


"""simple docstring"""






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Optional[int]    =		(DEISMultistepScheduler,)
     A_       :	Dict    =		(('num_inference_steps', 25),)


     def 							_UpperCAmelCase      (      self    ,				**__UpperCAmelCase							)       ->							Optional[Any]:
          _a    	=				{
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.0001,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
              '''solver_order''': 2,
          }

          config.update(**__UpperCAmelCase							)
          return config


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase=0    ,				**__UpperCAmelCase							)       ->							Any:
          _a    	=				dict(self.forward_default_kwargs							)
          _a    	=				kwargs.pop('''num_inference_steps'''    ,				__UpperCAmelCase							)
          _a    	=				self.dummy_sample
          _a    	=				0.1 * sample
          _a    	=				[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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


     def 							_UpperCAmelCase      (      self							)       ->							str:
          pass


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase=0    ,				**__UpperCAmelCase							)       ->							List[str]:
          _a    	=				dict(self.forward_default_kwargs							)
          _a    	=				kwargs.pop('''num_inference_steps'''    ,				__UpperCAmelCase							)
          _a    	=				self.dummy_sample
          _a    	=				0.1 * sample
          _a    	=				[residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
               _a    	=				self.get_scheduler_config()
               _a    	=				scheduler_class(**__UpperCAmelCase							)
               scheduler.set_timesteps(__UpperCAmelCase							)

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

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

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

               _a    	=				scheduler.step(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				**__UpperCAmelCase							).prev_sample
               _a    	=				new_scheduler.step(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				**__UpperCAmelCase							).prev_sample

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


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase=None    ,				**__UpperCAmelCase							)       ->							Optional[Any]:
          if scheduler is None:
               _a    	=				self.scheduler_classes[0]
               _a    	=				self.get_scheduler_config(**__UpperCAmelCase							)
               _a    	=				scheduler_class(**__UpperCAmelCase							)

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

          _a    	=				10
          _a    	=				self.dummy_model()
          _a    	=				self.dummy_sample_deter
          scheduler.set_timesteps(__UpperCAmelCase							)

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

          return sample


     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          _a    	=				dict(self.forward_default_kwargs							)

          _a    	=				kwargs.pop('''num_inference_steps'''    ,				__UpperCAmelCase							)

          for scheduler_class in self.scheduler_classes:
               _a    	=				self.get_scheduler_config()
               _a    	=				scheduler_class(**__UpperCAmelCase							)

               _a    	=				self.dummy_sample
               _a    	=				0.1 * sample

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

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

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

               _a    	=				scheduler.step(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				**__UpperCAmelCase							).prev_sample
               _a    	=				scheduler.step(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				**__UpperCAmelCase							).prev_sample

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


     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          # make sure that iterating over schedulers with same config names gives same results
          # for defaults
          _a    	=				DEISMultistepScheduler(**self.get_scheduler_config()							)
          _a    	=				self.full_loop(scheduler=__UpperCAmelCase							)
          _a    	=				torch.mean(torch.abs(__UpperCAmelCase							)							)

          assert abs(result_mean.item() - 0.23916							) < 1e-3

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

          _a    	=				self.full_loop(scheduler=__UpperCAmelCase							)
          _a    	=				torch.mean(torch.abs(__UpperCAmelCase							)							)

          assert abs(result_mean.item() - 0.23916							) < 1e-3


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


     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          self.check_over_configs(thresholding=__UpperCAmelCase							)
          for order in [1, 2, 3]:
               for solver_type in ["logrho"]:
                    for threshold in [0.5, 1.0, 2.0]:
                         for prediction_type in ["epsilon", "sample"]:
                              self.check_over_configs(
                                  thresholding=__UpperCAmelCase    ,				prediction_type=__UpperCAmelCase    ,				sample_max_value=__UpperCAmelCase    ,				algorithm_type='''deis'''    ,				solver_order=__UpperCAmelCase    ,				solver_type=__UpperCAmelCase    ,				)


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


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          for algorithm_type in ["deis"]:
               for solver_type in ["logrho"]:
                    for order in [1, 2, 3]:
                         for prediction_type in ["epsilon", "sample"]:
                              self.check_over_configs(
                                  solver_order=__UpperCAmelCase    ,				solver_type=__UpperCAmelCase    ,				prediction_type=__UpperCAmelCase    ,				algorithm_type=__UpperCAmelCase    ,				)
                              _a    	=				self.full_loop(
                                  solver_order=__UpperCAmelCase    ,				solver_type=__UpperCAmelCase    ,				prediction_type=__UpperCAmelCase    ,				algorithm_type=__UpperCAmelCase    ,				)
                              assert not torch.isnan(__UpperCAmelCase							).any(), "Samples have nan numbers"


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          self.check_over_configs(lower_order_final=__UpperCAmelCase							)
          self.check_over_configs(lower_order_final=__UpperCAmelCase							)


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


     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          _a    	=				self.full_loop()
          _a    	=				torch.mean(torch.abs(__UpperCAmelCase							)							)

          assert abs(result_mean.item() - 0.23916							) < 1e-3


     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          _a    	=				self.full_loop(prediction_type='''v_prediction'''							)
          _a    	=				torch.mean(torch.abs(__UpperCAmelCase							)							)

          assert abs(result_mean.item() - 0.091							) < 1e-3





     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          _a    	=				self.scheduler_classes[0]
          _a    	=				self.get_scheduler_config(thresholding=__UpperCAmelCase    ,				dynamic_thresholding_ratio=0							)
          _a    	=				scheduler_class(**__UpperCAmelCase							)

          _a    	=				10
          _a    	=				self.dummy_model()
          _a    	=				self.dummy_sample_deter.half()
          scheduler.set_timesteps(__UpperCAmelCase							)

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

          assert sample.dtype == torch.floataa



"""simple docstring"""






from typing import List, Optional, Union

import numpy as np

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


__snake_case		=       logging.get_logger(__name__)


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Union[str, Any]    =		['input_values', 'padding_mask']


     def __init__(      self    ,				__UpperCAmelCase = 1    ,				__UpperCAmelCase = 24000    ,				__UpperCAmelCase = 0.0    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							List[str]:
          super().__init__(feature_size=__UpperCAmelCase    ,				sampling_rate=__UpperCAmelCase    ,				padding_value=__UpperCAmelCase    ,				**__UpperCAmelCase							)
          _a    	=				chunk_length_s
          _a    	=				overlap


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          if self.chunk_length_s is None:
               return None
          else:
               return int(self.chunk_length_s * self.sampling_rate							)


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          if self.chunk_length_s is None or self.overlap is None:
               return None
          else:
               return max(1    ,				int((1.0 - self.overlap) * self.chunk_length							)							)





     def __call__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = False    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				)       ->							BatchFeature:
          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 audio 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.'''							)

          if padding and truncation:
               raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''							)
          elif padding is None:
               # by default let's pad the inputs
               _a    	=				True

          _a    	=				bool(
              isinstance(__UpperCAmelCase    ,				(list, tuple)							) and (isinstance(raw_audio[0]    ,				(np.ndarray, tuple, list)							))							)

          if is_batched:
               _a    	=				[np.asarray(__UpperCAmelCase    ,				dtype=np.floataa							).T for audio in raw_audio]
          elif not is_batched and not isinstance(__UpperCAmelCase    ,				np.ndarray							):
               _a    	=				np.asarray(__UpperCAmelCase    ,				dtype=np.floataa							)
          elif isinstance(__UpperCAmelCase    ,				np.ndarray							) and raw_audio.dtype is np.dtype(np.floataa							):
               _a    	=				raw_audio.astype(np.floataa							)

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

          # verify inputs are valid
          for idx, example in enumerate(__UpperCAmelCase							):
               if example.ndim > 2:
                    raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}'							)
               if self.feature_size == 1 and example.ndim != 1:
                    raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels'							)
               if self.feature_size == 2 and example.shape[-1] != 2:
                    raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels'							)

          _a    	=				None
          _a    	=				BatchFeature({'''input_values''': raw_audio}							)
          if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
               if truncation:
                    _a    	=				min(array.shape[0] for array in raw_audio							)
                    _a    	=				int(np.floor(max_length / self.chunk_stride							)							)
                    _a    	=				(nb_step - 1) * self.chunk_stride + self.chunk_length
               elif padding:
                    _a    	=				max(array.shape[0] for array in raw_audio							)
                    _a    	=				int(np.ceil(max_length / self.chunk_stride							)							)
                    _a    	=				(nb_step - 1) * self.chunk_stride + self.chunk_length
                    _a    	=				'''max_length'''
               else:
                    _a    	=				input_values

        # normal padding on batch
          if padded_inputs is None:
               _a    	=				self.pad(
                   __UpperCAmelCase    ,				max_length=__UpperCAmelCase    ,				truncation=__UpperCAmelCase    ,				padding=__UpperCAmelCase    ,				return_attention_mask=__UpperCAmelCase    ,				)
               if padding:
                    _a    	=				padded_inputs.pop('''attention_mask'''							)

          _a    	=				[]
          for example in padded_inputs.pop('''input_values'''							):
               if self.feature_size == 1:
                    _a    	=				example[..., None]
               input_values.append(example.T							)

          _a    	=				input_values
          if return_tensors is not None:
               _a    	=				padded_inputs.convert_to_tensors(__UpperCAmelCase							)

          return padded_inputs