




import timeit

import numpy as np

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







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			List[str]:



       """simple docstring"""

       def wrapper(*lowercase_					,							**lowercase_		):
              A__ 					=       timeit.default_timer()
              A__ 					=       func(*lowercase_					,							**lowercase_		)
              A__ 					=       timeit.default_timer() - starttime
              return delta

       A__ 					=       func.__name__

       return wrapper







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_=100					,							lowercase_=None		)  ->			int:



       """simple docstring"""

       A__ 					=       []
       A__ 					=       seq_shapes or {}
       for i in range(lowercase_		):
              A__ 					=       {}
              for col_id, (k, v) in enumerate(features.items()		):
                     if isinstance(lowercase_					,							_ArrayXD		):
                            A__ 					=       np.random.rand(*v.shape		).astype(v.dtype		)
                     elif isinstance(lowercase_					,							datasets.Value		):
                            if v.dtype == "string":
                                   A__ 					=       '''The small grey turtle was surprisingly fast when challenged.'''
                            else:
                                   A__ 					=       np.random.randint(10					,							size=1		).astype(v.dtype		).item()
                     elif isinstance(lowercase_					,							datasets.Sequence		):
                            while isinstance(lowercase_					,							datasets.Sequence		):
                                   A__ 					=       v.feature
                            A__ 					=       seq_shapes[k]
                            A__ 					=       np.random.rand(*lowercase_		).astype(v.dtype		)
                     A__ 					=       data

              dummy_data.append((i, example)		)

       return dummy_data







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_=100					,							lowercase_=None		)  ->			Optional[Any]:



       """simple docstring"""

       A__ 					=       generate_examples(lowercase_					,							num_examples=lowercase_					,							seq_shapes=lowercase_		)

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

              A__					,				A__ 					=       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}."""		)

       A__ 					=       datasets.Dataset.from_file(filename=lowercase_					,							info=datasets.DatasetInfo(features=lowercase_		)		)

       return dataset





def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE = 100   ):
      '''simple docstring'''







      A_							       =		0
      A_							       =		0
      for i in range(1		,				n + 1   ):
            sum_of_squares += i**2
            sum_of_ints += i
      return sum_of_ints**2 - sum_of_squares


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





