


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

import numpy as np

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


a_ :Any  							=			logging.get_logger(__name__)

class 							lowercase  (						_UpperCAmelCase			):
     lowerCamelCase			:			Optional[Any]      = ['''input_features''']







     def __init__(						self :						List[Any]   ,   _lowercase :						Tuple=80   ,   _lowercase :						Optional[Any]=1_60_00   ,   _lowercase :						int=1_60   ,   _lowercase :						Dict=30   ,   _lowercase :						str=4_00   ,   _lowercase :						Tuple=0.0   ,   _lowercase :						Dict=False   ,   **_lowercase :						Tuple   ,   ):
           super().__init__(
               feature_size=_lowercase   ,   sampling_rate=_lowercase   ,   padding_value=_lowercase   ,   return_attention_mask=_lowercase   ,   **_lowercase   ,   )
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		n_fft
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		hop_length
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		chunk_length
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		chunk_length * sampling_rate
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		self.n_samples // hop_length
           SCREAMING_SNAKE_CASE__       :  int						     =		sampling_rate
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		mel_filter_bank(
               num_frequency_bins=1 + n_fft // 2   ,   num_mel_filters=_lowercase   ,   min_frequency=0.0   ,   max_frequency=8000.0   ,   sampling_rate=_lowercase   ,   norm='''slaney'''   ,   mel_scale='''slaney'''   ,   )







     def 							lowercase__       (						self :						int   ,   _lowercase :						np.array      ):
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		spectrogram(
               _lowercase   ,   window_function(self.n_fft   ,   '''hann'''      )   ,   frame_length=self.n_fft   ,   hop_length=self.hop_length   ,   power=2.0   ,   mel_filters=self.mel_filters   ,   log_mel='''log10'''   ,   )
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		log_spec[:, :-1]
           SCREAMING_SNAKE_CASE__       :  Any						     =		np.maximum(_lowercase   ,   log_spec.max() - 8.0      )
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		(log_spec + 4.0) / 4.0
           return log_spec







     @staticmethod
     # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
     def 							lowercase__       (						_lowercase :						List[np.ndarray]   ,   _lowercase :						List[np.ndarray]   ,   _lowercase :						float = 0.0      ):
           if attention_mask is not None:
                 SCREAMING_SNAKE_CASE__       :  Any						     =		np.array(_lowercase   ,   np.intaa      )
                 SCREAMING_SNAKE_CASE__       :  Tuple						     =		[]

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

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

           return normed_input_values







     def __call__(						self :						Union[str, Any]   ,   _lowercase :						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]   ,   _lowercase :						bool = True   ,   _lowercase :						Optional[int] = None   ,   _lowercase :						Optional[Union[str, TensorType]] = None   ,   _lowercase :						Optional[bool] = None   ,   _lowercase :						Optional[str] = "max_length"   ,   _lowercase :						Optional[int] = None   ,   _lowercase :						Optional[int] = None   ,   _lowercase :						Optional[bool] = None   ,   **_lowercase :						Union[str, Any]   ,   ):

           if sampling_rate is not None:
                 if sampling_rate != self.sampling_rate:
                       raise ValueError(
                           f"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
                           f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
                           f""" was sampled 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.'''      )

           SCREAMING_SNAKE_CASE__       :  Any						     =		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}"""      )
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		is_batched_numpy or (
               isinstance(_lowercase   ,   (list, tuple)      ) and (isinstance(raw_speech[0]   ,   (np.ndarray, tuple, list)      ))
           )

           if is_batched:
                 SCREAMING_SNAKE_CASE__       :  Any						     =		[np.asarray([speech]   ,   dtype=np.floataa      ).T for speech in raw_speech]
           elif not is_batched and not isinstance(_lowercase   ,   np.ndarray      ):
                 SCREAMING_SNAKE_CASE__       :  str						     =		np.asarray(_lowercase   ,   dtype=np.floataa      )
           elif isinstance(_lowercase   ,   np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
                 SCREAMING_SNAKE_CASE__       :  str						     =		raw_speech.astype(np.floataa      )

           # always return batch
           if not is_batched:
                 SCREAMING_SNAKE_CASE__       :  List[Any]						     =		[np.asarray([raw_speech]      ).T]

           SCREAMING_SNAKE_CASE__       :  Any						     =		BatchFeature({'''input_features''': raw_speech}      )

           # convert into correct format for padding

           SCREAMING_SNAKE_CASE__       :  Any						     =		self.pad(
               _lowercase   ,   padding=_lowercase   ,   max_length=max_length if max_length else self.n_samples   ,   truncation=_lowercase   ,   pad_to_multiple_of=_lowercase   ,   return_attention_mask=return_attention_mask or do_normalize   ,   )

           # zero-mean and unit-variance normalization
           if do_normalize:
                 SCREAMING_SNAKE_CASE__       :  Dict						     =		self.zero_mean_unit_var_norm(
                     padded_inputs['''input_features''']   ,   attention_mask=padded_inputs['''attention_mask''']   ,   padding_value=self.padding_value   ,   )
                 SCREAMING_SNAKE_CASE__       :  Dict						     =		np.stack(padded_inputs['''input_features''']   ,   axis=0      )

           # make sure list is in array format
           SCREAMING_SNAKE_CASE__       :  int						     =		padded_inputs.get('''input_features'''      ).transpose(2   ,   0   ,   1      )

           SCREAMING_SNAKE_CASE__       :  int						     =		[self._np_extract_fbank_features(_lowercase      ) for waveform in input_features[0]]

           if isinstance(input_features[0]   ,   _lowercase      ):
                 SCREAMING_SNAKE_CASE__       :  int						     =		[np.asarray(_lowercase   ,   dtype=np.floataa      ) for feature in input_features]
           else:
                 SCREAMING_SNAKE_CASE__       :  Any						     =		input_features

           if return_attention_mask:
                 # rescale from sample (48000) to feature (3000)
                 SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		padded_inputs['''attention_mask'''][:, :: self.hop_length]

           if return_tensors is not None:
                 SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		padded_inputs.convert_to_tensors(_lowercase      )

           return padded_inputs







     def 							lowercase__       (						self :						int      ):
           SCREAMING_SNAKE_CASE__       :  Dict						     =		copy.deepcopy(self.__dict__      )
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		self.__class__.__name__
           if "mel_filters" in output:
                 del output["mel_filters"]
           return output












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__magic_name__  :  Any					=       {
    """configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMv2Config"""],
    """processing_layoutlmv2""": ["""LayoutLMv2Processor"""],
    """tokenization_layoutlmv2""": ["""LayoutLMv2Tokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  List[str]					=       ["""LayoutLMv2TokenizerFast"""]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  Any					=       ["""LayoutLMv2FeatureExtractor"""]
      __magic_name__  :  Any					=       ["""LayoutLMv2ImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  Any					=       [
          """LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """LayoutLMv2ForQuestionAnswering""",
          """LayoutLMv2ForSequenceClassification""",
          """LayoutLMv2ForTokenClassification""",
          """LayoutLMv2Layer""",
          """LayoutLMv2Model""",
          """LayoutLMv2PreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
      from .processing_layoutlmva import LayoutLMvaProcessor
      from .tokenization_layoutlmva import LayoutLMvaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_layoutlmva import (
                LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                LayoutLMvaForQuestionAnswering,
                LayoutLMvaForSequenceClassification,
                LayoutLMvaForTokenClassification,
                LayoutLMvaLayer,
                LayoutLMvaModel,
                LayoutLMvaPreTrainedModel,
            )
else:
      import sys

      __magic_name__  :  Union[str, Any]					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

