





"""simple docstring"""





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_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       List[str]	 =		["""audio_values""", """audio_mask"""]




				def __init__(     self  :Optional[Any]						,    __lowercase  :str=2048						,    __lowercase  :Optional[int]=1						,    __lowercase  :Dict=[16, 16]						,    __lowercase  :Tuple=128						,    __lowercase  :int=4_4100						,    __lowercase  :Any=86						,    __lowercase  :List[Any]=2048						,    __lowercase  :str=0.0						,    **__lowercase  :str						,    ):
											super().__init__(
											    feature_size=__lowercase						,    sampling_rate=__lowercase						,    padding_value=__lowercase						,    **__lowercase						,    )

											__lowerCamelCase      :      str         =spectrogram_length
											__lowerCamelCase      :      Dict         =num_channels
											__lowerCamelCase      :      Tuple         =patch_size
											__lowerCamelCase      :      List[Any]         =feature_size // self.patch_size[1]
											__lowerCamelCase      :      Optional[Any]         =n_fft
											__lowerCamelCase      :      Any         =sampling_rate // hop_length_to_sampling_rate
											__lowerCamelCase      :      Optional[int]         =sampling_rate
											__lowerCamelCase      :      Optional[Any]         =padding_value
											__lowerCamelCase      :      Tuple         =mel_filter_bank(
											    num_frequency_bins=1 + n_fft // 2						,    num_mel_filters=__lowercase						,    min_frequency=0.0						,    max_frequency=22050.0						,    sampling_rate=__lowercase						,    norm='''slaney'''						,    mel_scale='''slaney'''						,    ).T




				def 					__lowercase  (     self  :Dict						,    __lowercase  :np.array			):
											__lowerCamelCase      :      List[Any]         =spectrogram(
											    __lowercase						,    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      :      Optional[int]         =log_spec[:, :-1]
											__lowerCamelCase      :      Tuple         =log_spec - 20.0
											__lowerCamelCase      :      Optional[Any]         =np.clip(log_spec / 40.0						,    -2.0						,    0.0			) + 1.0
											return log_spec




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

											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[Any]         =isinstance(__lowercase						,    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      :      Optional[Any]         =is_batched_numpy or (
											    isinstance(__lowercase						,    (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(__lowercase						,    np.ndarray			):
																		__lowerCamelCase      :      Union[str, Any]         =np.asarray(__lowercase						,    dtype=np.floataa			)
											elif isinstance(__lowercase						,    np.ndarray			) and raw_speech.dtype is np.dtype(np.floataa			):
																		__lowerCamelCase      :      str         =raw_speech.astype(np.floataa			)
											# always return batch
											if not is_batched:
																		__lowerCamelCase      :      List[str]         =[np.asarray([raw_speech]			).T]

											# Convert audio signals to log mel spectrograms, truncate by time axis
											__lowerCamelCase      :      List[str]         =[
											    self._np_extract_fbank_features(waveform.squeeze()			).T[: self.spectrogram_length] for waveform in raw_speech
											]
											if isinstance(audio_features[0]						,    __lowercase			):
																		__lowerCamelCase      :      Dict         =[np.asarray(__lowercase						,    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      :      Optional[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      :      Union[str, Any]         =np.array(__lowercase			).astype(np.floataa			)

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

											# return as BatchFeature
											if return_attention_mask:
																		__lowerCamelCase      :      List[str]         ={'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
											else:
																		__lowerCamelCase      :      Union[str, Any]         ={'''audio_values''': padded_audio_features}

											__lowerCamelCase      :      str         =BatchFeature(data=__lowercase						,    tensor_type=__lowercase			)
											return encoded_inputs











"""simple docstring"""





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       Optional[Any]	 =		(UnCLIPScheduler,)




				def 					__lowercase  (     self  :Any						,    **__lowercase  :str			):
											__lowerCamelCase      :      Optional[int]         ={
											    '''num_train_timesteps''': 1000,
											    '''variance_type''': '''fixed_small_log''',
											    '''clip_sample''': True,
											    '''clip_sample_range''': 1.0,
											    '''prediction_type''': '''epsilon''',
											}

											config.update(**__lowercase			)
											return config




				def 					__lowercase  (     self  :Tuple			):
											for timesteps in [1, 5, 100, 1000]:
																		self.check_over_configs(num_train_timesteps=__lowercase			)




				def 					__lowercase  (     self  :Tuple			):
											for variance in ["fixed_small_log", "learned_range"]:
																		self.check_over_configs(variance_type=__lowercase			)




				def 					__lowercase  (     self  :List[str]			):
											for clip_sample in [True, False]:
																		self.check_over_configs(clip_sample=__lowercase			)




				def 					__lowercase  (     self  :List[Any]			):
											for clip_sample_range in [1, 5, 10, 20]:
																		self.check_over_configs(clip_sample_range=__lowercase			)




				def 					__lowercase  (     self  :Any			):
											for prediction_type in ["epsilon", "sample"]:
																		self.check_over_configs(prediction_type=__lowercase			)




				def 					__lowercase  (     self  :Union[str, Any]			):
											for time_step in [0, 500, 999]:
																		for prev_timestep in [None, 5, 100, 250, 500, 750]:
																									if prev_timestep is not None and prev_timestep >= time_step:
																																continue

																									self.check_over_forward(time_step=__lowercase						,    prev_timestep=__lowercase			)




				def 					__lowercase  (     self  :Union[str, Any]			):
											__lowerCamelCase      :      List[Any]         =self.scheduler_classes[0]
											__lowerCamelCase      :      str         =self.get_scheduler_config(variance_type='''fixed_small_log'''			)
											__lowerCamelCase      :      Tuple         =scheduler_class(**__lowercase			)

											assert torch.sum(torch.abs(scheduler._get_variance(0			) - 1.0_0_0_0e-1_0			)			) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(487			) - 0.0549625			)			) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(999			) - 0.9994987			)			) < 1e-5




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Tuple         =self.scheduler_classes[0]
											__lowerCamelCase      :      Optional[Any]         =self.get_scheduler_config(variance_type='''learned_range'''			)
											__lowerCamelCase      :      Optional[Any]         =scheduler_class(**__lowercase			)

											__lowerCamelCase      :      Optional[int]         =0.5

											assert scheduler._get_variance(1						,    predicted_variance=__lowercase			) - -10.1712790 < 1e-5
											assert scheduler._get_variance(487						,    predicted_variance=__lowercase			) - -5.7998052 < 1e-5
											assert scheduler._get_variance(999						,    predicted_variance=__lowercase			) - -0.0010011 < 1e-5




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Any         =self.scheduler_classes[0]
											__lowerCamelCase      :      Optional[int]         =self.get_scheduler_config()
											__lowerCamelCase      :      str         =scheduler_class(**__lowercase			)

											__lowerCamelCase      :      Tuple         =scheduler.timesteps

											__lowerCamelCase      :      Any         =self.dummy_model()
											__lowerCamelCase      :      Any         =self.dummy_sample_deter
											__lowerCamelCase      :      List[Any]         =torch.manual_seed(0			)

											for i, t in enumerate(__lowercase			):
																		# 1. predict noise residual
																		__lowerCamelCase      :      Tuple         =model(__lowercase						,    __lowercase			)

																		# 2. predict previous mean of sample x_t-1
																		__lowerCamelCase      :      List[Any]         =scheduler.step(__lowercase						,    __lowercase						,    __lowercase						,    generator=__lowercase			).prev_sample

																		__lowerCamelCase      :      List[Any]         =pred_prev_sample

											__lowerCamelCase      :      Optional[Any]         =torch.sum(torch.abs(__lowercase			)			)
											__lowerCamelCase      :      Optional[int]         =torch.mean(torch.abs(__lowercase			)			)

											assert abs(result_sum.item() - 252.2682495			) < 1e-2
											assert abs(result_mean.item() - 0.3284743			) < 1e-3




				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      Optional[Any]         =self.scheduler_classes[0]
											__lowerCamelCase      :      Any         =self.get_scheduler_config()
											__lowerCamelCase      :      Optional[Any]         =scheduler_class(**__lowercase			)

											scheduler.set_timesteps(25			)

											__lowerCamelCase      :      Dict         =scheduler.timesteps

											__lowerCamelCase      :      List[str]         =self.dummy_model()
											__lowerCamelCase      :      List[str]         =self.dummy_sample_deter
											__lowerCamelCase      :      Any         =torch.manual_seed(0			)

											for i, t in enumerate(__lowercase			):
																		# 1. predict noise residual
																		__lowerCamelCase      :      Optional[Any]         =model(__lowercase						,    __lowercase			)

																		if i + 1 == timesteps.shape[0]:
																									__lowerCamelCase      :      Any         =None
																		else:
																									__lowerCamelCase      :      Union[str, Any]         =timesteps[i + 1]

																		# 2. predict previous mean of sample x_t-1
																		__lowerCamelCase      :      List[str]         =scheduler.step(
																		    __lowercase						,    __lowercase						,    __lowercase						,    prev_timestep=__lowercase						,    generator=__lowercase			).prev_sample

																		__lowerCamelCase      :      int         =pred_prev_sample

											__lowerCamelCase      :      List[Any]         =torch.sum(torch.abs(__lowercase			)			)
											__lowerCamelCase      :      List[str]         =torch.mean(torch.abs(__lowercase			)			)

											assert abs(result_sum.item() - 258.2044983			) < 1e-2
											assert abs(result_mean.item() - 0.3362038			) < 1e-3




				def 					__lowercase  (     self  :List[str]			):
											pass




				def 					__lowercase  (     self  :Tuple			):
											pass




