"""simple docstring"""
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


lowercase__   			=      logging.get_logger(__name__)




class   __snake_case			(	__lowerCAmelCase							):
      a__								=						["""input_values""", """attention_mask"""]



      def __init__(		self       ,     lowercase=1       ,     lowercase=1_60_00       ,     lowercase=0.0       ,     lowercase=False       ,     lowercase=True       ,     **lowercase       ,     )      ->     List[Any]:





        '''simple docstring'''





        super().__init__(feature_size=lowercase       ,     sampling_rate=lowercase       ,     padding_value=lowercase       ,     **lowercase)
        a__:	int     				=      return_attention_mask
        a__:	str     				=      do_normalize



      @staticmethod
      def     lowerCamelCase_  (		lowercase       ,     lowercase       ,     lowercase = 0.0)      ->     List[np.ndarray]:





        '''simple docstring'''





        if attention_mask is not None:
          a__:	List[Any]     				=      np.array(lowercase       ,     np.intaa)
          a__:	int     				=      []

          for vector, length in zip(lowercase       ,     attention_mask.sum(-1)):
            a__:	int     				=      (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(lowercase)
        else:
          a__:	List[str]     				=      [(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__:	Dict     				=      isinstance(lowercase       ,     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__:	Optional[Any]     				=      is_batched_numpy or (
            isinstance(lowercase       ,     (list, tuple)) and (isinstance(raw_speech[0]       ,     (np.ndarray, tuple, list)))
        )

        # always return batch
        if not is_batched:
          a__:	List[str]     				=      [raw_speech]

        # convert into correct format for padding
        a__:	Optional[Any]     				=      BatchFeature({'input_values': raw_speech})

        a__:	List[Any]     				=      self.pad(
            lowercase       ,     padding=lowercase       ,     max_length=lowercase       ,     truncation=lowercase       ,     pad_to_multiple_of=lowercase       ,     return_attention_mask=lowercase       ,     )

        # convert input values to correct format
        a__:	Dict     				=      padded_inputs['input_values']
        if not isinstance(input_values[0]       ,     np.ndarray):
          a__:	Union[str, Any]     				=      [np.asarray(lowercase       ,     dtype=np.floataa) for array in input_values]
        elif (
            not isinstance(lowercase       ,     np.ndarray)
            and isinstance(input_values[0]       ,     np.ndarray)
            and input_values[0].dtype is np.dtype(np.floataa)
        ):
          a__:	Optional[Any]     				=      [array.astype(np.floataa) for array in input_values]
        elif isinstance(lowercase       ,     np.ndarray) and input_values.dtype is np.dtype(np.floataa):
          a__:	List[str]     				=      input_values.astype(np.floataa)

        # convert attention_mask to correct format
        a__:	Dict     				=      padded_inputs.get('attention_mask')
        if attention_mask is not None:
          a__:	Union[str, Any]     				=      [np.asarray(lowercase       ,     dtype=np.intaa) for array in attention_mask]

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

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

        return padded_inputs



'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase       :     Tuple			=    {
    """configuration_markuplm""": ["""MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarkupLMConfig"""],
    """feature_extraction_markuplm""": ["""MarkupLMFeatureExtractor"""],
    """processing_markuplm""": ["""MarkupLMProcessor"""],
    """tokenization_markuplm""": ["""MarkupLMTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase       :     int			=    ["""MarkupLMTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase       :     Any			=    [
          """MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MarkupLMForQuestionAnswering""",
          """MarkupLMForSequenceClassification""",
          """MarkupLMForTokenClassification""",
          """MarkupLMModel""",
          """MarkupLMPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
      from .feature_extraction_markuplm import MarkupLMFeatureExtractor
      from .processing_markuplm import MarkupLMProcessor
      from .tokenization_markuplm import MarkupLMTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_markuplm_fast import MarkupLMTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_markuplm import (
                MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                MarkupLMForQuestionAnswering,
                MarkupLMForSequenceClassification,
                MarkupLMForTokenClassification,
                MarkupLMModel,
                MarkupLMPreTrainedModel,
            )


else:
      import sys

      UpperCamelCase       :     Dict			=    _LazyModule(__name__, globals()["""__file__"""], _import_structure)

