

import timeit

import numpy as np

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






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[Any]:
 def wrapper(*lowerCamelCase__     ,			**lowerCamelCase__      ):
  __lowerCamelCase    :    str 	=			timeit.default_timer()
  __lowerCamelCase    :    Optional[Any] 	=			func(*lowerCamelCase__     ,			**lowerCamelCase__      )
  __lowerCamelCase    :    Any 	=			timeit.default_timer() - starttime
  return delta

 __lowerCamelCase    :    str 	=			func.__name__

 return wrapper






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=1_0_0     ,			lowerCamelCase__=None      )  ->					Tuple:
 __lowerCamelCase    :    int 	=			[]
 __lowerCamelCase    :    str 	=			seq_shapes or {}
 for i in range(lowerCamelCase__      ):
  __lowerCamelCase    :    Any 	=			{}
  for col_id, (k, v) in enumerate(features.items()      ):
   if isinstance(lowerCamelCase__     ,			_ArrayXD      ):
    __lowerCamelCase    :    Optional[Any] 	=			np.random.rand(*v.shape      ).astype(v.dtype      )
   elif isinstance(lowerCamelCase__     ,			datasets.Value      ):
    if v.dtype == "string":
     __lowerCamelCase    :    Optional[int] 	=			'The small grey turtle was surprisingly fast when challenged.'
    else:
     __lowerCamelCase    :    List[Any] 	=			np.random.randint(1_0     ,			size=1      ).astype(v.dtype      ).item()
   elif isinstance(lowerCamelCase__     ,			datasets.Sequence      ):
    while isinstance(lowerCamelCase__     ,			datasets.Sequence      ):
     __lowerCamelCase    :    Union[str, Any] 	=			v.feature
    __lowerCamelCase    :    Tuple 	=			seq_shapes[k]
    __lowerCamelCase    :    List[Any] 	=			np.random.rand(*lowerCamelCase__      ).astype(v.dtype      )
   __lowerCamelCase    :    Dict 	=			data

  dummy_data.append((i, example)      )

 return dummy_data






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__=1_0_0     ,			lowerCamelCase__=None      )  ->					int:
 __lowerCamelCase    :    Optional[Any] 	=			generate_examples(lowerCamelCase__     ,			num_examples=lowerCamelCase__     ,			seq_shapes=lowerCamelCase__      )

 with ArrowWriter(features=lowerCamelCase__     ,			path=lowerCamelCase__      ) as writer:
  for key, record in dummy_data:
   __lowerCamelCase    :    Any 	=			features.encode_example(lowerCamelCase__      )
   writer.write(lowerCamelCase__      )

  __lowerCamelCase    :    Any 	=			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}."      )

 __lowerCamelCase    :    int 	=			datasets.Dataset.from_file(filename=lowerCamelCase__     ,			info=datasets.DatasetInfo(features=lowerCamelCase__      )      )

 return dataset







from __future__ import annotations






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					bool:
	if len(lowerCamelCase__      ) == 0:
		return False
	__lowerCamelCase    :    List[Any] 	=			len(lowerCamelCase__      ) // 2
	if a_list[midpoint] == item:
		return True
	if item < a_list[midpoint]:
		return binary_search(a_list[:midpoint]     ,			lowerCamelCase__      )
	else:
		return binary_search(a_list[midpoint + 1 :]     ,			lowerCamelCase__      )


if __name__ == "__main__":
					a		=input("""Enter numbers separated by comma:\n""").strip()
					a		=[int(item.strip()) for item in user_input.split(""",""")]
					a		=int(input("""Enter the number to be found in the list:\n""").strip())
					a		="""""" if binary_search(sequence, target) else """not """
					print(F"""{target} was {not_str}found in {sequence}""")




