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


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

__magic_name__								=256



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Tuple						=["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	)		->      Optional[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	)		->      Optional[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_	)		->      List[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_	)		->      Optional[int]:





       '''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_	)




def  __UpperCamelCase    (						A = 600851475143							):

     try:
          UpperCamelCase__										=      int(A							)
     except (TypeError, ValueError):
          raise TypeError('''Parameter n must be int or castable to int.'''							)
     if n <= 0:
          raise ValueError('''Parameter n must be greater than or equal to one.'''							)
     UpperCamelCase__										=      1
     UpperCamelCase__										=      2
     while i * i <= n:
          while n % i == 0:
               UpperCamelCase__										=      i
               n //= i
          i += 1
     if n > 1:
          UpperCamelCase__										=      n
     return int(A							)


if __name__ == "__main__":
     print(f"""{solution() = }""")



