"""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


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

__A			   = 2_5_6


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Dict						    = ["melgan"]






    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			):
       super().__init__()

       # From MELGAN
       lowercase__:   Any					=      math.log(1e-5							)  # Matches MelGAN training.
       lowercase__:   Optional[Any]					=      4.0  # Largest value for most examples
       lowercase__:   int					=      128

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






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

       lowercase__, lowercase__:   Any					=      self.continuous_encoder(
           encoder_inputs=_UpperCAmelCase    ,			encoder_inputs_mask=_UpperCAmelCase							)

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






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   List[Any]					=      noise_time
       if not torch.is_tensor(_UpperCAmelCase							):
          lowercase__:   Any					=      torch.tensor([timesteps]    ,			dtype=torch.long    ,			device=input_tokens.device							)
       elif torch.is_tensor(_UpperCAmelCase							) and len(timesteps.shape							) == 0:
          lowercase__:   Dict					=      timesteps[None].to(input_tokens.device							)

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

       lowercase__:   Dict					=      self.decoder(
           encodings_and_masks=_UpperCAmelCase    ,			decoder_input_tokens=_UpperCAmelCase    ,			decoder_noise_time=_UpperCAmelCase							)
       return logits






    @torch.no_grad()
    def __call__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = 100    ,			_UpperCAmelCase = True    ,			_UpperCAmelCase = "numpy"    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = 1    ,			):
       if (callback_steps is None) or (
           callback_steps is not None and (not isinstance(_UpperCAmelCase    ,			_UpperCAmelCase							) or callback_steps <= 0)
       ):
          raise ValueError(
              F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
              F""" {type(_UpperCAmelCase							)}."""							)

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

       for i, encoder_input_tokens in enumerate(_UpperCAmelCase							):
          if i == 0:
             lowercase__:   int					=      torch.from_numpy(pred_mel[:1].copy()							).to(
                 device=self.device    ,			dtype=self.decoder.dtype							)
             # The first chunk has no previous context.
             lowercase__:   Dict					=      torch.zeros((1, TARGET_FEATURE_LENGTH)    ,			dtype=_UpperCAmelCase    ,			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__:   Union[str, Any]					=      ones

          lowercase__:   Union[str, Any]					=      self.scale_features(
              _UpperCAmelCase    ,			output_range=[-1.0, 1.0]    ,			clip=_UpperCAmelCase							)

          lowercase__:   List[Any]					=      self.encode(
              input_tokens=torch.IntTensor([encoder_input_tokens]							).to(device=self.device							)    ,			continuous_inputs=_UpperCAmelCase    ,			continuous_mask=_UpperCAmelCase    ,			)

          # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
          lowercase__:   Optional[Any]					=      randn_tensor(
              shape=encoder_continuous_inputs.shape    ,			generator=_UpperCAmelCase    ,			device=self.device    ,			dtype=self.decoder.dtype    ,			)

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

          # Denoising diffusion loop
          for j, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
             lowercase__:   Optional[int]					=      self.decode(
                 encodings_and_masks=_UpperCAmelCase    ,			input_tokens=_UpperCAmelCase    ,			noise_time=t / self.scheduler.config.num_train_timesteps    ,			)

             # Compute previous output: x_t -> x_t-1
             lowercase__:   Union[str, Any]					=      self.scheduler.step(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			generator=_UpperCAmelCase							).prev_sample

          lowercase__:   Optional[int]					=      self.scale_to_features(_UpperCAmelCase    ,			input_range=[-1.0, 1.0]							)
          lowercase__:   Tuple					=      mel[:1]
          lowercase__:   List[str]					=      mel.cpu().float().numpy()

          lowercase__:   Any					=      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(_UpperCAmelCase    ,			_UpperCAmelCase							)

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

       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__:   str					=      self.melgan(input_features=full_pred_mel.astype(np.floataa							)							)
       else:
          lowercase__:   int					=      full_pred_mel

       if not return_dict:
          return (output,)

       return AudioPipelineOutput(audios=_UpperCAmelCase							)

"""simple docstring"""






def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  int:
   if n == 1 or not isinstance(__UpperCAmelCase       ,    __UpperCAmelCase ):
      return 0
   elif n == 2:
      return 1
   else:
      lowercase__:   List[Any]					=      [0, 1]
      for i in range(2       ,    n + 1 ):
         sequence.append(sequence[i - 1] + sequence[i - 2] )

      return sequence[n]



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  int:
   lowercase__:   Union[str, Any]					=      0
   lowercase__:   List[Any]					=      2

   while digits < n:
      index += 1
      lowercase__:   Dict					=      len(str(fibonacci(__UpperCAmelCase ) ) )

   return index



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase = 1_0_0_0 )  ->  int:
   return fibonacci_digits_index(__UpperCAmelCase )


if __name__ == "__main__":
       print(solution(int(str(input()).strip())))
