





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


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

__lowerCamelCase			:	Optional[Any]					    =	256



class    A__  (			__snake_case     ):
      _UpperCAmelCase      :Dict		       =  ['melgan']





      def __init__(					self			,				A_			,				A_			,				A_			,				A_			,				A_			,				):
       '''simple docstring'''




       super().__init__()

       # From MELGAN
       UpperCamelCase      :   Union[str, Any]			      =  math.log(1e-5  )  # Matches MelGAN training.
       UpperCamelCase      :   List[str]			      =  4.0  # Largest value for most examples
       UpperCamelCase      :   Dict			      =  128

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





      def 			__UpperCamelCase(					self			,				A_			,				A_=(-1.0, 1.0)			,				A_=False  ):
       '''simple docstring'''




       UpperCamelCase      ,       UpperCamelCase      :   Optional[Any]			      =  output_range
       if clip:
        UpperCamelCase      :   Tuple			      =  torch.clip(A_			,				self.min_value			,				self.max_value  )
       # Scale to [0, 1].
       UpperCamelCase      :   Optional[Any]			      =  (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			,				A_			,				A_=(-1.0, 1.0)			,				A_=False  ):
       '''simple docstring'''




       UpperCamelCase      ,       UpperCamelCase      :   Tuple			      =  input_range
       UpperCamelCase      :   Optional[Any]			      =  torch.clip(A_			,				A_			,				A_  ) if clip else outputs
       # Scale to [0, 1].
       UpperCamelCase      :   List[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 			__UpperCamelCase(					self			,				A_			,				A_			,				A_  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[int]			      =  input_tokens > 0
       UpperCamelCase      ,       UpperCamelCase      :   Tuple			      =  self.notes_encoder(
           encoder_input_tokens=A_			,				encoder_inputs_mask=A_  )

       UpperCamelCase      ,       UpperCamelCase      :   List[Any]			      =  self.continuous_encoder(
           encoder_inputs=A_			,				encoder_inputs_mask=A_  )

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





      def 			__UpperCamelCase(					self			,				A_			,				A_			,				A_  ):
       '''simple docstring'''




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

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

       UpperCamelCase      :   int			      =  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			,				):
       '''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_  )}."""  )

       UpperCamelCase      :   Union[str, Any]			      =  np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]			,				dtype=np.floataa  )
       UpperCamelCase      :   Tuple			      =  np.zeros([1, 0, self.n_dims]			,				np.floataa  )
       UpperCamelCase      :   Any			      =  torch.ones((1, TARGET_FEATURE_LENGTH)			,				dtype=A_			,				device=self.device  )

       for i, encoder_input_tokens in enumerate(A_  ):
        if i == 0:
         UpperCamelCase      :   List[Any]			      =  torch.from_numpy(pred_mel[:1].copy()  ).to(
             device=self.device			,				dtype=self.decoder.dtype  )
         # The first chunk has no previous context.
         UpperCamelCase      :   str			      =  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.
         UpperCamelCase      :   Union[str, Any]			      =  ones

        UpperCamelCase      :   int			      =  self.scale_features(
            A_			,				output_range=[-1.0, 1.0]			,				clip=A_  )

        UpperCamelCase      :   List[Any]			      =  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
        UpperCamelCase      :   str			      =  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  )  ):
         UpperCamelCase      :   Optional[Any]			      =  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
         UpperCamelCase      :   Union[str, Any]			      =  self.scheduler.step(A_			,				A_			,				A_			,				generator=A_  ).prev_sample

        UpperCamelCase      :   Dict			      =  self.scale_to_features(A_			,				input_range=[-1.0, 1.0]  )
        UpperCamelCase      :   int			      =  mel[:1]
        UpperCamelCase      :   List[str]			      =  mel.cpu().float().numpy()

        UpperCamelCase      :   List[str]			      =  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":
        UpperCamelCase      :   str			      =  self.melgan(input_features=full_pred_mel.astype(np.floataa  )  )
       else:
        UpperCamelCase      :   int			      =  full_pred_mel

       if not return_dict:
        return (output,)

       return AudioPipelineOutput(audios=A_  )







from typing import TYPE_CHECKING

from ...utils import _LazyModule


__lowerCamelCase			:	str					    =	{"""tokenization_byt5""": ["""ByT5Tokenizer"""]}


if TYPE_CHECKING:
    from .tokenization_byta import ByTaTokenizer
else:
    import sys

    __lowerCamelCase			:	List[str]					    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
