"""simple docstring"""


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        a				(		lowerCAmelCase_   ):


    def __init__(					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  float   ,				**__lowerCAmelCase	:  Optional[int]					):
      _UpperCAmelCase						 =      feature_size
      _UpperCAmelCase						 =      sampling_rate
      _UpperCAmelCase						 =      padding_value

      _UpperCAmelCase						 =      kwargs.pop("""padding_side"""   ,				"""right"""					)
      _UpperCAmelCase						 =      kwargs.pop("""return_attention_mask"""   ,				__lowerCAmelCase					)

      super().__init__(**__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  Union[
            BatchFeature,
            List[BatchFeature],
            Dict[str, BatchFeature],
            Dict[str, List[BatchFeature]],
            List[Dict[str, BatchFeature]],
        ]   ,				__lowerCAmelCase	:  Union[bool, str, PaddingStrategy] = True   ,				__lowerCAmelCase	:  Optional[int] = None   ,				__lowerCAmelCase	:  bool = False   ,				__lowerCAmelCase	:  Optional[int] = None   ,				__lowerCAmelCase	:  Optional[bool] = None   ,				__lowerCAmelCase	:  Optional[Union[str, TensorType]] = None   ,				):
      # 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(__lowerCAmelCase   ,				(list, tuple)					) and isinstance(processed_features[0]   ,				(dict, BatchFeature)					):
        _UpperCAmelCase						 =      {
            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()					)}'''					)

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

      if len(__lowerCAmelCase					) == 0:
        if return_attention_mask:
          _UpperCAmelCase						 =      []
        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

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

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

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

        # Convert padding_strategy in PaddingStrategy
      _UpperCAmelCase						 =      self._get_padding_strategies(padding=__lowerCAmelCase   ,				max_length=__lowerCAmelCase					)

      _UpperCAmelCase						 =      processed_features[self.model_input_names[0]]

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

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

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

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

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

      return BatchFeature(__lowerCAmelCase   ,				tensor_type=__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  Union[Dict[str, np.ndarray], BatchFeature]   ,				__lowerCAmelCase	:  Optional[int] = None   ,				__lowerCAmelCase	:  PaddingStrategy = PaddingStrategy.DO_NOT_PAD   ,				__lowerCAmelCase	:  Optional[int] = None   ,				__lowerCAmelCase	:  Optional[bool] = None   ,				):
      _UpperCAmelCase						 =      processed_features[self.model_input_names[0]]

      if padding_strategy == PaddingStrategy.LONGEST:
        _UpperCAmelCase						 =      len(__lowerCAmelCase					)

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

      _UpperCAmelCase						 =      padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__lowerCAmelCase					) < max_length

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

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

      return processed_features


    def      lowerCAmelCase_  (					self	:  Optional[Any]   ,				__lowerCAmelCase	:  Union[Dict[str, np.ndarray], BatchFeature]   ,				__lowerCAmelCase	:  Optional[int] = None   ,				__lowerCAmelCase	:  Optional[int] = None   ,				__lowerCAmelCase	:  Optional[bool] = None   ,				):
      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."""					)

      _UpperCAmelCase						 =      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):
        _UpperCAmelCase						 =      ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      _UpperCAmelCase						 =      len(__lowerCAmelCase					) > max_length

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

      return processed_features


    def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  List[Any]=False   ,				__lowerCAmelCase	:  List[str]=None					):

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

"""simple docstring"""


def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      abs(lowercase		)
  _UpperCAmelCase						 =      0
  while n > 0:
    res += n % 10
    n //= 10
  return res






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      abs(lowercase		)
  return n if n < 10 else n % 10 + sum_of_digits(n // 10		)






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return sum(int(lowercase		) for c in str(abs(lowercase		)		)		)






def     __UpperCAmelCase					(			):







  """simple docstring"""
  from collections.abc import Callable
  from timeit import timeit

  def benchmark_a_function(lowercase   ,lowercase		) -> None:
    _UpperCAmelCase						 =      f'''{func.__name__}({value})'''
    _UpperCAmelCase						 =      timeit(f'''__main__.{call}'''   ,setup="""import __main__"""		)
    print(f'''{call:56} = {func(lowercase		)} -- {timing:.4f} seconds'''		)

  for value in (26_21_44, 11_25_89_99_06_84_26_24, 1_26_76_50_60_02_28_22_94_01_49_67_03_20_53_76):
    for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
      benchmark_a_function(lowercase   ,lowercase		)
    print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()
