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

import numpy as np

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


UpperCamelCase_   	=	logging.get_logger(__name__)

class       a		(   __UpperCAmelCase					):
							lowercase_		:						List[Any]   							=  ['input_values', 'attention_mask']







							def __init__(						self  :  List[str]      ,				snake_case__  :  int = 1      ,				snake_case__  :  int = 16_000      ,				snake_case__  :  float = 0.0      ,				snake_case__  :  bool = False      ,				snake_case__  :  int = 80      ,				snake_case__  :  int = 16      ,				snake_case__  :  int = 64      ,				snake_case__  :  str = "hann_window"      ,				snake_case__  :  float = 1.0      ,				snake_case__  :  float = 80      ,				snake_case__  :  float = 7_600      ,				snake_case__  :  float = 1E-10      ,				snake_case__  :  int = 2      ,				snake_case__  :  bool = True      ,				**snake_case__  :  Optional[Any]      ,				):
								"""simple docstring"""







								super().__init__(feature_size=snake_case__      ,				sampling_rate=snake_case__      ,				padding_value=snake_case__      ,				**snake_case__ )
								__lowerCAmelCase         =							do_normalize
								__lowerCAmelCase         =							return_attention_mask

								__lowerCAmelCase         =							num_mel_bins
								__lowerCAmelCase         =							hop_length
								__lowerCAmelCase         =							win_length
								__lowerCAmelCase         =							win_function
								__lowerCAmelCase         =							frame_signal_scale
								__lowerCAmelCase         =							fmin
								__lowerCAmelCase         =							fmax
								__lowerCAmelCase         =							mel_floor
								__lowerCAmelCase         =							reduction_factor

								__lowerCAmelCase         =							win_length * sampling_rate // 1_000
								__lowerCAmelCase         =							hop_length * sampling_rate // 1_000
								__lowerCAmelCase         =							optimal_fft_length(self.sample_size )
								__lowerCAmelCase         =							(self.n_fft // 2) + 1

								__lowerCAmelCase         =							window_function(window_length=self.sample_size      ,				name=self.win_function      ,				periodic=snake_case__ )

								__lowerCAmelCase         =							mel_filter_bank(
								    num_frequency_bins=self.n_freqs      ,				num_mel_filters=self.num_mel_bins      ,				min_frequency=self.fmin      ,				max_frequency=self.fmax      ,				sampling_rate=self.sampling_rate      ,				norm="slaney"      ,				mel_scale="slaney"      ,				)

								if frame_signal_scale != 1.0:
									warnings.warn(
									    "The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"      ,				snake_case__      ,				)
								if reduction_factor != 2.0:
									warnings.warn(
									    "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"      ,				snake_case__      ,				)







							@staticmethod
							# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
							def       UpperCAmelCase__					(						snake_case__  :  List[np.ndarray]      ,				snake_case__  :  List[np.ndarray]      ,				snake_case__  :  float = 0.0 ):
								"""simple docstring"""







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

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

										normed_input_values.append(snake_case__ )
								else:
									__lowerCAmelCase         =							[(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]

								return normed_input_values







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  np.ndarray      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							spectrogram(
								    snake_case__      ,				window=self.window      ,				frame_length=self.sample_size      ,				hop_length=self.sample_stride      ,				fft_length=self.n_fft      ,				mel_filters=self.mel_filters      ,				mel_floor=self.mel_floor      ,				log_mel="log10"      ,				)
								return log_mel_spec.T







							def __call__(						self  :  Optional[int]      ,				snake_case__  :  Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None      ,				snake_case__  :  Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None      ,				snake_case__  :  Union[bool, str, PaddingStrategy] = False      ,				snake_case__  :  Optional[int] = None      ,				snake_case__  :  bool = False      ,				snake_case__  :  Optional[int] = None      ,				snake_case__  :  Optional[bool] = None      ,				snake_case__  :  Optional[Union[str, TensorType]] = None      ,				snake_case__  :  Optional[int] = None      ,				**snake_case__  :  Dict      ,				):
								"""simple docstring"""







								if audio is None and audio_target is None:
									raise ValueError("You must provide either `audio` or `audio_target` values." )

								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 audio is not None:
									__lowerCAmelCase         =							self._process_audio(
									    snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				**snake_case__      ,				)
								else:
									__lowerCAmelCase         =							None

								if audio_target is not None:
									__lowerCAmelCase         =							self._process_audio(
									    snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				**snake_case__      ,				)

									if inputs is None:
										return inputs_target
									else:
										__lowerCAmelCase         =							inputs_target["input_values"]
										__lowerCAmelCase         =							inputs_target.get("attention_mask" )
										if decoder_attention_mask is not None:
											__lowerCAmelCase         =							decoder_attention_mask

								return inputs







							def       UpperCAmelCase__					(						self  :  Any      ,				snake_case__  :  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,				snake_case__  :  bool = False      ,				snake_case__  :  Union[bool, str, PaddingStrategy] = False      ,				snake_case__  :  Optional[int] = None      ,				snake_case__  :  bool = False      ,				snake_case__  :  Optional[int] = None      ,				snake_case__  :  Optional[bool] = None      ,				snake_case__  :  Optional[Union[str, TensorType]] = None      ,				**snake_case__  :  int      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							isinstance(snake_case__      ,				np.ndarray ) and len(speech.shape ) > 1
								if is_batched_numpy and len(speech.shape ) > 2:
									raise ValueError(F"Only mono-channel audio is supported for input to {self}" )
								__lowerCAmelCase         =							is_batched_numpy or (
								    isinstance(snake_case__      ,				(list, tuple) ) and (isinstance(speech[0]      ,				(np.ndarray, tuple, list) ))
								)

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

								# always return batch
								if not is_batched:
									__lowerCAmelCase         =							[speech]

								# needed to make pad() work on spectrogram inputs
								__lowerCAmelCase         =							self.feature_size

								# convert into correct format for padding
								if is_target:
									__lowerCAmelCase         =							[self._extract_mel_features(snake_case__ ) for waveform in speech]
									__lowerCAmelCase         =							BatchFeature({"input_values": features} )
									__lowerCAmelCase         =							self.num_mel_bins
								else:
									__lowerCAmelCase         =							BatchFeature({"input_values": speech} )

								__lowerCAmelCase         =							self.pad(
								    snake_case__      ,				padding=snake_case__      ,				max_length=snake_case__      ,				truncation=snake_case__      ,				pad_to_multiple_of=snake_case__      ,				return_attention_mask=snake_case__      ,				**snake_case__      ,				)

								__lowerCAmelCase         =							feature_size_hack

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

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

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

								if return_tensors is not None:
									__lowerCAmelCase         =							padded_inputs.convert_to_tensors(snake_case__ )

								return padded_inputs







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							super().to_dict()

								# Don't serialize these as they are derived from the other properties.
								__lowerCAmelCase         =							["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
								for name in names:
									if name in output:
										del output[name]

								return output








# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    "pipelines_utils",
    "0.22.0",
    "Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.",
    standard_warn=False,
    stacklevel=3,
)






