"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_:					List[Any]     =					logging.get_logger(__name__)

lowerCAmelCase_:					int     =					{
    "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
    "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}






class    a__    (   _a	):
   snake_case_							=      "markuplm"


   def __init__(     self,				_UpperCAmelCase=3_0522,				_UpperCAmelCase=768,				_UpperCAmelCase=12,				_UpperCAmelCase=12,				_UpperCAmelCase=3072,				_UpperCAmelCase="gelu",				_UpperCAmelCase=0.1,				_UpperCAmelCase=0.1,				_UpperCAmelCase=512,				_UpperCAmelCase=2,				_UpperCAmelCase=0.02,				_UpperCAmelCase=1E-12,				_UpperCAmelCase=0,				_UpperCAmelCase=0,				_UpperCAmelCase=2,				_UpperCAmelCase=256,				_UpperCAmelCase=1024,				_UpperCAmelCase=216,				_UpperCAmelCase=1001,				_UpperCAmelCase=32,				_UpperCAmelCase=50,				_UpperCAmelCase="absolute",				_UpperCAmelCase=True,				_UpperCAmelCase=None,				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(
            pad_token_id=_UpperCAmelCase,				bos_token_id=_UpperCAmelCase,				eos_token_id=_UpperCAmelCase,				**_UpperCAmelCase,				)
        lowercase__				 =							vocab_size
        lowercase__				 =							hidden_size
        lowercase__				 =							num_hidden_layers
        lowercase__				 =							num_attention_heads
        lowercase__				 =							hidden_act
        lowercase__				 =							intermediate_size
        lowercase__				 =							hidden_dropout_prob
        lowercase__				 =							attention_probs_dropout_prob
        lowercase__				 =							max_position_embeddings
        lowercase__				 =							type_vocab_size
        lowercase__				 =							initializer_range
        lowercase__				 =							layer_norm_eps
        lowercase__				 =							position_embedding_type
        lowercase__				 =							use_cache
        lowercase__				 =							classifier_dropout
        # additional properties
        lowercase__				 =							max_depth
        lowercase__				 =							max_xpath_tag_unit_embeddings
        lowercase__				 =							max_xpath_subs_unit_embeddings
        lowercase__				 =							tag_pad_id
        lowercase__				 =							subs_pad_id
        lowercase__				 =							xpath_unit_hidden_size






"""simple docstring"""


from math import ceil
from typing import List, Optional, Union

import numpy as np

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


lowerCAmelCase_:					Union[str, Any]     =					logging.get_logger(__name__)






class    a__    (   _a	):
   snake_case_							=      ["audio_values", "audio_mask"]


   def __init__(     self,				_UpperCAmelCase=2048,				_UpperCAmelCase=1,				_UpperCAmelCase=[16, 16],				_UpperCAmelCase=128,				_UpperCAmelCase=4_4100,				_UpperCAmelCase=86,				_UpperCAmelCase=2048,				_UpperCAmelCase=0.0,				**_UpperCAmelCase,				):
        '''simple docstring'''



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

        lowercase__				 =							spectrogram_length
        lowercase__				 =							num_channels
        lowercase__				 =							patch_size
        lowercase__				 =							feature_size // self.patch_size[1]
        lowercase__				 =							n_fft
        lowercase__				 =							sampling_rate // hop_length_to_sampling_rate
        lowercase__				 =							sampling_rate
        lowercase__				 =							padding_value
        lowercase__				 =							mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2,				num_mel_filters=_UpperCAmelCase,				min_frequency=0.0,				max_frequency=22_050.0,				sampling_rate=_UpperCAmelCase,				norm="slaney",				mel_scale="slaney",				).T


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							spectrogram(
            _UpperCAmelCase,				window_function(self.n_fft,				"hann"  ),				frame_length=self.n_fft,				hop_length=self.hop_length,				power=2.0,				mel_filters=self.mel_filters.T,				log_mel="dB",				db_range=80.0,				)
        lowercase__				 =							log_spec[:, :-1]
        lowercase__				 =							log_spec - 20.0
        lowercase__				 =							np.clip(log_spec / 40.0,				-2.0,				0.0  ) + 1.0
        return log_spec


   def __call__(     self,				_UpperCAmelCase,				_UpperCAmelCase = None,				_UpperCAmelCase = True,				_UpperCAmelCase = None,				_UpperCAmelCase = False,				_UpperCAmelCase = False,				**_UpperCAmelCase,				):
        '''simple docstring'''



        if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                  raise ValueError(
                      "This feature extractor is set to support sampling rate"
                      F''' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'''
                      F''' with {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."  )

        lowercase__				 =							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}'''  )
        lowercase__				 =							is_batched_numpy or (
            isinstance(_UpperCAmelCase,				(list, tuple)  ) and (isinstance(raw_speech[0],				(np.ndarray, tuple, list)  ))
        )
        if is_batched:
             lowercase__				 =							[np.asarray([speech],				dtype=np.floataa  ).T for speech in raw_speech]
        elif not is_batched and not isinstance(_UpperCAmelCase,				np.ndarray  ):
             lowercase__				 =							np.asarray(_UpperCAmelCase,				dtype=np.floataa  )
        elif isinstance(_UpperCAmelCase,				np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
             lowercase__				 =							raw_speech.astype(np.floataa  )
        # always return batch
        if not is_batched:
             lowercase__				 =							[np.asarray([raw_speech]  ).T]

        # Convert audio signals to log mel spectrograms, truncate by time axis
        lowercase__				 =							[
            self._np_extract_fbank_features(waveform.squeeze()  ).T[: self.spectrogram_length] for waveform in raw_speech
        ]
        if isinstance(audio_features[0],				_UpperCAmelCase  ):
             lowercase__				 =							[np.asarray(_UpperCAmelCase,				dtype=np.floataa  ) for feature in audio_features]

        # Create audio attention mask
        lowercase__				 =							max(
            [ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len for feature in audio_features]  )  # The maximum number of audio patches in a batch
        if return_attention_mask:
             lowercase__				 =							[
                 (ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len) * [1]
                 + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len) * [0]
                 for feature in audio_features
             ]
             lowercase__				 =							np.array(_UpperCAmelCase  ).astype(np.floataa  )

        # convert into correct format for padding
        lowercase__				 =							max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
        lowercase__				 =							np.ones([len(_UpperCAmelCase  ), 1, max_time_len, self.feature_size]  ).astype(np.floataa  )
        lowercase__				 =							padded_audio_features * self.padding_value
        for i in range(len(_UpperCAmelCase  )  ):
             lowercase__				 =							audio_features[i]
             lowercase__				 =							feature

        # return as BatchFeature
        if return_attention_mask:
             lowercase__				 =							{"audio_values": padded_audio_features, "audio_mask": audio_mask}
        else:
             lowercase__				 =							{"audio_values": padded_audio_features}

        lowercase__				 =							BatchFeature(data=_UpperCAmelCase,				tensor_type=_UpperCAmelCase  )
        return encoded_inputs




