




'''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_						:      Union[str, Any]       =						256

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   Union[str, Any]       =						["""melgan"""]






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,)							->      None:
          super().__init__()

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

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






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=(-1.0, 1.0) ,_SCREAMING_SNAKE_CASE=False   )							->      Optional[Any]:
          _snake_case							, _snake_case							=				output_range
          if clip:
               _snake_case							=				torch.clip(_SCREAMING_SNAKE_CASE ,self.min_value ,self.max_value   )
          # Scale to [0, 1].
          _snake_case							=				(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     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=(-1.0, 1.0) ,_SCREAMING_SNAKE_CASE=False   )							->      Optional[int]:
          _snake_case							, _snake_case							=				input_range
          _snake_case							=				torch.clip(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) if clip else outputs
          # Scale to [0, 1].
          _snake_case							=				(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     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Any:
          _snake_case							=				input_tokens > 0
          _snake_case							, _snake_case							=				self.notes_encoder(
              encoder_input_tokens=_SCREAMING_SNAKE_CASE ,encoder_inputs_mask=_SCREAMING_SNAKE_CASE   )

          _snake_case							, _snake_case							=				self.continuous_encoder(
              encoder_inputs=_SCREAMING_SNAKE_CASE ,encoder_inputs_mask=_SCREAMING_SNAKE_CASE   )

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






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      str:
          _snake_case							=				noise_time
          if not torch.is_tensor(_SCREAMING_SNAKE_CASE   ):
               _snake_case							=				torch.tensor([timesteps] ,dtype=torch.long ,device=input_tokens.device   )
          elif torch.is_tensor(_SCREAMING_SNAKE_CASE   ) and len(timesteps.shape   ) == 0:
               _snake_case							=				timesteps[None].to(input_tokens.device   )

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

          _snake_case							=				self.decoder(
              encodings_and_masks=_SCREAMING_SNAKE_CASE ,decoder_input_tokens=_SCREAMING_SNAKE_CASE ,decoder_noise_time=_SCREAMING_SNAKE_CASE   )
          return logits






     @torch.no_grad()
     def __call__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = 100 ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = "numpy" ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = 1 ,)							->      Union[AudioPipelineOutput, Tuple]:
          if (callback_steps is None) or (
              callback_steps is not None and (not isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_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(_SCREAMING_SNAKE_CASE   )}."""   )

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

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

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

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

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

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

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

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

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

               _snake_case							=				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(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

               logger.info("Generated segment" ,_SCREAMING_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":
               _snake_case							=				self.melgan(input_features=full_pred_mel.astype(np.floataa   )   )
          else:
               _snake_case							=				full_pred_mel

          if not return_dict:
               return (output,)

          return AudioPipelineOutput(audios=_SCREAMING_SNAKE_CASE   )








'''simple docstring'''





from __future__ import annotations

from typing import Any





def      __a     (       _UpperCamelCase:   list[Any]   )						->			None:

     """simple docstring"""




     create_state_space_tree(_UpperCamelCase ,						[] ,						0   )





def      __a     (       _UpperCamelCase:   list[Any] ,						_UpperCamelCase:   list[Any] ,						_UpperCamelCase:   int   )						->			None:

     """simple docstring"""




     if index == len(_UpperCamelCase   ):
          print(_UpperCamelCase   )
          return

     create_state_space_tree(_UpperCamelCase ,						_UpperCamelCase ,						index + 1   )
     current_subsequence.append(sequence[index]   )
     create_state_space_tree(_UpperCamelCase ,						_UpperCamelCase ,						index + 1   )
     current_subsequence.pop()


if __name__ == "__main__":
   UpperCamelCase_						:      list[Any]       =						[3, 1, 2, 4]
   generate_all_subsequences(seq)

   seq.clear()
   seq.extend(['''A''', '''B''', '''C'''])
   generate_all_subsequences(seq)


