



'''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


_lowerCamelCase	:       int      	=	logging.get_logger(__name__)

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    def __init__(  self      :					Any  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					float  ,						**UpperCamelCase__      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase					=  feature_size
       UpperCamelCase					=  sampling_rate
       UpperCamelCase					=  padding_value

       UpperCamelCase					=  kwargs.pop('padding_side'  ,						'right' )
       UpperCamelCase					=  kwargs.pop('return_attention_mask'  ,						UpperCamelCase__ )

       super().__init__(**UpperCamelCase__ )






    def  A       (  self      :					int  ,						UpperCamelCase__      :					Union[
            BatchFeature,
            List[BatchFeature],
            Dict[str, BatchFeature],
            Dict[str, List[BatchFeature]],
            List[Dict[str, BatchFeature]],
        ]  ,						UpperCamelCase__      :					Union[bool, str, PaddingStrategy] = True  ,						UpperCamelCase__      :					Optional[int] = None  ,						UpperCamelCase__      :					bool = False  ,						UpperCamelCase__      :					Optional[int] = None  ,						UpperCamelCase__      :					Optional[bool] = None  ,						UpperCamelCase__      :					Optional[Union[str, TensorType]] = None  ,						):


       """simple docstring"""







       if isinstance(UpperCamelCase__  ,						(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(UpperCamelCase__ ) == 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(UpperCamelCase__  ,						(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(UpperCamelCase__ ):
             UpperCamelCase					=  required_input[index][0]

       if return_tensors is None:
          if is_tf_tensor(UpperCamelCase__ ):
             UpperCamelCase					=  'tf'
          elif is_torch_tensor(UpperCamelCase__ ):
             UpperCamelCase					=  'pt'
          elif isinstance(UpperCamelCase__  ,						(int, float, list, tuple, np.ndarray) ):
             UpperCamelCase					=  '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) ):
             UpperCamelCase					=  to_numpy(UpperCamelCase__ )
          else:
             UpperCamelCase					=  [to_numpy(UpperCamelCase__ ) for v in value]

        # Convert padding_strategy in PaddingStrategy
       UpperCamelCase					=  self._get_padding_strategies(padding=UpperCamelCase__  ,						max_length=UpperCamelCase__ )

       UpperCamelCase					=  processed_features[self.model_input_names[0]]

       UpperCamelCase					=  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.' )

       UpperCamelCase					=  []
       for i in range(UpperCamelCase__ ):
          UpperCamelCase					=  {k: v[i] for k, v in processed_features.items()}
          # truncation
          UpperCamelCase					=  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
          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(UpperCamelCase__ ):
          # padding
          UpperCamelCase					=  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:
                UpperCamelCase					=  []
             if value.dtype is np.dtype(np.floataa ):
                UpperCamelCase					=  value.astype(np.floataa )
             batch_outputs[key].append(UpperCamelCase__ )

       return BatchFeature(UpperCamelCase__  ,						tensor_type=UpperCamelCase__ )






    def  A       (  self      :					Union[str, Any]  ,						UpperCamelCase__      :					Union[Dict[str, np.ndarray], BatchFeature]  ,						UpperCamelCase__      :					Optional[int] = None  ,						UpperCamelCase__      :					PaddingStrategy = PaddingStrategy.DO_NOT_PAD  ,						UpperCamelCase__      :					Optional[int] = None  ,						UpperCamelCase__      :					Optional[bool] = None  ,						):


       """simple docstring"""







       UpperCamelCase					=  processed_features[self.model_input_names[0]]

       if padding_strategy == PaddingStrategy.LONGEST:
          UpperCamelCase					=  len(UpperCamelCase__ )

       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(UpperCamelCase__ ) < max_length

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

       if needs_to_be_padded:
          UpperCamelCase					=  max_length - len(UpperCamelCase__ )
          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(
                 UpperCamelCase__  ,						UpperCamelCase__  ,						'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(
                 UpperCamelCase__  ,						UpperCamelCase__  ,						'constant'  ,						constant_values=self.padding_value )
          else:
             raise ValueError('Invalid padding strategy:' + str(self.padding_side ) )

       return processed_features






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


       """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.' )

       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(UpperCamelCase__ ) > 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  A       (  self      :					List[Any]  ,						UpperCamelCase__      :					Tuple=False  ,						UpperCamelCase__      :					Optional[Any]=None ):


       """simple docstring"""







       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(UpperCamelCase__  ,						UpperCamelCase__ ):
             UpperCamelCase					=  PaddingStrategy(UpperCamelCase__ )
          elif isinstance(UpperCamelCase__  ,						UpperCamelCase__ ):
             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 				__lowerCamelCase				(   A__ = 50    )       ->	int:






   """simple docstring"""
   UpperCamelCase					=  [1] * (length + 1)

   for row_length in range(3    ,						length + 1    ):
      for block_length in range(3    ,						row_length + 1    ):
         for block_start in range(row_length - block_length    ):
            ways_number[row_length] += ways_number[
                row_length - block_start - block_length - 1
            ]

         ways_number[row_length] += 1

   return ways_number[length]


if __name__ == "__main__":
   print(f'''{solution() = }''')

