'''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


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

lowercase__  :      Dict        =					256







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	['''melgan''']






  def __init__(      self			:       Any  ,							UpperCAmelCase__			:       SpectrogramNotesEncoder  ,							UpperCAmelCase__			:       SpectrogramContEncoder  ,							UpperCAmelCase__			:       TaFilmDecoder  ,							UpperCAmelCase__			:       DDPMScheduler  ,							UpperCAmelCase__			:       OnnxRuntimeModel if is_onnx_available() else Any  ,							)				->None:
        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=UpperCAmelCase__  ,							continuous_encoder=UpperCAmelCase__  ,							decoder=UpperCAmelCase__  ,							scheduler=UpperCAmelCase__  ,							melgan=UpperCAmelCase__  ,							)






  def  lowerCAmelCase__ (      self			:       List[Any]  ,							UpperCAmelCase__			:       Tuple  ,							UpperCAmelCase__			:       Optional[int]=(-1.0, 1.0)  ,							UpperCAmelCase__			:       Any=False  )				->int:
        UpperCAmelCase_      ,		UpperCAmelCase_            =  output_range
        if clip:
              UpperCAmelCase_            =  torch.clip(UpperCAmelCase__  ,							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  lowerCAmelCase__ (      self			:       Any  ,							UpperCAmelCase__			:       Optional[Any]  ,							UpperCAmelCase__			:       str=(-1.0, 1.0)  ,							UpperCAmelCase__			:       Optional[Any]=False  )				->Optional[Any]:
        UpperCAmelCase_      ,		UpperCAmelCase_            =  input_range
        UpperCAmelCase_            =  torch.clip(UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  ) 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  lowerCAmelCase__ (      self			:       Optional[Any]  ,							UpperCAmelCase__			:       str  ,							UpperCAmelCase__			:       Union[str, Any]  ,							UpperCAmelCase__			:       List[Any]  )				->str:
        UpperCAmelCase_            =  input_tokens > 0
        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.notes_encoder(
            encoder_input_tokens=UpperCAmelCase__  ,							encoder_inputs_mask=UpperCAmelCase__  )

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.continuous_encoder(
            encoder_inputs=UpperCAmelCase__  ,							encoder_inputs_mask=UpperCAmelCase__  )

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






  def  lowerCAmelCase__ (      self			:       Optional[Any]  ,							UpperCAmelCase__			:       Dict  ,							UpperCAmelCase__			:       str  ,							UpperCAmelCase__			:       List[Any]  )				->str:
        UpperCAmelCase_            =  noise_time
        if not torch.is_tensor(UpperCAmelCase__  ):
              UpperCAmelCase_            =  torch.tensor([timesteps]  ,							dtype=torch.long  ,							device=input_tokens.device  )
        elif torch.is_tensor(UpperCAmelCase__  ) 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=UpperCAmelCase__  ,							decoder_input_tokens=UpperCAmelCase__  ,							decoder_noise_time=UpperCAmelCase__  )
        return logits






  @torch.no_grad()
  def __call__(      self			:       Any  ,							UpperCAmelCase__			:       List[List[int]]  ,							UpperCAmelCase__			:       Optional[torch.Generator] = None  ,							UpperCAmelCase__			:       int = 100  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       str = "numpy"  ,							UpperCAmelCase__			:       Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,							UpperCAmelCase__			:       int = 1  ,							)				->Union[AudioPipelineOutput, Tuple]:
        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(UpperCAmelCase__  ,							UpperCAmelCase__  ) or callback_steps <= 0)
        ):
              raise ValueError(
                  f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
                  f""" {type(UpperCAmelCase__  )}."""  )

        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=UpperCAmelCase__  ,							device=self.device  )

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

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

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

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

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

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

              UpperCAmelCase_            =  self.scale_to_features(UpperCAmelCase__  ,							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(UpperCAmelCase__  ,							UpperCAmelCase__  )

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

        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=UpperCAmelCase__  )






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase__  :      Union[str, Any]        =					{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      Union[str, Any]        =					["MobileViTFeatureExtractor"]
      lowercase__  :      List[Any]        =					["MobileViTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      Dict        =					[
          "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "MobileViTForImageClassification",
          "MobileViTForSemanticSegmentation",
          "MobileViTModel",
          "MobileViTPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      Optional[int]        =					[
          "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFMobileViTForImageClassification",
          "TFMobileViTForSemanticSegmentation",
          "TFMobileViTModel",
          "TFMobileViTPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_mobilevit import MobileViTFeatureExtractor
            from .image_processing_mobilevit import MobileViTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilevit import (
                MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileViTForImageClassification,
                MobileViTForSemanticSegmentation,
                MobileViTModel,
                MobileViTPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mobilevit import (
                TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMobileViTForImageClassification,
                TFMobileViTForSemanticSegmentation,
                TFMobileViTModel,
                TFMobileViTPreTrainedModel,
            )


else:
      import sys

      lowercase__  :      int        =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





