




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			)







def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					int:
     return 1 if digit in (0, 1) else (digit * factorial(digit - 1))

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					bool:
     a     =				0
     a     =				number
     while duplicate > 0:
          a  ,				a     =				divmod(__UpperCamelCase		, 10)
          fact_sum += factorial(__UpperCamelCase)
     return fact_sum == number


if __name__ == "__main__":
   print("Program to check whether a number is a Krisnamurthy Number or not.")
   lowercase__				:      str	   = int(input("Enter number: ").strip())
   print(
       F'{number} is {"" if krishnamurthy(number) else "not "}a Krishnamurthy Number.'
   )

