


from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


UpperCAmelCase__   	=							logging.get_logger(__name__)







class       lowerCamelCase__   (       lowerCAmelCase):





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     **UpperCAmelCase       )     ->   Optional[Any]:
      _lowercase							      =feature_size
      _lowercase							      =sampling_rate
      _lowercase							      =padding_value

      _lowercase							      =kwargs.pop('''padding_side'''		,     '''right'''       )
      _lowercase							      =kwargs.pop('''return_attention_mask'''		,     UpperCAmelCase       )

      super().__init__(**UpperCAmelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = True		,     UpperCAmelCase = None		,     UpperCAmelCase = False		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     )     ->   BatchFeature:
      # If we have a list of dicts, let's convert it in a dict of lists
      # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
      if isinstance(UpperCAmelCase		,     (list, tuple)       ) and isinstance(processed_features[0]		,     (dict, BatchFeature)       ):
       _lowercase							      ={
           key: [example[key] for example in processed_features] for key in processed_features[0].keys()
       }

      # The model's main input name, usually `input_values`, has be passed for padding
      if self.model_input_names[0] not in processed_features:
       raise ValueError(
           '''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
           f" to this method that includes {self.model_input_names[0]}, but you provided"
           f" {list(processed_features.keys()       )}"       )

      _lowercase							      =processed_features[self.model_input_names[0]]
      _lowercase							      =(
          return_attention_mask if return_attention_mask is not None else self.return_attention_mask
      )

      if len(UpperCAmelCase       ) == 0:
       if return_attention_mask:
        _lowercase							      =[]
       return processed_features

      # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
      # and rebuild them afterwards if no return_tensors is specified
      # Note that we lose the specific device the tensor may be on for PyTorch

      _lowercase							      =required_input[0]
      if isinstance(UpperCAmelCase		,     (list, tuple)       ):
       # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
       _lowercase							      =0
       while len(required_input[index]       ) == 0:
        index += 1
       if index < len(UpperCAmelCase       ):
        _lowercase							      =required_input[index][0]

      if return_tensors is None:
       if is_tf_tensor(UpperCAmelCase       ):
        _lowercase							      ='''tf'''
       elif is_torch_tensor(UpperCAmelCase       ):
        _lowercase							      ='''pt'''
       elif isinstance(UpperCAmelCase		,     (int, float, list, tuple, np.ndarray)       ):
        _lowercase							      ='''np'''
       else:
        raise ValueError(
            f"type of {first_element} unknown: {type(UpperCAmelCase       )}. "
            '''Should be one of a python, numpy, pytorch or tensorflow object.'''       )

      for key, value in processed_features.items():
       if isinstance(value[0]		,     (int, float)       ):
        _lowercase							      =to_numpy(UpperCAmelCase       )
       else:
        _lowercase							      =[to_numpy(UpperCAmelCase       ) for v in value]

        # Convert padding_strategy in PaddingStrategy
      _lowercase							      =self._get_padding_strategies(padding=UpperCAmelCase		,     max_length=UpperCAmelCase       )

      _lowercase							      =processed_features[self.model_input_names[0]]

      _lowercase							      =len(UpperCAmelCase       )
      if not all(len(UpperCAmelCase       ) == batch_size for v in processed_features.values()       ):
       raise ValueError('''Some items in the output dictionary have a different batch size than others.'''       )

      _lowercase							      =[]
      for i in range(UpperCAmelCase       ):
       _lowercase							      ={k: v[i] for k, v in processed_features.items()}
       # truncation
       _lowercase							      =self._truncate(
           UpperCAmelCase		,     max_length=UpperCAmelCase		,     pad_to_multiple_of=UpperCAmelCase		,     truncation=UpperCAmelCase		,     )
       truncated_inputs.append(UpperCAmelCase       )

      if padding_strategy == PaddingStrategy.LONGEST:
       # make sure that `max_length` cannot be longer than the longest truncated length
       _lowercase							      =max(len(input_slice[self.model_input_names[0]]       ) for input_slice in truncated_inputs       )
       _lowercase							      =PaddingStrategy.MAX_LENGTH

      _lowercase							      ={}
      for i in range(UpperCAmelCase       ):
       # padding
       _lowercase							      =self._pad(
           truncated_inputs[i]		,     max_length=UpperCAmelCase		,     padding_strategy=UpperCAmelCase		,     pad_to_multiple_of=UpperCAmelCase		,     return_attention_mask=UpperCAmelCase		,     )

       for key, value in outputs.items():
        if key not in batch_outputs:
         _lowercase							      =[]
        if value.dtype is np.dtype(np.floataa       ):
         _lowercase							      =value.astype(np.floataa       )
        batch_outputs[key].append(UpperCAmelCase       )

      return BatchFeature(UpperCAmelCase		,     tensor_type=UpperCAmelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = None		,     UpperCAmelCase = PaddingStrategy.DO_NOT_PAD		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     )     ->   dict:
      _lowercase							      =processed_features[self.model_input_names[0]]

      if padding_strategy == PaddingStrategy.LONGEST:
       _lowercase							      =len(UpperCAmelCase       )

      if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
       _lowercase							      =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      _lowercase							      =padding_strategy != PaddingStrategy.DO_NOT_PAD and len(UpperCAmelCase       ) < max_length

      if return_attention_mask and "attention_mask" not in processed_features:
       _lowercase							      =np.ones(len(UpperCAmelCase       )		,     dtype=np.intaa       )

      if needs_to_be_padded:
       _lowercase							      =max_length - len(UpperCAmelCase       )
       if self.padding_side == "right":
        if return_attention_mask:
         _lowercase							      =np.pad(
             processed_features['''attention_mask''']		,     (0, difference)       )
        _lowercase							      =((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
        _lowercase							      =np.pad(
            UpperCAmelCase		,     UpperCAmelCase		,     '''constant'''		,     constant_values=self.padding_value       )
       elif self.padding_side == "left":
        if return_attention_mask:
         _lowercase							      =np.pad(
             processed_features['''attention_mask''']		,     (difference, 0)       )
        _lowercase							      =((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
        _lowercase							      =np.pad(
            UpperCAmelCase		,     UpperCAmelCase		,     '''constant'''		,     constant_values=self.padding_value       )
       else:
        raise ValueError('''Invalid padding strategy:''' + str(self.padding_side       )       )

      return processed_features





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     )     ->   List[str]:
      if not truncation:
       return processed_features
      elif truncation and max_length is None:
       raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.'''       )

      _lowercase							      =processed_features[self.model_input_names[0]]

      # find `max_length` that fits `pad_to_multiple_of`
      if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
       _lowercase							      =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      _lowercase							      =len(UpperCAmelCase       ) > max_length

      if needs_to_be_truncated:
       _lowercase							      =processed_features[self.model_input_names[0]][:max_length]
       if "attention_mask" in processed_features:
        _lowercase							      =processed_features['''attention_mask'''][:max_length]

      return processed_features





     def      __A					(self		,     UpperCAmelCase=False		,     UpperCAmelCase=None       )     ->   Dict:

      # Get padding strategy
      if padding is not False:
       if padding is True:
        _lowercase							      =PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
       elif not isinstance(UpperCAmelCase		,     UpperCAmelCase       ):
        _lowercase							      =PaddingStrategy(UpperCAmelCase       )
       elif isinstance(UpperCAmelCase		,     UpperCAmelCase       ):
        _lowercase							      =padding
      else:
       _lowercase							      =PaddingStrategy.DO_NOT_PAD

      # Set max length if needed
      if max_length is None:
       if padding_strategy == PaddingStrategy.MAX_LENGTH:
        raise ValueError(
            f"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"       )

        # Test if we have a padding value
      if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
       raise ValueError(
           '''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
           ''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'''       )

      return padding_strategy





def 				UpperCAmelCase_					(							__snake_case						)     ->			str:







 """simple docstring"""


 _lowercase							      =0

 # if input_string is "aba" than new_input_string become "a|b|a"
 _lowercase							      =''''''
 _lowercase							      =''''''

 # append each character + "|" in new_string for range(0, length-1)
 for i in input_string[: len(__snake_case						) - 1]:
  new_input_string += i + "|"
 # append last character
 new_input_string += input_string[-1]

 # we will store the starting and ending of previous furthest ending palindromic
 # substring
 _lowercase      ,    _lowercase							      =0, 0

 # length[i] shows the length of palindromic substring with center i
 _lowercase							      =[1 for i in range(len(__snake_case						)						)]

 # for each character in new_string find corresponding palindromic string
 _lowercase							      =0
 for j in range(len(__snake_case						)						):
  _lowercase							      =1 if j > r else min(length[l + r - j] // 2     ,						r - j + 1						)
  while (
      j - k >= 0
      and j + k < len(__snake_case						)
      and new_input_string[k + j] == new_input_string[j - k]
  ):
   k += 1

  _lowercase							      =2 * k - 1

  # does this string is ending after the previously explored end (that is r) ?
  # if yes the update the new r to the last index of this
  if j + k - 1 > r:
   _lowercase							      =j - k + 1  # noqa: E741
   _lowercase							      =j + k - 1

  # update max_length and start position
  if max_length < length[j]:
   _lowercase							      =length[j]
   _lowercase							      =j

    # create that string
 _lowercase							      =new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
 for i in s:
  if i != "|":
   output_string += i

 return output_string


if __name__ == "__main__":
  import doctest

  doctest.testmod()

