





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


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

SCREAMING_SNAKE_CASE__			      =						2_5_6







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						str     		=				["""melgan"""]





   def __init__( self					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			)				->  None:
      """simple docstring"""





      super().__init__()

      # From MELGAN
      a__	:						Dict      		=  math.log(1E-5      )  # Matches MelGAN training.
      a__	:						Any      		=  4.0  # Largest value for most examples
      a__	:						int      		=  128

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





   def 	_snake_case ( self					,			snake_case					,			snake_case=(-1.0, 1.0)					,			snake_case=False      )				->  int:
      """simple docstring"""





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





      a__	:						str      		=  input_range
      a__	:						Tuple      		=  torch.clip(snake_case					,			snake_case					,			snake_case      ) if clip else outputs
      # Scale to [0, 1].
      a__	:						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 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case      )				->  str:
      """simple docstring"""





      a__	:						str      		=  input_tokens > 0
      a__	:						Dict      		=  self.notes_encoder(
          encoder_input_tokens=snake_case					,			encoder_inputs_mask=snake_case      )

      a__	:						int      		=  self.continuous_encoder(
          encoder_inputs=snake_case					,			encoder_inputs_mask=snake_case      )

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





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case      )				->  Union[str, Any]:
      """simple docstring"""





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

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

      a__	:						List[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					,			snake_case					,			snake_case = None					,			snake_case = 100					,			snake_case = True					,			snake_case = "numpy"					,			snake_case = None					,			snake_case = 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__	:						Tuple      		=  np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]					,			dtype=np.floataa      )
      a__	:						int      		=  np.zeros([1, 0, self.n_dims]					,			np.floataa      )
      a__	:						List[Any]      		=  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__	:						Dict      		=  torch.from_numpy(pred_mel[:1].copy()      ).to(
                device=self.device					,			dtype=self.decoder.dtype      )
            # The first chunk has no previous context.
            a__	:						int      		=  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__	:						str      		=  ones

         a__	:						Tuple      		=  self.scale_features(
             snake_case					,			output_range=[-1.0, 1.0]					,			clip=snake_case      )

         a__	:						Dict      		=  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__	:						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      )      ):
            a__	:						str      		=  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__	:						Any      		=  self.scheduler.step(snake_case					,			snake_case					,			snake_case					,			generator=snake_case      ).prev_sample

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

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

      if not return_dict:
         return (output,)

      return AudioPipelineOutput(audios=snake_case      )








from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE__       :			List[str]			      =						{"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						[
      """VAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """VanForImageClassification""",
      """VanModel""",
      """VanPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_van import (
        VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
        VanForImageClassification,
        VanModel,
        VanPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE__       :			List[str]			      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure)

