"""simple docstring"""
from typing import 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 ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)



class   lowerCAmelCase_    (			_SCREAMING_SNAKE_CASE					):



    """simple docstring"""
    _lowerCAmelCase     :    Tuple			  =						["input_features", "attention_mask"]

    def __init__(				self		,    lowerCAmelCase=80		,    lowerCAmelCase=1_60_00		,    lowerCAmelCase=0.0		,    lowerCAmelCase=10		,    lowerCAmelCase=25		,    lowerCAmelCase="hamming_window"		,    lowerCAmelCase=3_27_68.0		,    lowerCAmelCase=0.97		,    lowerCAmelCase=1.0		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=False		,    **lowerCAmelCase		,    ):






          """simple docstring"""

          super().__init__(feature_size=A_		,    sampling_rate=A_		,    padding_value=A_		,    **A_    )

          snake_case								=			feature_size
          snake_case								=			sampling_rate
          snake_case								=			padding_value
          snake_case								=			hop_length
          snake_case								=			win_length
          snake_case								=			frame_signal_scale
          snake_case								=			preemphasis_coeff
          snake_case								=			mel_floor
          snake_case								=			normalize_means
          snake_case								=			normalize_vars
          snake_case								=			win_function
          snake_case								=			return_attention_mask

          snake_case								=			win_length * sampling_rate // 10_00
          snake_case								=			hop_length * sampling_rate // 10_00

          snake_case								=			optimal_fft_length(self.sample_size    )
          snake_case								=			(self.n_fft // 2) + 1

    def 		snake_case (				self		,    lowerCAmelCase    ):






          """simple docstring"""

          if self.win_function == "hamming_window":
                snake_case								=			window_function(window_length=self.sample_size		,    name=self.win_function		,    periodic=A_    )
          else:
                snake_case								=			window_function(window_length=self.sample_size		,    name=self.win_function    )

          snake_case								=			mel_filter_bank(
              num_frequency_bins=self.n_freqs		,    num_mel_filters=self.feature_size		,    min_frequency=0.0		,    max_frequency=self.sampling_rate / 2.0		,    sampling_rate=self.sampling_rate		,    )

          snake_case								=			spectrogram(
              one_waveform * self.frame_signal_scale		,    window=A_		,    frame_length=self.sample_size		,    hop_length=self.sample_stride		,    fft_length=self.n_fft		,    center=A_		,    preemphasis=self.preemphasis_coeff		,    mel_filters=A_		,    mel_floor=self.mel_floor		,    log_mel='log'		,    )
          return msfc_features.T

    def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






          """simple docstring"""

          if self.normalize_means:
                snake_case								=			x[:input_length].mean(axis=0    )
                snake_case								=			np.subtract(A_		,    A_    )
          if self.normalize_vars:
                snake_case								=			x[:input_length].std(axis=0    )
                snake_case								=			np.divide(A_		,    A_    )

          if input_length < x.shape[0]:
                snake_case								=			padding_value

          # make sure array is in float32
          snake_case								=			x.astype(np.floataa    )

          return x

    def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None    ):






          """simple docstring"""

          snake_case								=			attention_mask.sum(-1    ) if attention_mask is not None else [x.shape[0] for x in input_features]
          return [self._normalize_one(A_		,    A_		,    self.padding_value    ) for x, n in zip(A_		,    A_    )]

    def __call__(				self		,    lowerCAmelCase		,    lowerCAmelCase = False		,    lowerCAmelCase = None		,    lowerCAmelCase = False		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    **lowerCAmelCase		,    ):






          """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.'    )

          snake_case								=			isinstance(A_		,    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}"""    )
          snake_case								=			is_batched_numpy or (
              isinstance(A_		,    (list, tuple)    ) and (isinstance(raw_speech[0]		,    (np.ndarray, tuple, list)    ))
          )

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

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

          # extract fbank features
          snake_case								=			[self._extract_mfsc_features(A_    ) for one_waveform in raw_speech]

          # convert into correct format for padding
          snake_case								=			BatchFeature({'input_features': features}    )

          snake_case								=			self.pad(
              A_		,    padding=A_		,    max_length=A_		,    truncation=A_		,    pad_to_multiple_of=A_		,    return_attention_mask=A_		,    **A_		,    )
          # make sure list is in array format
          snake_case								=			padded_inputs.get('input_features'    )
          if isinstance(input_features[0]		,    A_    ):
                snake_case								=			[np.asarray(A_		,    dtype=np.floataa    ) for feature in input_features]

          snake_case								=			padded_inputs.get('attention_mask'    )
          if attention_mask is not None:
                snake_case								=			[np.asarray(A_		,    dtype=np.intaa    ) for array in attention_mask]

          if self.normalize_means or self.normalize_vars:
                snake_case								=			(
                    np.array(A_		,    dtype=np.intaa    )
                    if self._get_padding_strategies(A_		,    max_length=A_    ) is not PaddingStrategy.DO_NOT_PAD
                    and padding
                    else None
                )
                snake_case								=			self.normalize(
                    padded_inputs['input_features']		,    attention_mask=A_    )

          if return_tensors is not None:
                snake_case								=			padded_inputs.convert_to_tensors(A_    )

          return padded_inputs



"""simple docstring"""
from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map





def    lowerCAmelCase__    (			_UpperCamelCase       :				Dict[str, torch.Tensor]					)					->				Dict[str, torch.Tensor]:





						"""simple docstring"""






						snake_case								=			[]
						snake_case								=			[]
						snake_case								=			[]

						for rt in rc.restypes:
												snake_case								=			rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
												restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]					)
												snake_case								=			{name: i for i, name in enumerate(_UpperCamelCase					)}
												restype_atomaa_to_atomaa_list.append(
												    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]					)

												restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]					)

						# Add dummy mapping for restype 'UNK'
						restype_atomaa_to_atomaa_list.append([0] * 1_4					)
						restype_atomaa_to_atomaa_list.append([0] * 3_7					)
						restype_atomaa_mask_list.append([0.0] * 1_4					)

						snake_case								=			torch.tensor(
						    _UpperCamelCase ,			dtype=torch.intaa ,			device=protein['aatype'].device ,			)
						snake_case								=			torch.tensor(
						    _UpperCamelCase ,			dtype=torch.intaa ,			device=protein['aatype'].device ,			)
						snake_case								=			torch.tensor(
						    _UpperCamelCase ,			dtype=torch.floataa ,			device=protein['aatype'].device ,			)
						snake_case								=			protein['aatype'].to(torch.long					)

						# create the mapping for (residx, atom14) --> atom37, i.e. an array
						# with shape (num_res, 14) containing the atom37 indices for this protein
						snake_case								=			restype_atomaa_to_atomaa[protein_aatype]
						snake_case								=			restype_atomaa_mask[protein_aatype]

						snake_case								=			residx_atomaa_mask
						snake_case								=			residx_atomaa_to_atomaa.long()

						# create the gather indices for mapping back
						snake_case								=			restype_atomaa_to_atomaa[protein_aatype]
						snake_case								=			residx_atomaa_to_atomaa.long()

						# create the corresponding mask
						snake_case								=			torch.zeros([2_1, 3_7] ,			dtype=torch.floataa ,			device=protein['aatype'].device					)
						for restype, restype_letter in enumerate(rc.restypes					):
												snake_case								=			rc.restype_atoa[restype_letter]
												snake_case								=			rc.residue_atoms[restype_name]
												for atom_name in atom_names:
																		snake_case								=			rc.atom_order[atom_name]
																		snake_case								=			1

						snake_case								=			restype_atomaa_mask[protein_aatype]
						snake_case								=			residx_atomaa_mask

						return protein





def    lowerCAmelCase__    (			_UpperCamelCase       :				Dict[str, torch.Tensor]					)					->				Dict[str, np.ndarray]:





						"""simple docstring"""






						snake_case								=			tree_map(lambda _UpperCamelCase					: torch.tensor(_UpperCamelCase ,			device=batch['aatype'].device					) ,			_UpperCamelCase ,			np.ndarray					)
						snake_case								=			tensor_tree_map(lambda _UpperCamelCase					: np.array(_UpperCamelCase					) ,			make_atomaa_masks(_UpperCamelCase					)					)
						return out


