






'''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


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

SCREAMING_SNAKE_CASE__											=     2_5_6






class        a_		(    lowerCamelCase       ):
  lowercase  	=   ["""melgan"""]

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



        """simple docstring"""






        super().__init__()

        # From MELGAN
        UpperCamelCase       						=      math.log(1e-5   )  # Matches MelGAN training.
        UpperCamelCase       						=      4.0  # Largest value for most examples
        UpperCamelCase       						=      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 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=(-1.0, 1.0)    ,	_SCREAMING_SNAKE_CASE=False   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      output_range
        if clip:
              UpperCamelCase       						=      torch.clip(_SCREAMING_SNAKE_CASE    ,	self.min_value    ,	self.max_value   )
        # Scale to [0, 1].
        UpperCamelCase       						=      (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 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=(-1.0, 1.0)    ,	_SCREAMING_SNAKE_CASE=False   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      input_range
        UpperCamelCase       						=      torch.clip(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ) if clip else outputs
        # Scale to [0, 1].
        UpperCamelCase       						=      (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 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      input_tokens > 0
        UpperCamelCase						,UpperCamelCase       						=      self.notes_encoder(
            encoder_input_tokens=_SCREAMING_SNAKE_CASE    ,	encoder_inputs_mask=_SCREAMING_SNAKE_CASE   )

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

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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   str:



        """simple docstring"""






        UpperCamelCase       						=      noise_time
        if not torch.is_tensor(_SCREAMING_SNAKE_CASE   ):
              UpperCamelCase       						=      torch.tensor([timesteps]    ,	dtype=torch.long    ,	device=input_tokens.device   )
        elif torch.is_tensor(_SCREAMING_SNAKE_CASE   ) and len(timesteps.shape   ) == 0:
              UpperCamelCase       						=      timesteps[None].to(input_tokens.device   )

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

        UpperCamelCase       						=      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]:



        """simple docstring"""






        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   )}."   )

        UpperCamelCase       						=      np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]    ,	dtype=np.floataa   )
        UpperCamelCase       						=      np.zeros([1, 0, self.n_dims]    ,	np.floataa   )
        UpperCamelCase       						=      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:
                    UpperCamelCase       						=      torch.from_numpy(pred_mel[:1].copy()   ).to(
                        device=self.device    ,	dtype=self.decoder.dtype   )
                    # The first chunk has no previous context.
                    UpperCamelCase       						=      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.
                    UpperCamelCase       						=      ones

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

              UpperCamelCase       						=      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
              UpperCamelCase       						=      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   )   ):
                    UpperCamelCase       						=      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
                    UpperCamelCase       						=      self.scheduler.step(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	generator=_SCREAMING_SNAKE_CASE   ).prev_sample

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

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

        if not return_dict:
              return (output,)

        return AudioPipelineOutput(audios=_SCREAMING_SNAKE_CASE   )








'''simple docstring'''

from math import factorial







def 					lowercase__				(   __UpperCamelCase = 20					)->				int:
      UpperCamelCase       						=      2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
      # 2, 3,...
      UpperCamelCase       						=      n // 2

      return int(factorial(__UpperCamelCase					) / (factorial(__UpperCamelCase					) * factorial(n - k					))					)


if __name__ == "__main__":
     import sys

     if len(sys.argv) == 1:
          print(solution(2_0))
     else:
          try:
               SCREAMING_SNAKE_CASE__											=     int(sys.argv[1])
               print(solution(n))
          except ValueError:
               print('Invalid entry - please enter a number.')
