



"""simple docstring"""
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


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					["input_values", "attention_mask"]






							def __init__(   self			,     a_ = 1			,     a_ = 1_6_0_0_0			,     a_ = 0.0			,     a_ = False			,     a_ = 8_0			,     a_ = 1_6			,     a_ = 6_4			,     a_ = "hann_window"			,     a_ = 1.0			,     a_ = 8_0			,     a_ = 7_6_0_0			,     a_ = 1e-10			,     a_ = 2			,     a_ = True			,     **a_			,     ):
									super().__init__(feature_size=a_			,     sampling_rate=a_			,     padding_value=a_			,     **a_      )
									a_     :      Optional[int] 						=     do_normalize
									a_     :      Optional[Any] 						=     return_attention_mask

									a_     :      Tuple 						=     num_mel_bins
									a_     :      List[Any] 						=     hop_length
									a_     :      Dict 						=     win_length
									a_     :      Any 						=     win_function
									a_     :      Union[str, Any] 						=     frame_signal_scale
									a_     :      Dict 						=     fmin
									a_     :      Any 						=     fmax
									a_     :      Dict 						=     mel_floor
									a_     :      Tuple 						=     reduction_factor

									a_     :      Optional[int] 						=     win_length * sampling_rate // 1_0_0_0
									a_     :      Union[str, Any] 						=     hop_length * sampling_rate // 1_0_0_0
									a_     :      List[Any] 						=     optimal_fft_length(self.sample_size      )
									a_     :      str 						=     (self.n_fft // 2) + 1

									a_     :      List[Any] 						=     window_function(window_length=self.sample_size			,     name=self.win_function			,     periodic=a_      )

									a_     :      List[Any] 						=     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"			,     a_			,     )
									if reduction_factor != 2.0:
											warnings.warn(
											    "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"			,     a_			,     )






							@staticmethod
							# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
							def snake_case_  (   a_			,     a_			,     a_ = 0.0      ):
									if attention_mask is not None:
											a_     :      List[Any] 						=     np.array(a_			,     np.intaa      )
											a_     :      Optional[int] 						=     []

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

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

									return normed_input_values






							def snake_case_  (   self			,     a_			,     ):
									a_     :      Union[str, Any] 						=     spectrogram(
									    a_			,     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			,     a_ = None			,     a_ = None			,     a_ = False			,     a_ = None			,     a_ = False			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     **a_			,     ):
									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:
											a_     :      List[str] 						=     self._process_audio(
											    a_			,     a_			,     a_			,     a_			,     a_			,     a_			,     a_			,     a_			,     **a_			,     )
									else:
											a_     :      Any 						=     None

									if audio_target is not None:
											a_     :      str 						=     self._process_audio(
											    a_			,     a_			,     a_			,     a_			,     a_			,     a_			,     a_			,     a_			,     **a_			,     )

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

									return inputs






							def snake_case_  (   self			,     a_			,     a_ = False			,     a_ = False			,     a_ = None			,     a_ = False			,     a_ = None			,     a_ = None			,     a_ = None			,     **a_			,     ):
									a_     :      Any 						=     isinstance(a_			,     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}"""      )
									a_     :      Dict 						=     is_batched_numpy or (
									    isinstance(a_			,     (list, tuple)      ) and (isinstance(speech[0]			,     (np.ndarray, tuple, list)      ))
									)

									if is_batched:
											a_     :      Optional[int] 						=     [np.asarray(a_			,     dtype=np.floataa      ) for speech in speech]
									elif not is_batched and not isinstance(a_			,     np.ndarray      ):
											a_     :      Any 						=     np.asarray(a_			,     dtype=np.floataa      )
									elif isinstance(a_			,     np.ndarray      ) and speech.dtype is np.dtype(np.floataa      ):
											a_     :      List[Any] 						=     speech.astype(np.floataa      )

									# always return batch
									if not is_batched:
											a_     :      List[str] 						=     [speech]

									# needed to make pad() work on spectrogram inputs
									a_     :      Dict 						=     self.feature_size

									# convert into correct format for padding
									if is_target:
											a_     :      List[Any] 						=     [self._extract_mel_features(a_      ) for waveform in speech]
											a_     :      str 						=     BatchFeature({"input_values": features}      )
											a_     :      List[str] 						=     self.num_mel_bins
									else:
											a_     :      Optional[int] 						=     BatchFeature({"input_values": speech}      )

									a_     :      int 						=     self.pad(
									    a_			,     padding=a_			,     max_length=a_			,     truncation=a_			,     pad_to_multiple_of=a_			,     return_attention_mask=a_			,     **a_			,     )

									a_     :      List[str] 						=     feature_size_hack

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

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

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

									if return_tensors is not None:
											a_     :      str 						=     padded_inputs.convert_to_tensors(a_      )

									return padded_inputs






							def snake_case_  (   self      ):
									a_     :      Tuple 						=     super().to_dict()

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

									return output





"""simple docstring"""
import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					["image_processor", "tokenizer"]
							__lowerCAmelCase    					=					"ViltImageProcessor"
							__lowerCAmelCase    					=					("BertTokenizer", "BertTokenizerFast")






							def __init__(   self			,     a_=None			,     a_=None			,     **a_      ):
									a_     :      int 						=     None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
											    " instead."			,     a_			,     )
											a_     :      List[Any] 						=     kwargs.pop("feature_extractor"      )

									a_     :      List[str] 						=     image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
											raise ValueError("You need to specify an `image_processor`."      )
									if tokenizer is None:
											raise ValueError("You need to specify a `tokenizer`."      )

									super().__init__(a_			,     a_      )
									a_     :      Dict 						=     self.image_processor






							def __call__(   self			,     a_			,     a_ = None			,     a_ = True			,     a_ = False			,     a_ = None			,     a_ = None			,     a_ = 0			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = False			,     a_ = False			,     a_ = False			,     a_ = False			,     a_ = True			,     a_ = None			,     **a_			,     ):
									a_     :      Union[str, Any] 						=     self.tokenizer(
									    text=a_			,     add_special_tokens=a_			,     padding=a_			,     truncation=a_			,     max_length=a_			,     stride=a_			,     pad_to_multiple_of=a_			,     return_token_type_ids=a_			,     return_attention_mask=a_			,     return_overflowing_tokens=a_			,     return_special_tokens_mask=a_			,     return_offsets_mapping=a_			,     return_length=a_			,     verbose=a_			,     return_tensors=a_			,     **a_			,     )
									# add pixel_values + pixel_mask
									a_     :      List[Any] 						=     self.image_processor(a_			,     return_tensors=a_      )
									encoding.update(a_      )

									return encoding






							def snake_case_  (   self			,     *a_			,     **a_      ):
									return self.tokenizer.batch_decode(*a_			,     **a_      )






							def snake_case_  (   self			,     *a_			,     **a_      ):
									return self.tokenizer.decode(*a_			,     **a_      )






							@property
							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 						=     self.tokenizer.model_input_names
									a_     :      Dict 						=     self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )






							@property
							def snake_case_  (   self      ):
									warnings.warn(
									    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,     a_			,     )
									return self.image_processor_class






							@property
							def snake_case_  (   self      ):
									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,     a_			,     )
									return self.image_processor