

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


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

__UpperCAmelCase    				=     2_5_6





class A__    ( __lowerCamelCase ):



 """simple docstring"""







 _lowercase     :					List[Any]      =						['''melgan''']

 def __init__(		self :	int     ,						A_ :	SpectrogramNotesEncoder     ,						A_ :	SpectrogramContEncoder     ,						A_ :	TaFilmDecoder     ,						A_ :	DDPMScheduler     ,						A_ :	OnnxRuntimeModel if is_onnx_available() else Any     ,						):






      '''simple docstring'''




      super().__init__()

      # From MELGAN
      _lowerCAmelCase   :					Optional[int]        =    math.log(1E-5       )  # Matches MelGAN training.
      _lowerCAmelCase   :					Tuple        =    4.0  # Largest value for most examples
      _lowerCAmelCase   :					str        =    1_2_8

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

 def    __magic_name__       (		self :	Dict     ,						A_ :	Any     ,						A_ :	str=(-1.0, 1.0)     ,						A_ :	Union[str, Any]=False       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    output_range
      if clip:
           _lowerCAmelCase   :					Any        =    torch.clip(UpperCAmelCase_     ,						self.min_value     ,						self.max_value       )
      # Scale to [0, 1].
      _lowerCAmelCase   :					Dict        =    (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    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	Dict     ,						A_ :	List[Any]=(-1.0, 1.0)     ,						A_ :	Dict=False       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    input_range
      _lowerCAmelCase   :					Optional[Any]        =    torch.clip(UpperCAmelCase_     ,						UpperCAmelCase_     ,						UpperCAmelCase_       ) if clip else outputs
      # Scale to [0, 1].
      _lowerCAmelCase   :					Any        =    (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    __magic_name__       (		self :	Optional[int]     ,						A_ :	List[str]     ,						A_ :	str     ,						A_ :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    input_tokens > 0
      _lowerCAmelCase   :					Dict        =    self.notes_encoder(
          encoder_input_tokens=UpperCAmelCase_     ,						encoder_inputs_mask=UpperCAmelCase_       )

      _lowerCAmelCase   :					Optional[Any]        =    self.continuous_encoder(
          encoder_inputs=UpperCAmelCase_     ,						encoder_inputs_mask=UpperCAmelCase_       )

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

 def    __magic_name__       (		self :	List[str]     ,						A_ :	Tuple     ,						A_ :	int     ,						A_ :	int       ):






      '''simple docstring'''




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

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

      _lowerCAmelCase   :					Any        =    self.decoder(
          encodings_and_masks=UpperCAmelCase_     ,						decoder_input_tokens=UpperCAmelCase_     ,						decoder_noise_time=UpperCAmelCase_       )
      return logits


 @torch.no_grad()
 def __call__(		self :	List[Any]     ,						A_ :	List[List[int]]     ,						A_ :	Optional[torch.Generator] = None     ,						A_ :	int = 1_0_0     ,						A_ :	bool = True     ,						A_ :	str = "numpy"     ,						A_ :	Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,						A_ :	int = 1     ,						):






      '''simple docstring'''




      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_       )}.'''       )

      _lowerCAmelCase   :					Dict        =    np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]     ,						dtype=np.floataa       )
      _lowerCAmelCase   :					List[str]        =    np.zeros([1, 0, self.n_dims]     ,						np.floataa       )
      _lowerCAmelCase   :					List[str]        =    torch.ones((1, TARGET_FEATURE_LENGTH)     ,						dtype=UpperCAmelCase_     ,						device=self.device       )

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

           _lowerCAmelCase   :					Tuple        =    self.scale_features(
               UpperCAmelCase_     ,						output_range=[-1.0, 1.0]     ,						clip=UpperCAmelCase_       )

           _lowerCAmelCase   :					Tuple        =    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
           _lowerCAmelCase   :					List[Any]        =    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       )       ):
                _lowerCAmelCase   :					Any        =    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
                _lowerCAmelCase   :					Optional[int]        =    self.scheduler.step(UpperCAmelCase_     ,						UpperCAmelCase_     ,						UpperCAmelCase_     ,						generator=UpperCAmelCase_       ).prev_sample

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

           _lowerCAmelCase   :					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(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":
           _lowerCAmelCase   :					Optional[Any]        =    self.melgan(input_features=full_pred_mel.astype(np.floataa       )       )
      else:
           _lowerCAmelCase   :					Optional[int]        =    full_pred_mel

      if not return_dict:
           return (output,)

      return AudioPipelineOutput(audios=UpperCAmelCase_       )





from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np





class A__    ( A ):



 """simple docstring"""







 def    __magic_name__       (		self :	List[Any]     ,						A_ :	float       ):






      '''simple docstring'''




      return 0.0







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      tuple[int | float, int | float]:

     """simple docstring"""







     _lowerCAmelCase   :					Union[str, Any]        =    min([-20, np.min(fft_results[1 : samplerate // 2 - 1]				)]				)
     _lowerCAmelCase   :					Optional[Any]        =    max([20, np.max(fft_results[1 : samplerate // 2 - 1]				)]				)
     return lowest, highest







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      None:

     """simple docstring"""







     _lowerCAmelCase   :					Any        =    512
     _lowerCAmelCase   :					Tuple        =    [1] + [0] * (size - 1)
     _lowerCAmelCase   :					Optional[Any]        =    [filter_type.process(SCREAMING_SNAKE_CASE				) for item in inputs]

     _lowerCAmelCase   :					Dict        =    [0] * (samplerate - size)  # zero-padding
     outputs += filler
     _lowerCAmelCase   :					Dict        =    np.abs(np.fft.fft(SCREAMING_SNAKE_CASE				)				)
     _lowerCAmelCase   :					str        =    20 * np.logaa(SCREAMING_SNAKE_CASE				)

     # Frequencies on log scale from 24 to nyquist frequency
     plt.xlim(24		,							samplerate / 2 - 1				)
     plt.xlabel("Frequency (Hz)"				)
     plt.xscale("log"				)

     # Display within reasonable bounds
     _lowerCAmelCase   :					Tuple        =    get_bounds(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)
     plt.ylim(max([-80, bounds[0]]				)		,							min([80, bounds[1]]				)				)
     plt.ylabel("Gain (dB)"				)

     plt.plot(SCREAMING_SNAKE_CASE				)
     plt.show()







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      None:

     """simple docstring"""







     _lowerCAmelCase   :					Tuple        =    512
     _lowerCAmelCase   :					Tuple        =    [1] + [0] * (size - 1)
     _lowerCAmelCase   :					Dict        =    [filter_type.process(SCREAMING_SNAKE_CASE				) for item in inputs]

     _lowerCAmelCase   :					Tuple        =    [0] * (samplerate - size)  # zero-padding
     outputs += filler
     _lowerCAmelCase   :					Tuple        =    np.angle(np.fft.fft(SCREAMING_SNAKE_CASE				)				)

     # Frequencies on log scale from 24 to nyquist frequency
     plt.xlim(24		,							samplerate / 2 - 1				)
     plt.xlabel("Frequency (Hz)"				)
     plt.xscale("log"				)

     plt.ylim(-2 * pi		,							2 * pi				)
     plt.ylabel("Phase shift (Radians)"				)
     plt.plot(np.unwrap(SCREAMING_SNAKE_CASE		,							-2 * pi				)				)
     plt.show()






