





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__:      Union[str, Any]									=		logging.get_logger(__name__)  # pylint: disable=invalid-name

a__:      List[str]									=		256




class     SCREAMING_SNAKE_CASE__ (						__A      ):
 __SCREAMING_SNAKE_CASE							       =	["melgan"]



 def __init__(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,):
        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=lowercase_,continuous_encoder=lowercase_,decoder=lowercase_,scheduler=lowercase_,melgan=lowercase_,)



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase=(-1.0, 1.0),__lowerCamelCase=False ):
        A__											=						output_range
        if clip:
               A__											=						torch.clip(lowercase_,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 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase=(-1.0, 1.0),__lowerCamelCase=False ):
        A__											=						input_range
        A__											=						torch.clip(lowercase_,lowercase_,lowercase_ ) 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 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        A__											=						input_tokens > 0
        A__											=						self.notes_encoder(
            encoder_input_tokens=lowercase_,encoder_inputs_mask=lowercase_ )

        A__											=						self.continuous_encoder(
            encoder_inputs=lowercase_,encoder_inputs_mask=lowercase_ )

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



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        A__											=						noise_time
        if not torch.is_tensor(lowercase_ ):
               A__											=						torch.tensor([timesteps],dtype=torch.long,device=input_tokens.device )
        elif torch.is_tensor(lowercase_ ) 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=lowercase_,decoder_input_tokens=lowercase_,decoder_noise_time=lowercase_ )
        return logits




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

        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=lowercase_,device=self.device )

        for i, encoder_input_tokens in enumerate(lowercase_ ):
               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=lowercase_,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(
                   lowercase_,output_range=[-1.0, 1.0],clip=lowercase_ )

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

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

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

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

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

               A__											=						self.scale_to_features(lowercase_,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(lowercase_,lowercase_ )

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

        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=lowercase_ )






def 			lowercase_				(			_lowerCamelCase							:       int):
    lowercase__ :   Dict    					=				n ** (1 / 3)
    return (val * val * val) == n


if __name__ == "__main__":
      print(perfect_cube(27))
      print(perfect_cube(4))



