






import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter




class 							__A	(     TensorFormatter[Mapping, np.ndarray, Mapping]					):





				"""simple docstring"""







				def __init__(					self				,    lowerCamelCase__=None				,    **lowerCamelCase__       ):




									"""simple docstring"""

									super().__init__(features=lowerCamelCase__       )
									__UpperCamelCase :   Any									=np_array_kwargs


				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									if isinstance(lowerCamelCase__				,    lowerCamelCase__       ):
														if column and all(
														    isinstance(lowerCamelCase__				,    np.ndarray       ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column       ):
																			return np.stack(lowerCamelCase__       )
														else:
																			# don't use np.array(column, dtype=object)
																			# since it fails in certain cases
																			# see https://stackoverflow.com/q/51005699
																			__UpperCamelCase :   Tuple									=np.empty(len(lowerCamelCase__       )				,    dtype=lowerCamelCase__       )
																			__UpperCamelCase :   Tuple									=column
																			return out
									return column


				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									if isinstance(lowerCamelCase__				,    (str, bytes, type(lowerCamelCase__       ))       ):
														return value
									elif isinstance(lowerCamelCase__				,    (np.character, np.ndarray)       ) and np.issubdtype(value.dtype				,    np.character       ):
														return value
									elif isinstance(lowerCamelCase__				,    np.number       ):
														return value

									__UpperCamelCase :   List[str]									={}

									if isinstance(lowerCamelCase__				,    np.ndarray       ) and np.issubdtype(value.dtype				,    np.integer       ):
														__UpperCamelCase :   int									={'dtype': np.intaa}
									elif isinstance(lowerCamelCase__				,    np.ndarray       ) and np.issubdtype(value.dtype				,    np.floating       ):
														__UpperCamelCase :   Optional[int]									={'dtype': np.floataa}
									elif config.PIL_AVAILABLE and "PIL" in sys.modules:
														import PIL.Image

														if isinstance(lowerCamelCase__				,    PIL.Image.Image       ):
																			return np.asarray(lowerCamelCase__				,    **self.np_array_kwargs       )

									return np.asarray(lowerCamelCase__				,    **{**default_dtype, **self.np_array_kwargs}       )


				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									if config.TORCH_AVAILABLE and "torch" in sys.modules:
														import torch

														if isinstance(lowerCamelCase__				,    torch.Tensor       ):
																			return self._tensorize(data_struct.detach().cpu().numpy()[()]       )
									if hasattr(lowerCamelCase__				,    '__array__'       ) and not isinstance(lowerCamelCase__				,    (np.ndarray, np.character, np.number)       ):
														__UpperCamelCase :   int									=data_struct.__array__()
									# support for nested types like struct of list of struct
									if isinstance(lowerCamelCase__				,    np.ndarray       ):
														if data_struct.dtype == object:
																			return self._consolidate([self.recursive_tensorize(lowerCamelCase__       ) for substruct in data_struct]       )
									if isinstance(lowerCamelCase__				,    (list, tuple)       ):
														return self._consolidate([self.recursive_tensorize(lowerCamelCase__       ) for substruct in data_struct]       )
									return self._tensorize(lowerCamelCase__       )


				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									return map_nested(self._recursive_tensorize				,    lowerCamelCase__				,    map_list=lowerCamelCase__       )


				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   List[str]									=self.numpy_arrow_extractor().extract_row(lowerCamelCase__       )
									__UpperCamelCase :   List[Any]									=self.python_features_decoder.decode_row(lowerCamelCase__       )
									return self.recursive_tensorize(lowerCamelCase__       )


				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Union[str, Any]									=self.numpy_arrow_extractor().extract_column(lowerCamelCase__       )
									__UpperCamelCase :   List[Any]									=self.python_features_decoder.decode_column(lowerCamelCase__				,    pa_table.column_names[0]       )
									__UpperCamelCase :   List[Any]									=self.recursive_tensorize(lowerCamelCase__       )
									__UpperCamelCase :   Tuple									=self._consolidate(lowerCamelCase__       )
									return column







				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   List[Any]									=self.numpy_arrow_extractor().extract_batch(lowerCamelCase__       )
									__UpperCamelCase :   int									=self.python_features_decoder.decode_batch(lowerCamelCase__       )
									__UpperCamelCase :   Optional[int]									=self.recursive_tensorize(lowerCamelCase__       )
									for column_name in batch:
														__UpperCamelCase :   List[str]									=self._consolidate(batch[column_name]       )
									return batch









from __future__ import annotations






def 				A	(			a_				,a_   )	->		list[int]:
					__UpperCamelCase :   dict[int, int]									={}
					for index, val in enumerate(a_   ):
										__UpperCamelCase :   Union[str, Any]									=target - val
										if compl in chk_map:
															return [chk_map[compl], index]
										__UpperCamelCase :   List[Any]									=index
					return []


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(f"{two_sum([2, 7, 11, 15], 9) = }")

