
'''simple docstring'''



from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_lowercase          =						logging.get_logger(__name__)




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: Any										=  ['''input_features''', '''attention_mask''']




						def __init__(   self       ,  _lowercase=80       ,  _lowercase=16_000       ,  _lowercase=80       ,  _lowercase=0.0       ,  _lowercase=True       ,  _lowercase=True       ,  _lowercase=True       ,  **_lowercase       ,  ):

												"""simple docstring"""



												super().__init__(feature_size=_lowercase       ,  sampling_rate=_lowercase       ,  padding_value=_lowercase       ,  **_lowercase )
												_lowerCAmelCase    		=					num_mel_bins
												_lowerCAmelCase    		=					do_ceptral_normalize
												_lowerCAmelCase    		=					normalize_means
												_lowerCAmelCase    		=					normalize_vars
												_lowerCAmelCase    		=					True




						def    _lowercase      (   self       ,  _lowercase       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
												_lowerCAmelCase    		=					torch.from_numpy(_lowercase ).unsqueeze(0 )
												_lowerCAmelCase    		=					ta_kaldi.fbank(_lowercase       ,  num_mel_bins=self.num_mel_bins       ,  sample_frequency=self.sampling_rate )
												return features.numpy()




						@staticmethod
						def    _lowercase      (   _lowercase       ,  _lowercase       ,  _lowercase = True       ,  _lowercase = True       ,  _lowercase = 0.0       ,  ):

												"""simple docstring"""



												if normalize_means:
																		_lowerCAmelCase    		=					x[:input_length].mean(axis=0 )
																		_lowerCAmelCase    		=					np.subtract(_lowercase       ,  _lowercase )
												if normalize_vars:
																		_lowerCAmelCase    		=					x[:input_length].std(axis=0 )
																		_lowerCAmelCase    		=					np.divide(_lowercase       ,  _lowercase )

												if input_length < x.shape[0]:
																		_lowerCAmelCase    		=					padding_value

												# make sure array is in float32
												_lowerCAmelCase    		=					x.astype(np.floataa )

												return x




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase = None ):

												"""simple docstring"""



												_lowerCAmelCase    		=					attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
												return [
												    self.utterance_cmvn(_lowercase       ,  _lowercase       ,  self.normalize_means       ,  self.normalize_vars       ,  self.padding_value )
												    for x, n in zip(_lowercase       ,  _lowercase )
												]




						def __call__(   self       ,  _lowercase       ,  _lowercase = False       ,  _lowercase = None       ,  _lowercase = False       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = None       ,  **_lowercase       ,  ):

												"""simple docstring"""



												if sampling_rate is not None:
																		if sampling_rate != self.sampling_rate:
																								raise ValueError(
																								    F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
																								    F' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
																								    F' {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    		=					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    		=					is_batched_numpy or (
												    isinstance(_lowercase       ,  (list, tuple) ) and (isinstance(raw_speech[0]       ,  (np.ndarray, tuple, list) ))
												)

												if is_batched:
																		_lowerCAmelCase    		=					[np.asarray(_lowercase       ,  dtype=np.floataa ) for speech in raw_speech]
												elif not is_batched and not isinstance(_lowercase       ,  np.ndarray ):
																		_lowerCAmelCase    		=					np.asarray(_lowercase       ,  dtype=np.floataa )
												elif isinstance(_lowercase       ,  np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
																		_lowerCAmelCase    		=					raw_speech.astype(np.floataa )

												# always return batch
												if not is_batched:
																		_lowerCAmelCase    		=					[raw_speech]

												# extract fbank features
												_lowerCAmelCase    		=					[self._extract_fbank_features(_lowercase ) for waveform in raw_speech]

												# convert into correct format for padding
												_lowerCAmelCase    		=					BatchFeature({"""input_features""": features} )

												_lowerCAmelCase    		=					self.pad(
												    _lowercase       ,  padding=_lowercase       ,  max_length=_lowercase       ,  truncation=_lowercase       ,  pad_to_multiple_of=_lowercase       ,  return_attention_mask=_lowercase       ,  **_lowercase       ,  )

												# make sure list is in array format
												_lowerCAmelCase    		=					padded_inputs.get("""input_features""" )
												if isinstance(input_features[0]       ,  _lowercase ):
																		_lowerCAmelCase    		=					[np.asarray(_lowercase       ,  dtype=np.floataa ) for feature in input_features]

												_lowerCAmelCase    		=					padded_inputs.get("""attention_mask""" )
												if attention_mask is not None:
																		_lowerCAmelCase    		=					[np.asarray(_lowercase       ,  dtype=np.intaa ) for array in attention_mask]

												# Utterance-level cepstral mean and variance normalization
												if self.do_ceptral_normalize:
																		_lowerCAmelCase    		=					(
																		    np.array(_lowercase       ,  dtype=np.intaa )
																		    if self._get_padding_strategies(_lowercase       ,  max_length=_lowercase ) is not PaddingStrategy.DO_NOT_PAD
																		    else None
																		)
																		_lowerCAmelCase    		=					self.normalize(
																		    padded_inputs["""input_features"""]       ,  attention_mask=_lowercase )

												if return_tensors is not None:
																		_lowerCAmelCase    		=					padded_inputs.convert_to_tensors(_lowercase )

												return padded_inputs


'''simple docstring'''



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: Tuple										=  (DDPMScheduler,)




						def    _lowercase      (   self       ,  **_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					{
												    """num_train_timesteps""": 1_000,
												    """beta_start""": 0.0001,
												    """beta_end""": 0.02,
												    """beta_schedule""": """linear""",
												    """variance_type""": """fixed_small""",
												    """clip_sample""": True,
												}

												config.update(**_lowercase )
												return config




						def    _lowercase      (   self ):

												"""simple docstring"""



												for timesteps in [1, 5, 100, 1_000]:
																		self.check_over_configs(num_train_timesteps=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]       ,  [0.002, 0.02, 0.2, 2] ):
																		self.check_over_configs(beta_start=_lowercase       ,  beta_end=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												for schedule in ["linear", "squaredcos_cap_v2"]:
																		self.check_over_configs(beta_schedule=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												for variance in ["fixed_small", "fixed_large", "other"]:
																		self.check_over_configs(variance_type=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												for clip_sample in [True, False]:
																		self.check_over_configs(clip_sample=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												self.check_over_configs(thresholding=_lowercase )
												for threshold in [0.5, 1.0, 2.0]:
																		for prediction_type in ["epsilon", "sample", "v_prediction"]:
																								self.check_over_configs(
																								    thresholding=_lowercase       ,  prediction_type=_lowercase       ,  sample_max_value=_lowercase       ,  )




						def    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



												for t in [0, 500, 999]:
																		self.check_over_forward(time_step=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0979 ) ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1e-5




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					len(_lowercase )

												_lowerCAmelCase    		=					self.dummy_model()
												_lowerCAmelCase    		=					self.dummy_sample_deter
												_lowerCAmelCase    		=					torch.manual_seed(0 )

												for t in reversed(range(_lowercase ) ):
																		# 1. predict noise residual
																		_lowerCAmelCase    		=					model(_lowercase       ,  _lowercase )

																		# 2. predict previous mean of sample x_t-1
																		_lowerCAmelCase    		=					scheduler.step(_lowercase       ,  _lowercase       ,  _lowercase       ,  generator=_lowercase ).prev_sample

																		# if t > 0:
																		#     noise = self.dummy_sample_deter
																		#     variance = scheduler.get_variance(t) ** (0.5) * noise
																		#
																		# sample = pred_prev_sample + variance
																		_lowerCAmelCase    		=					pred_prev_sample

												_lowerCAmelCase    		=					torch.sum(torch.abs(_lowercase ) )
												_lowerCAmelCase    		=					torch.mean(torch.abs(_lowercase ) )

												assert abs(result_sum.item() - 258.9606 ) < 1e-2
												assert abs(result_mean.item() - 0.3372 ) < 1e-3




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config(prediction_type="""v_prediction""" )
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					len(_lowercase )

												_lowerCAmelCase    		=					self.dummy_model()
												_lowerCAmelCase    		=					self.dummy_sample_deter
												_lowerCAmelCase    		=					torch.manual_seed(0 )

												for t in reversed(range(_lowercase ) ):
																		# 1. predict noise residual
																		_lowerCAmelCase    		=					model(_lowercase       ,  _lowercase )

																		# 2. predict previous mean of sample x_t-1
																		_lowerCAmelCase    		=					scheduler.step(_lowercase       ,  _lowercase       ,  _lowercase       ,  generator=_lowercase ).prev_sample

																		# if t > 0:
																		#     noise = self.dummy_sample_deter
																		#     variance = scheduler.get_variance(t) ** (0.5) * noise
																		#
																		# sample = pred_prev_sample + variance
																		_lowerCAmelCase    		=					pred_prev_sample

												_lowerCAmelCase    		=					torch.sum(torch.abs(_lowercase ) )
												_lowerCAmelCase    		=					torch.mean(torch.abs(_lowercase ) )

												assert abs(result_sum.item() - 202.0296 ) < 1e-2
												assert abs(result_mean.item() - 0.2631 ) < 1e-3




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					[100, 87, 50, 1, 0]

												scheduler.set_timesteps(timesteps=_lowercase )

												_lowerCAmelCase    		=					scheduler.timesteps

												for i, timestep in enumerate(_lowercase ):
																		if i == len(_lowercase ) - 1:
																								_lowerCAmelCase    		=					-1
																		else:
																								_lowerCAmelCase    		=					timesteps[i + 1]

																		_lowerCAmelCase    		=					scheduler.previous_timestep(_lowercase )
																		_lowerCAmelCase    		=					prev_t.item()

																		self.assertEqual(_lowercase       ,  _lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					[100, 87, 50, 51, 0]

												with self.assertRaises(_lowercase       ,  msg="""`custom_timesteps` must be in descending order.""" ):
																		scheduler.set_timesteps(timesteps=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					[100, 87, 50, 1, 0]
												_lowerCAmelCase    		=					len(_lowercase )

												with self.assertRaises(_lowercase       ,  msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
																		scheduler.set_timesteps(num_inference_steps=_lowercase       ,  timesteps=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					[scheduler.config.num_train_timesteps]

												with self.assertRaises(
												    _lowercase       ,  msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""       ,  ):
																		scheduler.set_timesteps(timesteps=_lowercase )
