
'''simple docstring'''
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       =(DDIMParallelScheduler,)
 lowerCamelCase				       =(('''eta''', 0.0), ('''num_inference_steps''', 50))
 def 		lowercase_(   self			:    Any				,   **lowerCamelCase			:    int       ):



   """simple docstring"""





   __A		:     int												=		{
       """num_train_timesteps""": 10_00,
       """beta_start""": 0.0_001,
       """beta_end""": 0.02,
       """beta_schedule""": """linear""",
       """clip_sample""": True,
   }

   config.update(**lowerCamelCase       )
   return config
 def 		lowercase_(   self			:    List[Any]				,   **lowerCamelCase			:    Tuple       ):



   """simple docstring"""





   __A		:     int												=		self.scheduler_classes[0]
   __A		:     int												=		self.get_scheduler_config(**lowerCamelCase       )
   __A		:     Any												=		scheduler_class(**lowerCamelCase       )

   __A		:     List[str]												=		10, 0.0

   __A		:     Dict												=		self.dummy_model()
   __A		:     List[Any]												=		self.dummy_sample_deter

   scheduler.set_timesteps(lowerCamelCase       )

   for t in scheduler.timesteps:
     __A		:     Union[str, Any]												=		model(lowerCamelCase				,   lowerCamelCase       )
     __A		:     Optional[int]												=		scheduler.step(lowerCamelCase				,   lowerCamelCase				,   lowerCamelCase				,   lowerCamelCase       ).prev_sample

   return sample
 def 		lowercase_(   self			:    int       ):



   """simple docstring"""





   for timesteps in [1_00, 5_00, 10_00]:
     self.check_over_configs(num_train_timesteps=lowerCamelCase       )
 def 		lowercase_(   self			:    int       ):



   """simple docstring"""





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

   __A		:     Any												=		self.scheduler_classes[0]
   __A		:     List[str]												=		self.get_scheduler_config(steps_offset=1       )
   __A		:     Dict												=		scheduler_class(**lowerCamelCase       )
   scheduler.set_timesteps(5       )
   assert torch.equal(scheduler.timesteps				,   torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]       )       )
 def 		lowercase_(   self			:    Dict       ):



   """simple docstring"""





   for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]				,   [0.002, 0.02, 0.2, 2]       ):
     self.check_over_configs(beta_start=lowerCamelCase				,   beta_end=lowerCamelCase       )
 def 		lowercase_(   self			:    Dict       ):



   """simple docstring"""





   for schedule in ["linear", "squaredcos_cap_v2"]:
     self.check_over_configs(beta_schedule=lowerCamelCase       )
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   for prediction_type in ["epsilon", "v_prediction"]:
     self.check_over_configs(prediction_type=lowerCamelCase       )
 def 		lowercase_(   self			:    int       ):



   """simple docstring"""





   for clip_sample in [True, False]:
     self.check_over_configs(clip_sample=lowerCamelCase       )
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   for timestep_spacing in ["trailing", "leading"]:
     self.check_over_configs(timestep_spacing=lowerCamelCase       )
 def 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   for rescale_betas_zero_snr in [True, False]:
     self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase       )
 def 		lowercase_(   self			:    str       ):



   """simple docstring"""





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



   """simple docstring"""





   for t in [1, 10, 49]:
     self.check_over_forward(time_step=lowerCamelCase       )
 def 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   for t, num_inference_steps in zip([1, 10, 50]				,   [10, 50, 5_00]       ):
     self.check_over_forward(time_step=lowerCamelCase				,   num_inference_steps=lowerCamelCase       )
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





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



   """simple docstring"""





   __A		:     Dict												=		self.scheduler_classes[0]
   __A		:     Optional[int]												=		self.get_scheduler_config()
   __A		:     List[Any]												=		scheduler_class(**lowerCamelCase       )

   assert torch.sum(torch.abs(scheduler._get_variance(0				,   0       ) - 0.0       )       ) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(4_20				,   4_00       ) - 0.14_771       )       ) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(9_80				,   9_60       ) - 0.32_460       )       ) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(0				,   0       ) - 0.0       )       ) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(4_87				,   4_86       ) - 0.00_979       )       ) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(9_99				,   9_98       ) - 0.02       )       ) < 1e-5
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   __A		:     int												=		self.scheduler_classes[0]
   __A		:     Dict												=		self.get_scheduler_config()
   __A		:     List[str]												=		scheduler_class(**lowerCamelCase       )

   __A		:     List[Any]												=		10, 0.0
   scheduler.set_timesteps(lowerCamelCase       )

   __A		:     Optional[int]												=		self.dummy_model()
   __A		:     Optional[int]												=		self.dummy_sample_deter
   __A		:     Union[str, Any]												=		self.dummy_sample_deter + 0.1
   __A		:     Optional[Any]												=		self.dummy_sample_deter - 0.1

   __A		:     List[Any]												=		samplea.shape[0]
   __A		:     Dict												=		torch.stack([samplea, samplea, samplea]				,   dim=0       )
   __A		:     Union[str, Any]												=		torch.arange(lowerCamelCase       )[0:3, None].repeat(1				,   lowerCamelCase       )

   __A		:     List[str]												=		model(samples.flatten(0				,   1       )				,   timesteps.flatten(0				,   1       )       )
   __A		:     Tuple												=		scheduler.batch_step_no_noise(lowerCamelCase				,   timesteps.flatten(0				,   1       )				,   samples.flatten(0				,   1       )				,   lowerCamelCase       )

   __A		:     Dict												=		torch.sum(torch.abs(lowerCamelCase       )       )
   __A		:     int												=		torch.mean(torch.abs(lowerCamelCase       )       )

   assert abs(result_sum.item() - 11_47.79_04       ) < 1e-2
   assert abs(result_mean.item() - 0.4_982       ) < 1e-3
 def 		lowercase_(   self			:    str       ):



   """simple docstring"""





   __A		:     Optional[Any]												=		self.full_loop()

   __A		:     Optional[Any]												=		torch.sum(torch.abs(lowerCamelCase       )       )
   __A		:     Dict												=		torch.mean(torch.abs(lowerCamelCase       )       )

   assert abs(result_sum.item() - 172.0_067       ) < 1e-2
   assert abs(result_mean.item() - 0.223_967       ) < 1e-3
 def 		lowercase_(   self			:    Union[str, Any]       ):



   """simple docstring"""





   __A		:     str												=		self.full_loop(prediction_type="""v_prediction"""       )

   __A		:     Tuple												=		torch.sum(torch.abs(lowerCamelCase       )       )
   __A		:     Tuple												=		torch.mean(torch.abs(lowerCamelCase       )       )

   assert abs(result_sum.item() - 52.5_302       ) < 1e-2
   assert abs(result_mean.item() - 0.0_684       ) < 1e-3
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   __A		:     Optional[Any]												=		self.full_loop(set_alpha_to_one=lowerCamelCase				,   beta_start=0.01       )
   __A		:     Union[str, Any]												=		torch.sum(torch.abs(lowerCamelCase       )       )
   __A		:     Optional[Any]												=		torch.mean(torch.abs(lowerCamelCase       )       )

   assert abs(result_sum.item() - 149.8_295       ) < 1e-2
   assert abs(result_mean.item() - 0.1_951       ) < 1e-3






 def 		lowercase_(   self			:    Dict       ):



   """simple docstring"""





   __A		:     int												=		self.full_loop(set_alpha_to_one=lowerCamelCase				,   beta_start=0.01       )
   __A		:     Union[str, Any]												=		torch.sum(torch.abs(lowerCamelCase       )       )
   __A		:     Optional[int]												=		torch.mean(torch.abs(lowerCamelCase       )       )

   assert abs(result_sum.item() - 149.0_784       ) < 1e-2
   assert abs(result_mean.item() - 0.1_941       ) < 1e-3





'''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


A__   :		Dict 		=logging.get_logger(__name__)







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       =['''input_values''', '''padding_mask''']
 def __init__(   self			:    Optional[int]				,   lowerCamelCase			:    int = 1				,   lowerCamelCase			:    int = 2_40_00				,   lowerCamelCase			:    float = 0.0				,   lowerCamelCase			:    float = None				,   lowerCamelCase			:    float = None				,   **lowerCamelCase			:    Dict				,   ):



   """simple docstring"""





   super().__init__(feature_size=lowerCamelCase				,   sampling_rate=lowerCamelCase				,   padding_value=lowerCamelCase				,   **lowerCamelCase       )
   __A		:     str												=		chunk_length_s
   __A		:     str												=		overlap
 @property
 def 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   if self.chunk_length_s is None:
     return None
   else:
     return int(self.chunk_length_s * self.sampling_rate       )
 @property
 def 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   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			:    List[str]				,   lowerCamelCase			:    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,   lowerCamelCase			:    Optional[Union[bool, str, PaddingStrategy]] = None				,   lowerCamelCase			:    Optional[bool] = False				,   lowerCamelCase			:    Optional[int] = None				,   lowerCamelCase			:    Optional[Union[str, TensorType]] = None				,   lowerCamelCase			:    Optional[int] = None				,   ):



   """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 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		:     List[Any]												=		True

   __A		:     List[str]												=		bool(
       isinstance(lowerCamelCase				,   (list, tuple)       ) and (isinstance(raw_audio[0]				,   (np.ndarray, tuple, list)       ))       )

   if is_batched:
     __A		:     Optional[Any]												=		[np.asarray(lowerCamelCase				,   dtype=np.floataa       ).T for audio in raw_audio]
   elif not is_batched and not isinstance(lowerCamelCase				,   np.ndarray       ):
     __A		:     Union[str, Any]												=		np.asarray(lowerCamelCase				,   dtype=np.floataa       )
   elif isinstance(lowerCamelCase				,   np.ndarray       ) and raw_audio.dtype is np.dtype(np.floataa       ):
     __A		:     Optional[Any]												=		raw_audio.astype(np.floataa       )

   # always return batch
   if not is_batched:
     __A		:     Tuple												=		[np.asarray(lowerCamelCase       ).T]

   # verify inputs are valid
   for idx, example in enumerate(lowerCamelCase       ):
     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		:     Optional[Any]												=		None
   __A		:     Dict												=		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		:     Optional[Any]												=		min(array.shape[0] for array in raw_audio       )
       __A		:     Dict												=		int(np.floor(max_length / self.chunk_stride       )       )
       __A		:     Union[str, Any]												=		(nb_step - 1) * self.chunk_stride + self.chunk_length
     elif padding:
       __A		:     int												=		max(array.shape[0] for array in raw_audio       )
       __A		:     Optional[Any]												=		int(np.ceil(max_length / self.chunk_stride       )       )
       __A		:     Any												=		(nb_step - 1) * self.chunk_stride + self.chunk_length
       __A		:     Optional[int]												=		"""max_length"""
     else:
       __A		:     Optional[Any]												=		input_values

        # normal padding on batch
   if padded_inputs is None:
     __A		:     Tuple												=		self.pad(
         lowerCamelCase				,   max_length=lowerCamelCase				,   truncation=lowerCamelCase				,   padding=lowerCamelCase				,   return_attention_mask=lowerCamelCase				,   )
     if padding:
       __A		:     List[Any]												=		padded_inputs.pop("""attention_mask"""       )

   __A		:     List[Any]												=		[]
   for example in padded_inputs.pop("""input_values"""       ):
     if self.feature_size == 1:
       __A		:     Optional[Any]												=		example[..., None]
     input_values.append(example.T       )

   __A		:     str												=		input_values
   if return_tensors is not None:
     __A		:     Any												=		padded_inputs.convert_to_tensors(lowerCamelCase       )

   return padded_inputs



