


"""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


__snake_case             =   logging.get_logger(__name__)







class _lowerCAmelCase    (				snake_case_     ):







   def __init__(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     **UpperCamelCase__						)     ->		Any:

       '''simple docstring'''




       snake_case   :				Dict				 =   feature_size
       snake_case   :				Any				 =   sampling_rate
       snake_case   :				str				 =   padding_value

       snake_case   :				Dict				 =   kwargs.pop("padding_side"						,     "right"						)
       snake_case   :				str				 =   kwargs.pop("return_attention_mask"						,     UpperCamelCase__						)

       super().__init__(**UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__ = True						,     UpperCamelCase__ = None						,     UpperCamelCase__ = False						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     )     ->		BatchFeature:

       '''simple docstring'''




       if isinstance(UpperCamelCase__						,     (list, tuple)						) and isinstance(processed_features[0]						,     (dict, BatchFeature)						):
           snake_case   :				Optional[int]				 =   {
               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()						)}'						)

       snake_case   :				List[str]				 =   processed_features[self.model_input_names[0]]
       snake_case   :				Dict				 =   (
           return_attention_mask if return_attention_mask is not None else self.return_attention_mask
       )

       if len(UpperCamelCase__						) == 0:
           if return_attention_mask:
               snake_case   :				int				 =   []
           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

       snake_case   :				Dict				 =   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.
           snake_case   :				Dict				 =   0
           while len(required_input[index]						) == 0:
               index += 1
           if index < len(UpperCamelCase__						):
               snake_case   :				Optional[int]				 =   required_input[index][0]

       if return_tensors is None:
           if is_tf_tensor(UpperCamelCase__						):
               snake_case   :				str				 =   "tf"
           elif is_torch_tensor(UpperCamelCase__						):
               snake_case   :				int				 =   "pt"
           elif isinstance(UpperCamelCase__						,     (int, float, list, tuple, np.ndarray)						):
               snake_case   :				Dict				 =   "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)						):
               snake_case   :				Union[str, Any]				 =   to_numpy(UpperCamelCase__						)
           else:
               snake_case   :				str				 =   [to_numpy(UpperCamelCase__						) for v in value]

        # Convert padding_strategy in PaddingStrategy
       snake_case   :				List[Any]				 =   self._get_padding_strategies(padding=UpperCamelCase__						,     max_length=UpperCamelCase__						)

       snake_case   :				Union[str, Any]				 =   processed_features[self.model_input_names[0]]

       snake_case   :				Any				 =   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."						)

       snake_case   :				Union[str, Any]				 =   []
       for i in range(UpperCamelCase__						):
           snake_case   :				Union[str, Any]				 =   {k: v[i] for k, v in processed_features.items()}
           # truncation
           snake_case   :				Tuple				 =   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
           snake_case   :				Union[str, Any]				 =   max(len(input_slice[self.model_input_names[0]]						) for input_slice in truncated_inputs						)
           snake_case   :				Union[str, Any]				 =   PaddingStrategy.MAX_LENGTH

       snake_case   :				Union[str, Any]				 =   {}
       for i in range(UpperCamelCase__						):
           # padding
           snake_case   :				Dict				 =   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:
                   snake_case   :				int				 =   []
               if value.dtype is np.dtype(np.floataa						):
                   snake_case   :				List[Any]				 =   value.astype(np.floataa						)
               batch_outputs[key].append(UpperCamelCase__						)

       return BatchFeature(UpperCamelCase__						,     tensor_type=UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__ = None						,     UpperCamelCase__ = PaddingStrategy.DO_NOT_PAD						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     )     ->		dict:

       '''simple docstring'''




       snake_case   :				List[str]				 =   processed_features[self.model_input_names[0]]

       if padding_strategy == PaddingStrategy.LONGEST:
           snake_case   :				Any				 =   len(UpperCamelCase__						)

       if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
           snake_case   :				Optional[Any]				 =   ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

       snake_case   :				Tuple				 =   padding_strategy != PaddingStrategy.DO_NOT_PAD and len(UpperCamelCase__						) < max_length

       if return_attention_mask and "attention_mask" not in processed_features:
           snake_case   :				Dict				 =   np.ones(len(UpperCamelCase__						)						,     dtype=np.intaa						)

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

       return processed_features







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     )     ->		Tuple:

       '''simple docstring'''




       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."						)

       snake_case   :				List[Any]				 =   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):
           snake_case   :				Optional[int]				 =   ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

       snake_case   :				int				 =   len(UpperCamelCase__						) > max_length

       if needs_to_be_truncated:
           snake_case   :				Optional[Any]				 =   processed_features[self.model_input_names[0]][:max_length]
           if "attention_mask" in processed_features:
               snake_case   :				Tuple				 =   processed_features["attention_mask"][:max_length]

       return processed_features







   def 			lowerCamelCase		(     self						,     UpperCamelCase__=False						,     UpperCamelCase__=None						)     ->		List[str]:

       '''simple docstring'''




       if padding is not False:
           if padding is True:
               snake_case   :				Tuple				 =   PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
           elif not isinstance(UpperCamelCase__						,     UpperCamelCase__						):
               snake_case   :				List[str]				 =   PaddingStrategy(UpperCamelCase__						)
           elif isinstance(UpperCamelCase__						,     UpperCamelCase__						):
               snake_case   :				List[str]				 =   padding
       else:
           snake_case   :				Union[str, Any]				 =   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   __lowerCAmelCase						(						lowercase  :  int			)     ->						int:


    """simple docstring"""





    if not isinstance(lowercase						,							lowercase			):
        raise ValueError("Input must be an integer"			)
    if input_num <= 0:
        raise ValueError("Input must be positive"			)
    return sum(
        divisor for divisor in range(1						,							input_num // 2 + 1			) if input_num % divisor == 0			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()





