'''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


lowerCAmelCase_			:			Any    	=   logging.get_logger(__name__)





class 				lowercase    (    a__			):
   lowerCamelCase_            =['input_values', 'attention_mask']


   def __init__( self					:						Optional[Any] ,		__lowerCAmelCase					:						int=1 ,		__lowerCAmelCase					:						Union[str, Any]=1_6000 ,		__lowerCAmelCase					:						int=0.0 ,		__lowerCAmelCase					:						Optional[Any]=False ,		__lowerCAmelCase					:						Optional[Any]=True ,		**__lowerCAmelCase					:						Union[str, Any] ,		)   ->       Optional[Any]:
         super().__init__(feature_size=__UpperCAmelCase ,		sampling_rate=__UpperCAmelCase ,		padding_value=__UpperCAmelCase ,		**__UpperCAmelCase)
         lowercase_     			=   return_attention_mask
         lowercase_     			=   do_normalize


   @staticmethod
   def __UpperCAmelCase							( __lowerCAmelCase					:						int ,		__lowerCAmelCase					:						Any ,		__lowerCAmelCase					:						Union[str, Any] = 0.0)   ->       List[np.ndarray]:
         if attention_mask is not None:
               lowercase_     			=   np.array(__UpperCAmelCase ,		np.intaa)
               lowercase_     			=   []

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

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

         return normed_input_values


   def __call__( self					:						List[str] ,		__lowerCAmelCase					:						List[str] ,		__lowerCAmelCase					:						Any = False ,		__lowerCAmelCase					:						int = None ,		__lowerCAmelCase					:						List[Any] = False ,		__lowerCAmelCase					:						Optional[Any] = None ,		__lowerCAmelCase					:						Dict = None ,		__lowerCAmelCase					:						Tuple = None ,		__lowerCAmelCase					:						str = None ,		**__lowerCAmelCase					:						Optional[int] ,		)   ->       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 `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(__UpperCAmelCase ,		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(__UpperCAmelCase ,		(list, tuple)) and (isinstance(raw_speech[0] ,		(np.ndarray, tuple, list)))
         )

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

         # convert into correct format for padding
         lowercase_     			=   BatchFeature({"input_values": raw_speech})

         lowercase_     			=   self.pad(
             __UpperCAmelCase ,		padding=__UpperCAmelCase ,		max_length=__UpperCAmelCase ,		truncation=__UpperCAmelCase ,		pad_to_multiple_of=__UpperCAmelCase ,		return_attention_mask=__UpperCAmelCase ,		)

         # convert input values to correct format
         lowercase_     			=   padded_inputs["input_values"]
         if not isinstance(input_values[0] ,		np.ndarray):
               lowercase_     			=   [np.asarray(__UpperCAmelCase ,		dtype=np.floataa) for array in input_values]
         elif (
             not isinstance(__UpperCAmelCase ,		np.ndarray)
             and isinstance(input_values[0] ,		np.ndarray)
             and input_values[0].dtype is np.dtype(np.floataa)
         ):
               lowercase_     			=   [array.astype(np.floataa) for array in input_values]
         elif isinstance(__UpperCAmelCase ,		np.ndarray) and input_values.dtype is np.dtype(np.floataa):
               lowercase_     			=   input_values.astype(np.floataa)

         # convert attention_mask to correct format
         lowercase_     			=   padded_inputs.get("attention_mask")
         if attention_mask is not None:
               lowercase_     			=   [np.asarray(__UpperCAmelCase ,		dtype=np.intaa) for array in attention_mask]

         # zero-mean and unit-variance normalization
         if self.do_normalize:
               lowercase_     			=   (
                   attention_mask
                   if self._get_padding_strategies(__UpperCAmelCase ,		max_length=__UpperCAmelCase) is not PaddingStrategy.DO_NOT_PAD
                   else None
               )
               lowercase_     			=   self.zero_mean_unit_var_norm(
                   padded_inputs["input_values"] ,		attention_mask=__UpperCAmelCase ,		padding_value=self.padding_value)

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

         return padded_inputs










"""simple docstring"""






import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class      __lowerCamelCase	(       a__					):

     '''simple docstring'''
     A_							:						Tuple     =		(DDIMScheduler,)
     A_							:						Tuple     =		(('eta', 0.0), ('num_inference_steps', 50))


     def 				_UpperCAmelCase   (   self     ,					**__UpperCAmelCase				)							->				str:
          _a     						=					{
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.0001,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
              '''clip_sample''': True,
          }

          config.update(**__UpperCAmelCase				)
          return config


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

          _a						,   _a     						=					10, 0.0

          _a     						=					self.dummy_model()
          _a     						=					self.dummy_sample_deter

          scheduler.set_timesteps(__UpperCAmelCase				)

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

          return sample


     def 				_UpperCAmelCase   (   self				)							->				str:
          for timesteps in [100, 500, 1000]:
               self.check_over_configs(num_train_timesteps=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Dict:
          for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=__UpperCAmelCase				)

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


     def 				_UpperCAmelCase   (   self				)							->				int:
          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=__UpperCAmelCase     ,					beta_end=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Union[str, Any]:
          for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=__UpperCAmelCase				)


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


     def 				_UpperCAmelCase   (   self				)							->				int:
          for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Union[str, Any]:
          for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          self.check_over_configs(thresholding=__UpperCAmelCase				)
          for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(
                        thresholding=__UpperCAmelCase     ,					prediction_type=__UpperCAmelCase     ,					sample_max_value=__UpperCAmelCase     ,					)


     def 				_UpperCAmelCase   (   self				)							->				int:
          for t in [1, 10, 49]:
               self.check_over_forward(time_step=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Tuple:
          for t, num_inference_steps in zip([1, 10, 50]     ,					[10, 50, 500]				):
               self.check_over_forward(time_step=__UpperCAmelCase     ,					num_inference_steps=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Optional[Any]:
          for t, eta in zip([1, 10, 49]     ,					[0.0, 0.5, 1.0]				):
               self.check_over_forward(time_step=__UpperCAmelCase     ,					eta=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				int:
          _a     						=					self.scheduler_classes[0]
          _a     						=					self.get_scheduler_config()
          _a     						=					scheduler_class(**__UpperCAmelCase				)

          assert torch.sum(torch.abs(scheduler._get_variance(0     ,					0				) - 0.0				)				) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(420     ,					400				) - 0.14771				)				) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(980     ,					960				) - 0.32460				)				) < 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				) - 0.00979				)				) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(999     ,					998				) - 0.02				)				) < 1e-5


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

          _a     						=					torch.sum(torch.abs(__UpperCAmelCase				)				)
          _a     						=					torch.mean(torch.abs(__UpperCAmelCase				)				)

          assert abs(result_sum.item() - 172.0067				) < 1e-2
          assert abs(result_mean.item() - 0.223967				) < 1e-3


     def 				_UpperCAmelCase   (   self				)							->				Any:
          _a     						=					self.full_loop(prediction_type='''v_prediction'''				)

          _a     						=					torch.sum(torch.abs(__UpperCAmelCase				)				)
          _a     						=					torch.mean(torch.abs(__UpperCAmelCase				)				)

          assert abs(result_sum.item() - 52.5302				) < 1e-2
          assert abs(result_mean.item() - 0.0684				) < 1e-3


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          # We specify different beta, so that the first alpha is 0.99
          _a     						=					self.full_loop(set_alpha_to_one=__UpperCAmelCase     ,					beta_start=0.01				)
          _a     						=					torch.sum(torch.abs(__UpperCAmelCase				)				)
          _a     						=					torch.mean(torch.abs(__UpperCAmelCase				)				)

          assert abs(result_sum.item() - 149.8295				) < 1e-2
          assert abs(result_mean.item() - 0.1951				) < 1e-3





     def 				_UpperCAmelCase   (   self				)							->				int:
          # We specify different beta, so that the first alpha is 0.99
          _a     						=					self.full_loop(set_alpha_to_one=__UpperCAmelCase     ,					beta_start=0.01				)
          _a     						=					torch.sum(torch.abs(__UpperCAmelCase				)				)
          _a     						=					torch.mean(torch.abs(__UpperCAmelCase				)				)

          assert abs(result_sum.item() - 149.0784				) < 1e-2
          assert abs(result_mean.item() - 0.1941				) < 1e-3