

'''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 TensorType, logging


UpperCAmelCase							=					logging.get_logger(__name__)


class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	["input_values", "attention_mask"]







       def __init__(							self     :	Any  , __lowercase     :	Tuple=1  , __lowercase     :	Tuple=16000  , __lowercase     :	Optional[Any]=128  , __lowercase     :	Dict=1024  , __lowercase     :	Optional[int]=0.0  , __lowercase     :	Union[str, Any]=True  , __lowercase     :	int=-4.2_6_7_7_3_9_3  , __lowercase     :	Union[str, Any]=4.5_6_8_9_9_7_4  , __lowercase     :	Tuple=False  , **__lowercase     :	Any  , ):







            """simple docstring"""







            super().__init__(feature_size=__lowercase  , sampling_rate=__lowercase  , padding_value=__lowercase  , **__lowercase )
            __lowercase     						=num_mel_bins
            __lowercase     						=max_length
            __lowercase     						=do_normalize
            __lowercase     						=mean
            __lowercase     						=std
            __lowercase     						=return_attention_mask







       def      snake_case				(							self     :	Union[str, Any]  , __lowercase     :	np.ndarray  , __lowercase     :	int  , ):







            """simple docstring"""







            __lowercase     						=torch.from_numpy(__lowercase ).unsqueeze(0 )
            __lowercase     						=ta_kaldi.fbank(
                __lowercase  , htk_compat=__lowercase  , sample_frequency=self.sampling_rate  , use_energy=__lowercase  , window_type='hanning'  , num_mel_bins=self.num_mel_bins  , dither=0.0  , frame_shift=10  , )

            __lowercase     						=fbank.shape[0]
            __lowercase     						=max_length - n_frames

            # pad or truncate, depending on difference
            if difference > 0:
                 __lowercase     						=torch.nn.ZeroPadad((0, 0, 0, difference) )
                 __lowercase     						=pad_module(__lowercase )
            elif difference < 0:
                 __lowercase     						=fbank[0:max_length, :]

            __lowercase     						=fbank.numpy()

            return fbank







       def      snake_case				(							self     :	Dict  , __lowercase     :	np.ndarray ):







            """simple docstring"""







            return (input_values - (self.mean)) / (self.std * 2)







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







            """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.' )

            __lowercase     						=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}''' )
            __lowercase     						=is_batched_numpy or (
                isinstance(__lowercase  , (list, tuple) ) and (isinstance(raw_speech[0]  , (np.ndarray, tuple, list) ))
            )

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

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

            # extract fbank features and pad/truncate to max_length
            __lowercase     						=[self._extract_fbank_features(__lowercase  , max_length=self.max_length ) for waveform in raw_speech]

            # convert into BatchFeature
            __lowercase     						=BatchFeature({'input_values': features} )

            # make sure list is in array format
            __lowercase     						=padded_inputs.get('input_values' )
            if isinstance(input_values[0]  , __lowercase ):
                 __lowercase     						=[np.asarray(__lowercase  , dtype=np.floataa ) for feature in input_values]

            # normalization
            if self.do_normalize:
                 __lowercase     						=[self.normalize(__lowercase ) for feature in input_values]

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

            return padded_inputs





'''simple docstring'''

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	(DDIMScheduler,)
       lowerCAmelCase_				=	(("eta", 0.0), ("num_inference_steps", 5_0))







       def      snake_case				(							self     :	List[Any]  , **__lowercase     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						={
                'num_train_timesteps': 1000,
                'beta_start': 0.0_0_0_1,
                'beta_end': 0.0_2,
                'beta_schedule': 'linear',
                'clip_sample': True,
            }

            config.update(**__lowercase )
            return config







       def      snake_case				(							self     :	Optional[Any]  , **__lowercase     :	str ):







            """simple docstring"""







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

            __lowercase						,       __lowercase     						=10, 0.0

            __lowercase     						=self.dummy_model()
            __lowercase     						=self.dummy_sample_deter

            scheduler.set_timesteps(__lowercase )

            for t in scheduler.timesteps:
                 __lowercase     						=model(__lowercase  , __lowercase )
                 __lowercase     						=scheduler.step(__lowercase  , __lowercase  , __lowercase  , __lowercase ).prev_sample

            return sample







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            for timesteps in [100, 500, 1000]:
                 self.check_over_configs(num_train_timesteps=__lowercase )







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=__lowercase )

            __lowercase     						=self.scheduler_classes[0]
            __lowercase     						=self.get_scheduler_config(steps_offset=1 )
            __lowercase     						=scheduler_class(**__lowercase )
            scheduler.set_timesteps(5 )
            assert torch.equal(scheduler.timesteps  , torch.LongTensor([801, 601, 401, 201, 1] ) )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]  , [0.0_0_2, 0.0_2, 0.2, 2] ):
                 self.check_over_configs(beta_start=__lowercase  , beta_end=__lowercase )







       def      snake_case				(							self     :	str ):







            """simple docstring"""







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







       def      snake_case				(							self     :	str ):







            """simple docstring"""







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







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







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







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=__lowercase )







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=__lowercase )







       def      snake_case				(							self     :	int ):







            """simple docstring"""







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







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            for t in [1, 10, 49]:
                 self.check_over_forward(time_step=__lowercase )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for t, num_inference_steps in zip([1, 10, 50]  , [10, 50, 500] ):
                 self.check_over_forward(time_step=__lowercase  , num_inference_steps=__lowercase )







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            for t, eta in zip([1, 10, 49]  , [0.0, 0.5, 1.0] ):
                 self.check_over_forward(time_step=__lowercase  , eta=__lowercase )







       def      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







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

            assert torch.sum(torch.abs(scheduler._get_variance(0  , 0 ) - 0.0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(420  , 400 ) - 0.1_4_7_7_1 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(980  , 960 ) - 0.3_2_4_6_0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(0  , 0 ) - 0.0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(487  , 486 ) - 0.0_0_9_7_9 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999  , 998 ) - 0.0_2 ) ) < 1E-5







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            __lowercase     						=self.full_loop()

            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_7_2.0_0_6_7 ) < 1E-2
            assert abs(result_mean.item() - 0.2_2_3_9_6_7 ) < 1E-3







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            __lowercase     						=self.full_loop(prediction_type='v_prediction' )

            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 5_2.5_3_0_2 ) < 1E-2
            assert abs(result_mean.item() - 0.0_6_8_4 ) < 1E-3







       def      snake_case				(							self     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						=self.full_loop(set_alpha_to_one=__lowercase  , beta_start=0.0_1 )
            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_4_9.8_2_9_5 ) < 1E-2
            assert abs(result_mean.item() - 0.1_9_5_1 ) < 1E-3







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            __lowercase     						=self.full_loop(set_alpha_to_one=__lowercase  , beta_start=0.0_1 )
            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_4_9.0_7_8_4 ) < 1E-2
            assert abs(result_mean.item() - 0.1_9_4_1 ) < 1E-3


