

from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


__UpperCAmelCase								=			logging.get_logger(__name__)



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      Union[str, Any]										=["audio_values", "audio_mask"]







  def __init__(      self			:   Optional[int]	,							lowerCAmelCase			:   Optional[int]=20_48	,							lowerCAmelCase			:   Union[str, Any]=1	,							lowerCAmelCase			:   Tuple=[16, 16]	,							lowerCAmelCase			:   Any=1_28	,							lowerCAmelCase			:   Dict=4_41_00	,							lowerCAmelCase			:   int=86	,							lowerCAmelCase			:   str=20_48	,							lowerCAmelCase			:   List[str]=0.0	,							**lowerCAmelCase			:   int	,							)							->     Optional[int]:






     """simple docstring"""







     super().__init__(
         feature_size=lowerCAmelCase	,							sampling_rate=lowerCAmelCase	,							padding_value=lowerCAmelCase	,							**lowerCAmelCase	,							)

     __lowerCAmelCase  :      Dict    =						spectrogram_length
     __lowerCAmelCase  :      str    =						num_channels
     __lowerCAmelCase  :      Union[str, Any]    =						patch_size
     __lowerCAmelCase  :      int    =						feature_size // self.patch_size[1]
     __lowerCAmelCase  :      str    =						n_fft
     __lowerCAmelCase  :      Optional[Any]    =						sampling_rate // hop_length_to_sampling_rate
     __lowerCAmelCase  :      Tuple    =						sampling_rate
     __lowerCAmelCase  :      Any    =						padding_value
     __lowerCAmelCase  :      str    =						mel_filter_bank(
         num_frequency_bins=1 + n_fft // 2	,							num_mel_filters=lowerCAmelCase	,							min_frequency=0.0	,							max_frequency=2_2050.0	,							sampling_rate=lowerCAmelCase	,							norm="""slaney"""	,							mel_scale="""slaney"""	,							).T







  def 	SCREAMING_SNAKE_CASE   (      self			:   int	,							lowerCAmelCase			:   np.array   )							->     np.ndarray:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						spectrogram(
         lowerCAmelCase	,							window_function(self.n_fft	,							"""hann"""   )	,							frame_length=self.n_fft	,							hop_length=self.hop_length	,							power=2.0	,							mel_filters=self.mel_filters.T	,							log_mel="""dB"""	,							db_range=80.0	,							)
     __lowerCAmelCase  :      Union[str, Any]    =						log_spec[:, :-1]
     __lowerCAmelCase  :      Optional[int]    =						log_spec - 20.0
     __lowerCAmelCase  :      List[str]    =						np.clip(log_spec / 40.0	,							-2.0	,							0.0   ) + 1.0
     return log_spec







  def __call__(      self			:   Optional[Any]	,							lowerCAmelCase			:   Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,							lowerCAmelCase			:   Optional[Union[str, TensorType]] = None	,							lowerCAmelCase			:   Optional[bool] = True	,							lowerCAmelCase			:   Optional[int] = None	,							lowerCAmelCase			:   bool = False	,							lowerCAmelCase			:   bool = False	,							**lowerCAmelCase			:   Union[str, Any]	,							)							->     BatchFeature:






     """simple docstring"""







     if sampling_rate is not None:
        if sampling_rate != self.sampling_rate:
           raise ValueError(
               """This feature extractor is set to support sampling rate"""
               f''' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'''
               f''' with {self.sampling_rate} and not {sampling_rate}.'''   )
     else:
        logger.warning(
            """It is strongly recommended to pass the `sampling_rate` argument to this function. """
            """Failing to do so can result in silent errors that might be hard to debug."""   )

     __lowerCAmelCase  :      List[str]    =						isinstance(lowerCAmelCase	,							np.ndarray   ) and len(raw_speech.shape   ) > 1
     if is_batched_numpy and len(raw_speech.shape   ) > 2:
        raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''   )
     __lowerCAmelCase  :      Any    =						is_batched_numpy or (
         isinstance(lowerCAmelCase	,							(list, tuple)   ) and (isinstance(raw_speech[0]	,							(np.ndarray, tuple, list)   ))
     )
     if is_batched:
        __lowerCAmelCase  :      int    =						[np.asarray([speech]	,							dtype=np.floataa   ).T for speech in raw_speech]
     elif not is_batched and not isinstance(lowerCAmelCase	,							np.ndarray   ):
        __lowerCAmelCase  :      Dict    =						np.asarray(lowerCAmelCase	,							dtype=np.floataa   )
     elif isinstance(lowerCAmelCase	,							np.ndarray   ) and raw_speech.dtype is np.dtype(np.floataa   ):
        __lowerCAmelCase  :      Optional[Any]    =						raw_speech.astype(np.floataa   )
     # always return batch
     if not is_batched:
        __lowerCAmelCase  :      List[Any]    =						[np.asarray([raw_speech]   ).T]

     # Convert audio signals to log mel spectrograms, truncate by time axis
     __lowerCAmelCase  :      int    =						[
         self._np_extract_fbank_features(waveform.squeeze()   ).T[: self.spectrogram_length] for waveform in raw_speech
     ]
     if isinstance(audio_features[0]	,							lowerCAmelCase   ):
        __lowerCAmelCase  :      Dict    =						[np.asarray(lowerCAmelCase	,							dtype=np.floataa   ) for feature in audio_features]

     # Create audio attention mask
     __lowerCAmelCase  :      int    =						max(
         [ceil(feature.shape[0] / self.patch_size[0]   ) * self.freq_len for feature in audio_features]   )  # The maximum number of audio patches in a batch
     if return_attention_mask:
        __lowerCAmelCase  :      Union[str, Any]    =						[
            (ceil(feature.shape[0] / self.patch_size[0]   ) * self.freq_len) * [1]
            + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]   ) * self.freq_len) * [0]
            for feature in audio_features
        ]
        __lowerCAmelCase  :      List[str]    =						np.array(lowerCAmelCase   ).astype(np.floataa   )

     # convert into correct format for padding
     __lowerCAmelCase  :      List[Any]    =						max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
     __lowerCAmelCase  :      Dict    =						np.ones([len(lowerCAmelCase   ), 1, max_time_len, self.feature_size]   ).astype(np.floataa   )
     __lowerCAmelCase  :      Optional[int]    =						padded_audio_features * self.padding_value
     for i in range(len(lowerCAmelCase   )   ):
        __lowerCAmelCase  :      Tuple    =						audio_features[i]
        __lowerCAmelCase  :      Optional[int]    =						feature

     # return as BatchFeature
     if return_attention_mask:
        __lowerCAmelCase  :      Optional[int]    =						{"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
     else:
        __lowerCAmelCase  :      Dict    =						{"""audio_values""": padded_audio_features}

     __lowerCAmelCase  :      Tuple    =						BatchFeature(data=lowerCAmelCase	,							tensor_type=lowerCAmelCase   )
     return encoded_inputs





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      Dict										=(EulerDiscreteScheduler,)
  lowerCamelCase   :      Dict										=10







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							**lowerCAmelCase			:   Union[str, Any]   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						{
         """num_train_timesteps""": 11_00,
         """beta_start""": 0.0001,
         """beta_end""": 0.02,
         """beta_schedule""": """linear""",
     }

     config.update(**lowerCAmelCase   )
     return config







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     List[str]:






     """simple docstring"""







     for timesteps in [10, 50, 1_00, 10_00]:
        self.check_over_configs(num_train_timesteps=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Tuple:






     """simple docstring"""







     for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,							[0.0002, 0.002, 0.02]   ):
        self.check_over_configs(beta_start=lowerCAmelCase	,							beta_end=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     str:






     """simple docstring"""







     for schedule in ["linear", "scaled_linear"]:
        self.check_over_configs(beta_schedule=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     List[Any]:






     """simple docstring"""







     for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      int    =						self.get_scheduler_config()
     __lowerCAmelCase  :      Any    =						scheduler_class(**lowerCAmelCase   )

     scheduler.set_timesteps(self.num_inference_steps   )

     __lowerCAmelCase  :      str    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      List[Any]    =						self.dummy_model()
     __lowerCAmelCase  :      Dict    =						self.dummy_sample_deter * scheduler.init_noise_sigma
     __lowerCAmelCase  :      int    =						sample.to(lowerCAmelCase   )

     for i, t in enumerate(scheduler.timesteps   ):
        __lowerCAmelCase  :      Tuple    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Dict    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      str    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      Optional[int]    =						output.prev_sample

     __lowerCAmelCase  :      str    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      Optional[int]    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 10.0807   ) < 1e-2
     assert abs(result_mean.item() - 0.0131   ) < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      List[Any]    =						self.get_scheduler_config(prediction_type="""v_prediction"""   )
     __lowerCAmelCase  :      List[str]    =						scheduler_class(**lowerCAmelCase   )

     scheduler.set_timesteps(self.num_inference_steps   )

     __lowerCAmelCase  :      Any    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      int    =						self.dummy_model()
     __lowerCAmelCase  :      List[str]    =						self.dummy_sample_deter * scheduler.init_noise_sigma
     __lowerCAmelCase  :      List[Any]    =						sample.to(lowerCAmelCase   )

     for i, t in enumerate(scheduler.timesteps   ):
        __lowerCAmelCase  :      Any    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Any    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Dict    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      Dict    =						output.prev_sample

     __lowerCAmelCase  :      List[str]    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      Optional[int]    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 0.0002   ) < 1e-2
     assert abs(result_mean.item() - 2.2676e-06   ) < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      Dict    =						self.get_scheduler_config()
     __lowerCAmelCase  :      Optional[int]    =						scheduler_class(**lowerCAmelCase   )

     scheduler.set_timesteps(self.num_inference_steps	,							device=lowerCAmelCase   )

     __lowerCAmelCase  :      Union[str, Any]    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      Dict    =						self.dummy_model()
     __lowerCAmelCase  :      Dict    =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
     __lowerCAmelCase  :      Dict    =						sample.to(lowerCAmelCase   )

     for t in scheduler.timesteps:
        __lowerCAmelCase  :      Union[str, Any]    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      List[str]    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      int    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      Any    =						output.prev_sample

     __lowerCAmelCase  :      int    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      str    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 10.0807   ) < 1e-2
     assert abs(result_mean.item() - 0.0131   ) < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      Optional[int]    =						self.get_scheduler_config()
     __lowerCAmelCase  :      List[Any]    =						scheduler_class(**lowerCAmelCase	,							use_karras_sigmas=lowerCAmelCase   )

     scheduler.set_timesteps(self.num_inference_steps	,							device=lowerCAmelCase   )

     __lowerCAmelCase  :      str    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      str    =						self.dummy_model()
     __lowerCAmelCase  :      str    =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
     __lowerCAmelCase  :      int    =						sample.to(lowerCAmelCase   )

     for t in scheduler.timesteps:
        __lowerCAmelCase  :      int    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Dict    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Tuple    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      List[Any]    =						output.prev_sample

     __lowerCAmelCase  :      Tuple    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      Any    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 124.52_2994_9951_1719   ) < 1e-2
     assert abs(result_mean.item() - 0.1_6213_9326_3339_9963   ) < 1e-3


