





'''simple docstring'''




import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD







def _lowerCAmelCase  (   _lowerCAmelCase     )->  Any:
 def wrapper(*_lowerCAmelCase							,     **_lowerCAmelCase     ):
  __UpperCAmelCase       = timeit.default_timer()
  __UpperCAmelCase       = func(*_snake_case							,     **_snake_case     )
  __UpperCAmelCase       = timeit.default_timer() - starttime
  return delta

 __UpperCAmelCase       = func.__name__

 return wrapper







def _lowerCAmelCase  (   _lowerCAmelCase							,     _lowerCAmelCase=1_00							,     _lowerCAmelCase=None     )->  Tuple:
 __UpperCAmelCase       = []
 __UpperCAmelCase       = seq_shapes or {}
 for i in range(_snake_case     ):
  __UpperCAmelCase       = {}
  for col_id, (k, v) in enumerate(features.items()     ):
   if isinstance(_snake_case							,     _ArrayXD     ):
    __UpperCAmelCase       = np.random.rand(*v.shape     ).astype(v.dtype     )
   elif isinstance(_snake_case							,     datasets.Value     ):
    if v.dtype == "string":
     __UpperCAmelCase       = 'The small grey turtle was surprisingly fast when challenged.'
    else:
     __UpperCAmelCase       = np.random.randint(10							,     size=1     ).astype(v.dtype     ).item()
   elif isinstance(_snake_case							,     datasets.Sequence     ):
    while isinstance(_snake_case							,     datasets.Sequence     ):
     __UpperCAmelCase       = v.feature
    __UpperCAmelCase       = seq_shapes[k]
    __UpperCAmelCase       = np.random.rand(*_snake_case     ).astype(v.dtype     )
   __UpperCAmelCase       = data

  dummy_data.append((i, example)     )

 return dummy_data







def _lowerCAmelCase  (   _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase=1_00							,     _lowerCAmelCase=None     )->  str:
 __UpperCAmelCase       = generate_examples(_snake_case							,     num_examples=_snake_case							,     seq_shapes=_snake_case     )

 with ArrowWriter(features=_snake_case							,     path=_snake_case     ) as writer:
  for key, record in dummy_data:
   __UpperCAmelCase       = features.encode_example(_snake_case     )
   writer.write(_snake_case     )

  __UpperCAmelCase       = writer.finalize()

 if not num_final_examples == num_examples:
  raise ValueError(
      F'Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'     )

 __UpperCAmelCase       = datasets.Dataset.from_file(filename=_snake_case							,     info=datasets.DatasetInfo(features=_snake_case     )     )

 return dataset







"""simple docstring"""



from __future__ import annotations

from collections.abc import Iterator





class 				a			:



      def __init__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      str     							=					value
          UpperCAmelCase__	:      Node | None     							=					None
          UpperCAmelCase__	:      Node | None     							=					None





class 				a			:



      def __init__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      str     							=					tree



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          if node is None:
              return 0
          return node.value + (
              self.depth_first_search(node.left      ) + self.depth_first_search(node.right      )
          )



      def __iter__(   self      ):
          yield self.depth_first_search(self.tree      )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

