

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__						=	logging.get_logger(__name__)

class    a__  (   _A     ):
		"""simple docstring"""







		__lowerCamelCase	    =    ['input_values', 'attention_mask']


		def __init__(      self , lowercase=1 , lowercase=16000 , lowercase=0.0 , lowercase=False , lowercase=True , **lowercase , )					->  Any:
						'''simple docstring'''






						super().__init__(feature_size=UpperCamelCase__ , sampling_rate=UpperCamelCase__ , padding_value=UpperCamelCase__ , **UpperCamelCase__    )
						A__            =     return_attention_mask
						A__            =     do_normalize


		@staticmethod
		def        UpperCamelCase						(      lowercase , lowercase , lowercase = 0.0    )					->  List[np.ndarray]:
						'''simple docstring'''






						if attention_mask is not None:
										A__            =     np.array(UpperCamelCase__ , np.intaa    )
										A__            =     []

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

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

						return normed_input_values


		def __call__(      self , lowercase , lowercase = False , lowercase = None , lowercase = False , lowercase = None , lowercase = None , lowercase = None , lowercase = None , **lowercase , )					->  BatchFeature:
						'''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."    )

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

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

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

						A__            =     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
						A__            =     padded_inputs["input_values"]
						if not isinstance(input_values[0] , np.ndarray    ):
										A__            =     [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    )
						):
										A__            =     [array.astype(np.floataa    ) for array in input_values]
						elif isinstance(UpperCamelCase__ , np.ndarray    ) and input_values.dtype is np.dtype(np.floataa    ):
										A__            =     input_values.astype(np.floataa    )

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

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

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

						return padded_inputs




import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest

class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    (DDIMScheduler,)
			__lowerCamelCase	    =    (('eta', 0.0), ('num_inference_steps', 50))


			def        UpperCamelCase						(      self , **lowercase    )					->  Optional[Any]:
							'''simple docstring'''






							A__            =     {
							    "num_train_timesteps": 1000,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							    "clip_sample": True,
							}

							config.update(**lowercase    )
							return config


			def        UpperCamelCase						(      self , **lowercase    )					->  List[Any]:
							'''simple docstring'''






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

							A__      ,			A__            =     10, 0.0

							A__            =     self.dummy_model()
							A__            =     self.dummy_sample_deter

							scheduler.set_timesteps(lowercase    )

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

							return sample


			def        UpperCamelCase						(      self    )					->  Optional[Any]:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  Union[str, Any]:
							'''simple docstring'''






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

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


			def        UpperCamelCase						(      self    )					->  Dict:
							'''simple docstring'''






							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=lowercase , beta_end=lowercase    )


			def        UpperCamelCase						(      self    )					->  Optional[int]:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  Dict:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  Dict:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  Union[str, Any]:
							'''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        UpperCamelCase						(      self    )					->  Dict:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  int:
							'''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        UpperCamelCase						(      self    )					->  List[str]:
							'''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        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






							A__            =     self.scheduler_classes[0]
							A__            =     self.get_scheduler_config()
							A__            =     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_4771    )    ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(980 , 960    ) - 0.3_2460    )    ) < 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_0979    )    ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999 , 998    ) - 0.02    )    ) < 1e-5


			def        UpperCamelCase						(      self    )					->  str:
							'''simple docstring'''






							A__            =     self.full_loop()

							A__            =     torch.sum(torch.abs(lowercase    )    )
							A__            =     torch.mean(torch.abs(lowercase    )    )

							assert abs(result_sum.item() - 172.0067    ) < 1e-2
							assert abs(result_mean.item() - 0.22_3967    ) < 1e-3


			def        UpperCamelCase						(      self    )					->  Dict:
							'''simple docstring'''






							A__            =     self.full_loop(prediction_type="v_prediction"    )

							A__            =     torch.sum(torch.abs(lowercase    )    )
							A__            =     torch.mean(torch.abs(lowercase    )    )

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


			def        UpperCamelCase						(      self    )					->  List[Any]:
							'''simple docstring'''






							A__            =     self.full_loop(set_alpha_to_one=lowercase , beta_start=0.01    )
							A__            =     torch.sum(torch.abs(lowercase    )    )
							A__            =     torch.mean(torch.abs(lowercase    )    )

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


			def        UpperCamelCase						(      self    )					->  Tuple:
							'''simple docstring'''






							A__            =     self.full_loop(set_alpha_to_one=lowercase , beta_start=0.01    )
							A__            =     torch.sum(torch.abs(lowercase    )    )
							A__            =     torch.mean(torch.abs(lowercase    )    )

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

