

import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= (UniPCMultistepScheduler,)
  a									= (("num_inference_steps", 25),)






  def 				lowercase_		(							self						:		List[str]     ,       **__lowerCamelCase						:		Any )		->							Dict:
         SCREAMING_SNAKE_CASE__   =   {
             '''num_train_timesteps''': 1000,
             '''beta_start''': 0.0001,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
             '''solver_order''': 2,
             '''solver_type''': '''bh2''',
         }

         config.update(**__lowerCamelCase )
         return config






  def 				lowercase_		(							self						:		List[str]     ,       __lowerCamelCase						:		Optional[int]=0     ,       **__lowerCamelCase						:		int )		->							Optional[int]:
         SCREAMING_SNAKE_CASE__   =   dict(self.forward_default_kwargs )
         SCREAMING_SNAKE_CASE__   =   kwargs.pop('''num_inference_steps'''     ,       __lowerCamelCase )
         SCREAMING_SNAKE_CASE__   =   self.dummy_sample
         SCREAMING_SNAKE_CASE__   =   0.1 * sample
         SCREAMING_SNAKE_CASE__   =   [residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
                SCREAMING_SNAKE_CASE__   =   self.get_scheduler_config(**__lowerCamelCase )
                SCREAMING_SNAKE_CASE__   =   scheduler_class(**__lowerCamelCase )
                scheduler.set_timesteps(__lowerCamelCase )
                # copy over dummy past residuals
                SCREAMING_SNAKE_CASE__   =   dummy_past_residuals[: scheduler.config.solver_order]

                with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(__lowerCamelCase )
                       SCREAMING_SNAKE_CASE__   =   scheduler_class.from_pretrained(__lowerCamelCase )
                       new_scheduler.set_timesteps(__lowerCamelCase )
                       # copy over dummy past residuals
                       SCREAMING_SNAKE_CASE__   =   dummy_past_residuals[: new_scheduler.config.solver_order]

                SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   sample, sample
                for t in range(__lowerCamelCase     ,       time_step + scheduler.config.solver_order + 1 ):
                       SCREAMING_SNAKE_CASE__   =   scheduler.step(__lowerCamelCase     ,       __lowerCamelCase     ,       __lowerCamelCase     ,       **__lowerCamelCase ).prev_sample
                       SCREAMING_SNAKE_CASE__   =   new_scheduler.step(__lowerCamelCase     ,       __lowerCamelCase     ,       __lowerCamelCase     ,       **__lowerCamelCase ).prev_sample

                       assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"






  def 				lowercase_		(							self						:		Dict     ,       __lowerCamelCase						:		str=0     ,       **__lowerCamelCase						:		Optional[Any] )		->							Tuple:
         SCREAMING_SNAKE_CASE__   =   dict(self.forward_default_kwargs )
         SCREAMING_SNAKE_CASE__   =   kwargs.pop('''num_inference_steps'''     ,       __lowerCamelCase )
         SCREAMING_SNAKE_CASE__   =   self.dummy_sample
         SCREAMING_SNAKE_CASE__   =   0.1 * sample
         SCREAMING_SNAKE_CASE__   =   [residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
                SCREAMING_SNAKE_CASE__   =   self.get_scheduler_config()
                SCREAMING_SNAKE_CASE__   =   scheduler_class(**__lowerCamelCase )
                scheduler.set_timesteps(__lowerCamelCase )

                # copy over dummy past residuals (must be after setting timesteps)
                SCREAMING_SNAKE_CASE__   =   dummy_past_residuals[: scheduler.config.solver_order]

                with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(__lowerCamelCase )
                       SCREAMING_SNAKE_CASE__   =   scheduler_class.from_pretrained(__lowerCamelCase )
                       # copy over dummy past residuals
                       new_scheduler.set_timesteps(__lowerCamelCase )

                       # copy over dummy past residual (must be after setting timesteps)
                       SCREAMING_SNAKE_CASE__   =   dummy_past_residuals[: new_scheduler.config.solver_order]

                SCREAMING_SNAKE_CASE__   =   scheduler.step(__lowerCamelCase     ,       __lowerCamelCase     ,       __lowerCamelCase     ,       **__lowerCamelCase ).prev_sample
                SCREAMING_SNAKE_CASE__   =   new_scheduler.step(__lowerCamelCase     ,       __lowerCamelCase     ,       __lowerCamelCase     ,       **__lowerCamelCase ).prev_sample

                assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"






  def 				lowercase_		(							self						:		Union[str, Any]     ,       __lowerCamelCase						:		Any=None     ,       **__lowerCamelCase						:		Tuple )		->							Tuple:
         if scheduler is None:
                SCREAMING_SNAKE_CASE__   =   self.scheduler_classes[0]
                SCREAMING_SNAKE_CASE__   =   self.get_scheduler_config(**__lowerCamelCase )
                SCREAMING_SNAKE_CASE__   =   scheduler_class(**__lowerCamelCase )

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

         SCREAMING_SNAKE_CASE__   =   10
         SCREAMING_SNAKE_CASE__   =   self.dummy_model()
         SCREAMING_SNAKE_CASE__   =   self.dummy_sample_deter
         scheduler.set_timesteps(__lowerCamelCase )

         for i, t in enumerate(scheduler.timesteps ):
                SCREAMING_SNAKE_CASE__   =   model(__lowerCamelCase     ,       __lowerCamelCase )
                SCREAMING_SNAKE_CASE__   =   scheduler.step(__lowerCamelCase     ,       __lowerCamelCase     ,       __lowerCamelCase ).prev_sample

         return sample






  def 				lowercase_		(							self						:		str )		->							Union[str, Any]:
         SCREAMING_SNAKE_CASE__   =   dict(self.forward_default_kwargs )

         SCREAMING_SNAKE_CASE__   =   kwargs.pop('''num_inference_steps'''     ,       __lowerCamelCase )

         for scheduler_class in self.scheduler_classes:
                SCREAMING_SNAKE_CASE__   =   self.get_scheduler_config()
                SCREAMING_SNAKE_CASE__   =   scheduler_class(**__lowerCamelCase )

                SCREAMING_SNAKE_CASE__   =   self.dummy_sample
                SCREAMING_SNAKE_CASE__   =   0.1 * sample

                if num_inference_steps is not None and hasattr(__lowerCamelCase     ,       '''set_timesteps''' ):
                       scheduler.set_timesteps(__lowerCamelCase )
                elif num_inference_steps is not None and not hasattr(__lowerCamelCase     ,       '''set_timesteps''' ):
                       SCREAMING_SNAKE_CASE__   =   num_inference_steps

                # copy over dummy past residuals (must be done after set_timesteps)
                SCREAMING_SNAKE_CASE__   =   [residual + 0.2, residual + 0.15, residual + 0.10]
                SCREAMING_SNAKE_CASE__   =   dummy_past_residuals[: scheduler.config.solver_order]

                SCREAMING_SNAKE_CASE__   =   scheduler.timesteps[5]
                SCREAMING_SNAKE_CASE__   =   scheduler.timesteps[6]

                SCREAMING_SNAKE_CASE__   =   scheduler.step(__lowerCamelCase     ,       __lowerCamelCase     ,       __lowerCamelCase     ,       **__lowerCamelCase ).prev_sample
                SCREAMING_SNAKE_CASE__   =   scheduler.step(__lowerCamelCase     ,       __lowerCamelCase     ,       __lowerCamelCase     ,       **__lowerCamelCase ).prev_sample

                self.assertEqual(output_a.shape     ,       sample.shape )
                self.assertEqual(output_a.shape     ,       output_a.shape )






  def 				lowercase_		(							self						:		Dict )		->							Optional[Any]:
         # make sure that iterating over schedulers with same config names gives same results
         # for defaults
         SCREAMING_SNAKE_CASE__   =   UniPCMultistepScheduler(**self.get_scheduler_config() )
         SCREAMING_SNAKE_CASE__   =   self.full_loop(scheduler=__lowerCamelCase )
         SCREAMING_SNAKE_CASE__   =   torch.mean(torch.abs(__lowerCamelCase ) )

         assert abs(result_mean.item() - 0.2464 ) < 1e-3

         SCREAMING_SNAKE_CASE__   =   DPMSolverSinglestepScheduler.from_config(scheduler.config )
         SCREAMING_SNAKE_CASE__   =   DEISMultistepScheduler.from_config(scheduler.config )
         SCREAMING_SNAKE_CASE__   =   DPMSolverMultistepScheduler.from_config(scheduler.config )
         SCREAMING_SNAKE_CASE__   =   UniPCMultistepScheduler.from_config(scheduler.config )

         SCREAMING_SNAKE_CASE__   =   self.full_loop(scheduler=__lowerCamelCase )
         SCREAMING_SNAKE_CASE__   =   torch.mean(torch.abs(__lowerCamelCase ) )

         assert abs(result_mean.item() - 0.2464 ) < 1e-3






  def 				lowercase_		(							self						:		Any )		->							Tuple:
         for timesteps in [25, 50, 100, 999, 1000]:
                self.check_over_configs(num_train_timesteps=__lowerCamelCase )






  def 				lowercase_		(							self						:		str )		->							Dict:
         self.check_over_configs(thresholding=__lowerCamelCase )
         for order in [1, 2, 3]:
                for solver_type in ["bh1", "bh2"]:
                       for threshold in [0.5, 1.0, 2.0]:
                              for prediction_type in ["epsilon", "sample"]:
                                     self.check_over_configs(
                                         thresholding=__lowerCamelCase     ,       prediction_type=__lowerCamelCase     ,       sample_max_value=__lowerCamelCase     ,       solver_order=__lowerCamelCase     ,       solver_type=__lowerCamelCase     ,       )






  def 				lowercase_		(							self						:		int )		->							Dict:
         for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=__lowerCamelCase )






  def 				lowercase_		(							self						:		Tuple )		->							List[str]:
         for solver_type in ["bh1", "bh2"]:
                for order in [1, 2, 3]:
                       for prediction_type in ["epsilon", "sample"]:
                              self.check_over_configs(
                                  solver_order=__lowerCamelCase     ,       solver_type=__lowerCamelCase     ,       prediction_type=__lowerCamelCase     ,       )
                              SCREAMING_SNAKE_CASE__   =   self.full_loop(
                                  solver_order=__lowerCamelCase     ,       solver_type=__lowerCamelCase     ,       prediction_type=__lowerCamelCase     ,       )
                              assert not torch.isnan(__lowerCamelCase ).any(), "Samples have nan numbers"






  def 				lowercase_		(							self						:		List[Any] )		->							Any:
         self.check_over_configs(lower_order_final=__lowerCamelCase )
         self.check_over_configs(lower_order_final=__lowerCamelCase )






  def 				lowercase_		(							self						:		List[str] )		->							Optional[Any]:
         for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
                self.check_over_forward(num_inference_steps=__lowerCamelCase     ,       time_step=0 )






  def 				lowercase_		(							self						:		Optional[Any] )		->							Any:
         SCREAMING_SNAKE_CASE__   =   self.full_loop()
         SCREAMING_SNAKE_CASE__   =   torch.mean(torch.abs(__lowerCamelCase ) )

         assert abs(result_mean.item() - 0.2464 ) < 1e-3






  def 				lowercase_		(							self						:		Dict )		->							List[str]:
         SCREAMING_SNAKE_CASE__   =   self.full_loop(prediction_type='''v_prediction''' )
         SCREAMING_SNAKE_CASE__   =   torch.mean(torch.abs(__lowerCamelCase ) )

         assert abs(result_mean.item() - 0.1014 ) < 1e-3






  def 				lowercase_		(							self						:		Any )		->							str:
         SCREAMING_SNAKE_CASE__   =   self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE__   =   self.get_scheduler_config(thresholding=__lowerCamelCase     ,       dynamic_thresholding_ratio=0 )
         SCREAMING_SNAKE_CASE__   =   scheduler_class(**__lowerCamelCase )

         SCREAMING_SNAKE_CASE__   =   10
         SCREAMING_SNAKE_CASE__   =   self.dummy_model()
         SCREAMING_SNAKE_CASE__   =   self.dummy_sample_deter.half()
         scheduler.set_timesteps(__lowerCamelCase )

         for i, t in enumerate(scheduler.timesteps ):
                SCREAMING_SNAKE_CASE__   =   model(__lowerCamelCase     ,       __lowerCamelCase )
                SCREAMING_SNAKE_CASE__   =   scheduler.step(__lowerCamelCase     ,       __lowerCamelCase     ,       __lowerCamelCase ).prev_sample

         assert sample.dtype == torch.floataa







  def 				lowercase_		(							self						:		str     ,       **__lowerCamelCase						:		Tuple )		->							List[str]:
         for scheduler_class in self.scheduler_classes:
                SCREAMING_SNAKE_CASE__   =   self.get_scheduler_config(**__lowerCamelCase )
                SCREAMING_SNAKE_CASE__   =   scheduler_class(**__lowerCamelCase )

                scheduler.set_timesteps(scheduler.config.num_train_timesteps )
                assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps





import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


_SCREAMING_SNAKE_CASE					:  List[str]             =      logging.get_logger(__name__)






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= ["input_features"]






  def __init__(							self						:		Dict     ,       __lowerCamelCase						:		Tuple=80     ,       __lowerCamelCase						:		List[Any]=1_6000     ,       __lowerCamelCase						:		Optional[int]=160     ,       __lowerCamelCase						:		List[str]=30     ,       __lowerCamelCase						:		List[Any]=400     ,       __lowerCamelCase						:		Union[str, Any]=0.0     ,       __lowerCamelCase						:		str=False     ,       **__lowerCamelCase						:		List[str]     ,       )		->							Any:
       super().__init__(
           feature_size=__lowerCamelCase     ,       sampling_rate=__lowerCamelCase     ,       padding_value=__lowerCamelCase     ,       return_attention_mask=__lowerCamelCase     ,       **__lowerCamelCase     ,       )
       SCREAMING_SNAKE_CASE__   =   n_fft
       SCREAMING_SNAKE_CASE__   =   hop_length
       SCREAMING_SNAKE_CASE__   =   chunk_length
       SCREAMING_SNAKE_CASE__   =   chunk_length * sampling_rate
       SCREAMING_SNAKE_CASE__   =   self.n_samples // hop_length
       SCREAMING_SNAKE_CASE__   =   sampling_rate
       SCREAMING_SNAKE_CASE__   =   mel_filter_bank(
           num_frequency_bins=1 + n_fft // 2     ,       num_mel_filters=__lowerCamelCase     ,       min_frequency=0.0     ,       max_frequency=8000.0     ,       sampling_rate=__lowerCamelCase     ,       norm='''slaney'''     ,       mel_scale='''slaney'''     ,       )






  def 				lowercase_		(							self						:		int     ,       __lowerCamelCase						:		np.array )		->							np.ndarray:
       SCREAMING_SNAKE_CASE__   =   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     ,       log_mel='''log10'''     ,       )
       SCREAMING_SNAKE_CASE__   =   log_spec[:, :-1]
       SCREAMING_SNAKE_CASE__   =   np.maximum(__lowerCamelCase     ,       log_spec.max() - 8.0 )
       SCREAMING_SNAKE_CASE__   =   (log_spec + 4.0) / 4.0
       return log_spec






  @staticmethod
  # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
  def 				lowercase_		(							__lowerCamelCase						:		List[np.ndarray]     ,       __lowerCamelCase						:		List[np.ndarray]     ,       __lowerCamelCase						:		float = 0.0 )		->							List[np.ndarray]:
       if attention_mask is not None:
            SCREAMING_SNAKE_CASE__   =   np.array(__lowerCamelCase     ,       np.intaa )
            SCREAMING_SNAKE_CASE__   =   []

            for vector, length in zip(__lowerCamelCase     ,       attention_mask.sum(-1 ) ):
                 SCREAMING_SNAKE_CASE__   =   (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 )
                 if length < normed_slice.shape[0]:
                      SCREAMING_SNAKE_CASE__   =   padding_value

                 normed_input_values.append(__lowerCamelCase )
       else:
            SCREAMING_SNAKE_CASE__   =   [(x - x.mean()) / np.sqrt(x.var() + 1e-7 ) for x in input_values]

       return normed_input_values






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

       if sampling_rate is not None:
            if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     f'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
                     f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
                     f''' was sampled 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.''' )

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

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

       # always return batch
       if not is_batched:
            SCREAMING_SNAKE_CASE__   =   [np.asarray([raw_speech] ).T]

       SCREAMING_SNAKE_CASE__   =   BatchFeature({'''input_features''': raw_speech} )

       # convert into correct format for padding

       SCREAMING_SNAKE_CASE__   =   self.pad(
           __lowerCamelCase     ,       padding=__lowerCamelCase     ,       max_length=max_length if max_length else self.n_samples     ,       truncation=__lowerCamelCase     ,       pad_to_multiple_of=__lowerCamelCase     ,       return_attention_mask=return_attention_mask or do_normalize     ,       )

       # zero-mean and unit-variance normalization
       if do_normalize:
            SCREAMING_SNAKE_CASE__   =   self.zero_mean_unit_var_norm(
                padded_inputs['''input_features''']     ,       attention_mask=padded_inputs['''attention_mask''']     ,       padding_value=self.padding_value     ,       )
            SCREAMING_SNAKE_CASE__   =   np.stack(padded_inputs['''input_features''']     ,       axis=0 )

       # make sure list is in array format
       SCREAMING_SNAKE_CASE__   =   padded_inputs.get('''input_features''' ).transpose(2     ,       0     ,       1 )

       SCREAMING_SNAKE_CASE__   =   [self._np_extract_fbank_features(__lowerCamelCase ) for waveform in input_features[0]]

       if isinstance(input_features[0]     ,       __lowerCamelCase ):
            SCREAMING_SNAKE_CASE__   =   [np.asarray(__lowerCamelCase     ,       dtype=np.floataa ) for feature in input_features]
       else:
            SCREAMING_SNAKE_CASE__   =   input_features

       if return_attention_mask:
            # rescale from sample (48000) to feature (3000)
            SCREAMING_SNAKE_CASE__   =   padded_inputs['''attention_mask'''][:, :: self.hop_length]

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

       return padded_inputs







  def 				lowercase_		(							self						:		str )		->							Dict[str, Any]:
       SCREAMING_SNAKE_CASE__   =   copy.deepcopy(self.__dict__ )
       SCREAMING_SNAKE_CASE__   =   self.__class__.__name__
       if "mel_filters" in output:
            del output["mel_filters"]
       return output


