





'''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 : Union[str, Any]											=			logging.get_logger(__name__)  # pylint: disable=invalid-name

__lowerCamelCase : Optional[Any]											=			256

class       UpperCAmelCase (						_lowercase  ):
  UpperCAmelCase					:	Tuple 			=		['''melgan''']




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

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

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




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

      lowercase   ,							lowercase         = self.continuous_encoder(
          encoder_inputs=A__							,			encoder_inputs_mask=A__				)

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




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

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

      lowercase         = self.decoder(
          encodings_and_masks=A__							,			decoder_input_tokens=A__							,			decoder_noise_time=A__				)
      return logits





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

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

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

          lowercase         = self.scale_features(
              A__							,			output_range=[-1.0, 1.0]							,			clip=A__				)

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

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

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

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

              # Compute previous output: x_t -> x_t-1
              lowercase         = self.scheduler.step(A__							,			A__							,			A__							,			generator=A__				).prev_sample

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

          lowercase         = 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(A__							,			A__				)

          logger.info("Generated segment"							,			A__				)

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

      if not return_dict:
          return (output,)

      return AudioPipelineOutput(audios=A__				)











'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase : Optional[Any]											=			logging.get_logger(__name__)

__lowerCamelCase : Optional[Any]											=			{
    "facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	Union[str, Any] 			=		'''vit_mae'''




		def __init__(self      :	Dict							,			A__      :	Tuple=7_6_8							,			A__      :	str=1_2							,			A__      :	Dict=1_2							,			A__      :	Optional[Any]=3_0_7_2							,			A__      :	Union[str, Any]="gelu"							,			A__      :	Optional[int]=0.0							,			A__      :	str=0.0							,			A__      :	Optional[Any]=0.0_2							,			A__      :	Optional[Any]=1e-12							,			A__      :	Tuple=2_2_4							,			A__      :	str=1_6							,			A__      :	Optional[int]=3							,			A__      :	List[Any]=True							,			A__      :	Dict=1_6							,			A__      :	Dict=5_1_2							,			A__      :	Dict=8							,			A__      :	Union[str, Any]=2_0_4_8							,			A__      :	str=0.7_5							,			A__      :	str=False							,			**A__      :	Any							,			)					-> Union[str, Any]:
						super().__init__(**A__				)

						lowercase         = hidden_size
						lowercase         = num_hidden_layers
						lowercase         = num_attention_heads
						lowercase         = intermediate_size
						lowercase         = hidden_act
						lowercase         = hidden_dropout_prob
						lowercase         = attention_probs_dropout_prob
						lowercase         = initializer_range
						lowercase         = layer_norm_eps
						lowercase         = image_size
						lowercase         = patch_size
						lowercase         = num_channels
						lowercase         = qkv_bias
						lowercase         = decoder_num_attention_heads
						lowercase         = decoder_hidden_size
						lowercase         = decoder_num_hidden_layers
						lowercase         = decoder_intermediate_size
						lowercase         = mask_ratio
						lowercase         = norm_pix_loss




