




"""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


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

lowercase__				:      Union[str, Any]	   = 256




class       a__	( UpperCamelCase__				):
     a		:      Any		 =     ["""melgan"""]







     def __init__(     self  ,		A  ,		A  ,		A  ,		A  ,		A  ,		)  ->   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=A  ,		continuous_encoder=A  ,		decoder=A  ,		scheduler=A  ,		melgan=A  ,		)







     def       lowerCAmelCase_    (     self  ,		A  ,		A=(-1.0, 1.0)  ,		A=False			)  ->   Optional[Any]:



          '''simple docstring'''



          a  ,				a     =				output_range
          if clip:
               a     =				torch.clip(A  ,		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  ,		A  ,		A=(-1.0, 1.0)  ,		A=False			)  ->   Optional[Any]:



          '''simple docstring'''



          a  ,				a     =				input_range
          a     =				torch.clip(A  ,		A  ,		A			) 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  ,		A  ,		A  ,		A			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				input_tokens > 0
          a  ,				a     =				self.notes_encoder(
              encoder_input_tokens=A  ,		encoder_inputs_mask=A			)

          a  ,				a     =				self.continuous_encoder(
              encoder_inputs=A  ,		encoder_inputs_mask=A			)

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







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				noise_time
          if not torch.is_tensor(A			):
               a     =				torch.tensor([timesteps]  ,		dtype=torch.long  ,		device=input_tokens.device			)
          elif torch.is_tensor(A			) 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=A  ,		decoder_input_tokens=A  ,		decoder_noise_time=A			)
          return logits







     @torch.no_grad()
     def __call__(     self  ,		A  ,		A = None  ,		A = 100  ,		A = True  ,		A = "numpy"  ,		A = None  ,		A = 1  ,		)  ->   Union[AudioPipelineOutput, Tuple]:



          '''simple docstring'''



          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			)}.'''			)

          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=A  ,		device=self.device			)

          for i, encoder_input_tokens in enumerate(A			):
               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=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.
                    a     =				ones

               a     =				self.scale_features(
                   A  ,		output_range=[-1.0, 1.0]  ,		clip=A			)

               a     =				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
               a     =				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			)			):
                    a     =				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
                    a     =				self.scheduler.step(A  ,		A  ,		A  ,		generator=A			).prev_sample

               a     =				self.scale_to_features(A  ,		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(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":
               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=A			)







from __future__ import annotations

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					list[int]:
     a     =				2
     a     =				[]
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.append(__UpperCamelCase)
     if n > 1:
          factors.append(__UpperCamelCase)
     return factors


if __name__ == "__main__":
   import doctest

   doctest.testmod()

