





'''simple docstring'''


from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


lowerCAmelCase :					Tuple 							=logging.get_logger(__name__)
def 							UpperCAmelCase_  (						__lowerCamelCase : Union[tf.Tensor, np.ndarray]			):
   if isinstance(__lowerCamelCase     ,np.ndarray			):
      return list(tensor.shape			)

   lowercase_      :Optional[int]         =			tf.shape(__lowerCamelCase			)

   if tensor.shape == tf.TensorShape(__lowerCamelCase			):
      return dynamic

   lowercase_      :Union[str, Any]         =			tensor.shape.as_list()

   return [dynamic[i] if s is None else s for i, s in enumerate(__lowerCamelCase			)]
def 							UpperCAmelCase_  (						__lowerCamelCase : tf.Tensor     ,__lowerCamelCase : Optional[int] = None     ,__lowerCamelCase : Optional[str] = None			):
   return tf.nn.softmax(logits=logits + 1e-9     ,axis=__lowerCamelCase     ,name=__lowerCamelCase			)
def 							UpperCAmelCase_  (						__lowerCamelCase : str     ,__lowerCamelCase : List[Any]     ,__lowerCamelCase : Any     ,__lowerCamelCase : List[str]=1e-5     ,__lowerCamelCase : List[str]=-1			):
   # This is a very simplified functional layernorm, designed to duplicate
   # the functionality of PyTorch nn.functional.layer_norm when this is needed to port
   # models in Transformers.

   if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__lowerCamelCase     ,__lowerCamelCase			):
      raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis."			)

   # Get mean and variance on the axis to be normalized
   lowercase_      ,							lowercase_      :List[str]         =			tf.nn.moments(__lowerCamelCase     ,axes=[axis]     ,keepdims=__lowerCamelCase			)

   if axis != -1:
      # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
      # on every dimension except axis
      lowercase_      :Union[str, Any]         =			[1] * inputs.shape.rank
      lowercase_      :Optional[Any]         =			shape_list(__lowerCamelCase			)[axis]
      lowercase_      :List[str]         =			tf.reshape(__lowerCamelCase     ,__lowerCamelCase			)
      lowercase_      :Dict         =			tf.reshape(__lowerCamelCase     ,__lowerCamelCase			)

   # Compute layer normalization using the batch_normalization
   # function.
   lowercase_      :Union[str, Any]         =			tf.nn.batch_normalization(
       __lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,offset=__lowerCamelCase     ,scale=__lowerCamelCase     ,variance_epsilon=__lowerCamelCase     ,)
   return outputs
def 							UpperCAmelCase_  (						__lowerCamelCase : Optional[int]     ,__lowerCamelCase : Union[str, Any]=0     ,__lowerCamelCase : Dict=-1			):
   # Replicates the behavior of torch.flatten in TF

   # If end_dim or start_dim is negative, count them from the end
   if end_dim < 0:
      end_dim += input.shape.rank
   if start_dim < 0:
      start_dim += input.shape.rank

   if start_dim == end_dim:
      return input

   lowercase_      :Optional[int]         =			tf.shape(__lowerCamelCase			)
   lowercase_      :Optional[int]         =			tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]			)
   lowercase_      :List[str]         =			tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]     ,axis=0			)
   return tf.reshape(__lowerCamelCase     ,__lowerCamelCase			)
def 							UpperCAmelCase_  (						__lowerCamelCase : tf.Tensor			):
   if not isinstance(__lowerCamelCase     ,tf.Tensor			):
      lowercase_      :str         =			tf.convert_to_tensor(__lowerCamelCase			)  # Catches stray NumPy inputs
   if encoder_attention_mask.shape.rank == 3:
      lowercase_      :List[Any]         =			encoder_attention_mask[:, None, :, :]
   if encoder_attention_mask.shape.rank == 2:
      lowercase_      :Optional[int]         =			encoder_attention_mask[:, None, None, :]
   # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
   # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
   # /transformer/transformer_layers.py#L270
   # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
   # encoder_extended_attention_mask.transpose(-1, -2))
   lowercase_      :str         =			(
       tf.cast(1     ,encoder_attention_mask.dtype			) - encoder_extended_attention_mask
   ) * encoder_extended_attention_mask.dtype.min

   return encoder_extended_attention_mask
def 							UpperCAmelCase_  (						__lowerCamelCase : tf.Tensor     ,__lowerCamelCase : int     ,__lowerCamelCase : str = "input_ids"			):
   tf.debugging.assert_less(
       __lowerCamelCase     ,tf.cast(__lowerCamelCase     ,dtype=tensor.dtype			)     ,message=(
           F'The maximum value of {tensor_name} ({tf.math.reduce_max(__lowerCamelCase			)}) must be smaller than the embedding '
           F'layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'
       )     ,)
def 							UpperCAmelCase_  (						__lowerCamelCase : List[str]     ,__lowerCamelCase : Tuple     ,__lowerCamelCase : Dict			):
   lowercase_      :int         =			6_45_12
   # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
   # because in that case even chunking the array would not make the saving
   # possible.
   lowercase_      :Union[str, Any]         =			[x for x in data if len(__lowerCamelCase			) > HDF5_OBJECT_HEADER_LIMIT]

   # Expecting this to never be true.
   if bad_attributes:
      raise RuntimeError(
          "The following attributes cannot be saved to HDF5 file because "
          F'they are larger than {HDF5_OBJECT_HEADER_LIMIT} '
          F'bytes: {bad_attributes}'			)

   lowercase_      :Union[str, Any]         =			np.asarray(__lowerCamelCase			)

   lowercase_      :Optional[int]         =			1
   lowercase_      :int         =			np.array_split(__lowerCamelCase     ,__lowerCamelCase			)

   # This will never loop forever thanks to the test above.
   while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data			):
      num_chunks += 1
      lowercase_      :List[Any]         =			np.array_split(__lowerCamelCase     ,__lowerCamelCase			)

   if num_chunks > 1:
      for chunk_id, chunk_data in enumerate(__lowerCamelCase			):
         lowercase_      :int         =			chunk_data
   else:
      lowercase_      :Tuple         =			data
def 							UpperCAmelCase_  (						__lowerCamelCase : str     ,__lowerCamelCase : Tuple			):
   if name in group.attrs:
      lowercase_      :Optional[Any]         =			[n.decode("utf8"			) if hasattr(__lowerCamelCase     ,"decode"			) else n for n in group.attrs[name]]
   else:
      lowercase_      :List[str]         =			[]
      lowercase_      :str         =			0
      while "%s%d" % (name, chunk_id) in group.attrs:
         data.extend(
             [n.decode("utf8"			) if hasattr(__lowerCamelCase     ,"decode"			) else n for n in group.attrs["%s%d" % (name, chunk_id)]]			)
         chunk_id += 1
   return data


def 							UpperCAmelCase_  (						__lowerCamelCase : str			):

   def _expand_single_ad_tensor(__lowerCamelCase : Tuple			):
      if isinstance(__lowerCamelCase     ,tf.Tensor			) and t.shape.rank == 1:
         return tf.expand_dims(__lowerCamelCase     ,axis=-1			)
      return t

   return tf.nest.map_structure(_expand_single_ad_tensor     ,__lowerCamelCase			)









'''simple docstring'''


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


lowerCAmelCase :					Optional[int] 							=logging.get_logger(__name__)
class 				a_       (    _lowerCAmelCase							):
     __A								=      ["input_features"]





     def __init__(			self		: Any     ,				lowercase		: Tuple=80     ,				lowercase		: Optional[int]=16_000     ,				lowercase		: Optional[Any]=160     ,				lowercase		: Optional[int]=30     ,				lowercase		: List[Any]=400     ,				lowercase		: Dict=0.0     ,				lowercase		: Tuple=False     ,				**lowercase		: Optional[int]     ,				):





        """simple docstring"""




        super().__init__(
            feature_size=lowercase     ,				sampling_rate=lowercase     ,				padding_value=lowercase     ,				return_attention_mask=lowercase     ,				**lowercase     ,				)
        lowercase_      :Optional[int]         =			n_fft
        lowercase_      :List[Any]         =			hop_length
        lowercase_      :Tuple         =			chunk_length
        lowercase_      :List[str]         =			chunk_length * sampling_rate
        lowercase_      :Optional[Any]         =			self.n_samples // hop_length
        lowercase_      :Any         =			sampling_rate
        lowercase_      :List[Any]         =			mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2     ,				num_mel_filters=lowercase     ,				min_frequency=0.0     ,				max_frequency=80_00.0     ,				sampling_rate=lowercase     ,				norm="slaney"     ,				mel_scale="slaney"     ,				)





     def 							lowercase__				(			self		: str     ,				lowercase		: np.array						):





        """simple docstring"""




        lowercase_      :Any         =			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"     ,				)
        lowercase_      :Any         =			log_spec[:, :-1]
        lowercase_      :List[Any]         =			np.maximum(lowercase     ,				log_spec.max() - 8.0						)
        lowercase_      :Dict         =			(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						):





        """simple docstring"""




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

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

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

        return normed_input_values





     def __call__(			self		: Tuple     ,				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]     ,				):





        """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.__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."						)

        lowercase_      :List[str]         =			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}'						)
        lowercase_      :Optional[Any]         =			is_batched_numpy or (
            isinstance(lowercase     ,				(list, tuple)						) and (isinstance(raw_speech[0]     ,				(np.ndarray, tuple, list)						))
        )

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

        # always return batch
        if not is_batched:
           lowercase_      :Optional[int]         =			[np.asarray([raw_speech]						).T]

        lowercase_      :int         =			BatchFeature({"input_features": raw_speech}						)

        # convert into correct format for padding

        lowercase_      :Tuple         =			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:
           lowercase_      :Union[str, Any]         =			self.zero_mean_unit_var_norm(
               padded_inputs["input_features"]     ,				attention_mask=padded_inputs["attention_mask"]     ,				padding_value=self.padding_value     ,				)
           lowercase_      :List[Any]         =			np.stack(padded_inputs["input_features"]     ,				axis=0						)

        # make sure list is in array format
        lowercase_      :Union[str, Any]         =			padded_inputs.get("input_features"						).transpose(2     ,				0     ,				1						)

        lowercase_      :List[str]         =			[self._np_extract_fbank_features(lowercase						) for waveform in input_features[0]]

        if isinstance(input_features[0]     ,				lowercase						):
           lowercase_      :Tuple         =			[np.asarray(lowercase     ,				dtype=np.floataa						) for feature in input_features]
        else:
           lowercase_      :Union[str, Any]         =			input_features

        if return_attention_mask:
           # rescale from sample (48000) to feature (3000)
           lowercase_      :Dict         =			padded_inputs["attention_mask"][:, :: self.hop_length]

        if return_tensors is not None:
           lowercase_      :Tuple         =			padded_inputs.convert_to_tensors(lowercase						)

        return padded_inputs







     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			copy.deepcopy(self.__dict__						)
        lowercase_      :List[str]         =			self.__class__.__name__
        if "mel_filters" in output:
           del output["mel_filters"]
        return output


