



'''simple docstring'''
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]     ):





		def __init__(  self			,							__UpperCAmelCase=None			,							**__UpperCAmelCase					)							->  Any:

					'''simple docstring'''
					super().__init__(features=lowerCamelCase__					)
					__UpperCAmelCase :   Any    					=  np_array_kwargs





		def 				__A							(  self			,							__UpperCAmelCase					)							->  List[Any]:

					'''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 				__A							(  self			,							__UpperCAmelCase					)							->  Union[str, Any]:

					'''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 				__A							(  self			,							__UpperCAmelCase					)							->  List[Any]:

					'''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 				__A							(  self			,							__UpperCAmelCase					)							->  Union[str, Any]:

					'''simple docstring'''
					return map_nested(self._recursive_tensorize			,							lowerCamelCase__			,							map_list=lowerCamelCase__					)





		def 				__A							(  self			,							__UpperCAmelCase					)							->  Optional[Any]:

					'''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 				__A							(  self			,							__UpperCAmelCase					)							->  List[Any]:

					'''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 				__A							(  self			,							__UpperCAmelCase					)							->  Dict:

					'''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) = }")

