input_features=full_pred_mel.astype(np.floataa							)							)
    else:
     _UpperCamelCase:     List[Any]							= full_pred_mel

    if not return_dict:
     return (output,)

    return AudioPipelineOutput(audios=_lowercase							)
import warnings

from ..trainer import Trainer
from ..utils import logging


UpperCAmelCase_			    =							logging.get_logger(__name__)




class  __magic_name__  (		__a    ):





   """simple docstring"""




   def __init__(	self					:  List[Any]		,  _lowercase					:  int=None		,  **_lowercase					:  Optional[Any]							):



    """simple docstring"""



    warnings.warn(
        '''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
        '''instead.'''		,  _lowercase		,  )
    super().__init__(args=_lowercase		,  **_lowercase							)