



# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

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


if TYPE_CHECKING:
						import tensorflow as tf

class     SCREAMING_SNAKE_CASE(							TensorFormatter[Mapping, """tf.Tensor""", Mapping]      ):







	"""simple docstring"""






	def __init__(						self      :					Optional[Any]		,      __snake_case      :					List[str]=None		,      **__snake_case      :					Optional[Any]		)      ->		Optional[Any]:
								super().__init__(features=_A		)
								UpperCAmelCase			:	Optional[int]										=		tf_tensor_kwargs
								import tensorflow as tf  # noqa: import tf at initialization







	def 					A     (						self      :					Dict		,      __snake_case      :					List[str]		)      ->		Union[str, Any]:
								import tensorflow as tf

								if isinstance(_A		,      _A		) and column:
															if all(
															    isinstance(_A		,      tf.Tensor		) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column		):
																						return tf.stack(_A		)
															elif all(
															    isinstance(_A		,      (tf.Tensor, tf.RaggedTensor)		) and x.ndim == 1 and x.dtype == column[0].dtype
															    for x in column		):
																						# only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
																						return tf.ragged.stack(_A		)

								return column







	def 					A     (						self      :					List[str]		,      __snake_case      :					List[Any]		)      ->		Any:
								import tensorflow as tf

								if value is None:
															return value

								UpperCAmelCase			:	str										=		{}

								if isinstance(_A		,      (np.number, np.ndarray)		) and np.issubdtype(value.dtype		,      np.integer		):
															UpperCAmelCase			:	Dict										=		{'''dtype''': tf.intaa}
								elif isinstance(_A		,      (np.number, np.ndarray)		) and np.issubdtype(value.dtype		,      np.floating		):
															UpperCAmelCase			:	Union[str, Any]										=		{'''dtype''': tf.floataa}
								elif config.PIL_AVAILABLE and "PIL" in sys.modules:
															import PIL.Image

															if isinstance(_A		,      PIL.Image.Image		):
																						UpperCAmelCase			:	Union[str, Any]										=		np.asarray(_A		)

								return tf.convert_to_tensor(_A		,      **{**default_dtype, **self.tf_tensor_kwargs}		)







	def 					A     (						self      :					Optional[Any]		,      __snake_case      :					Tuple		)      ->		Optional[Any]:
								import tensorflow as tf

								# support for torch, tf, jax etc.
								if config.TORCH_AVAILABLE and "torch" in sys.modules:
															import torch

															if isinstance(_A		,      torch.Tensor		):
																						return self._tensorize(data_struct.detach().cpu().numpy()[()]		)
								if hasattr(_A		,      '''__array__'''		) and not isinstance(_A		,      tf.Tensor		):
															UpperCAmelCase			:	List[str]										=		data_struct.__array__()
								# support for nested types like struct of list of struct
								if isinstance(_A		,      np.ndarray		):
															if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
																						return self._consolidate([self.recursive_tensorize(_A		) for substruct in data_struct]		)
								elif isinstance(_A		,      (list, tuple)		):
															return self._consolidate([self.recursive_tensorize(_A		) for substruct in data_struct]		)
								return self._tensorize(_A		)







	def 					A     (						self      :					str		,      __snake_case      :					dict		)      ->		Optional[Any]:
								return map_nested(self._recursive_tensorize		,      _A		,      map_list=_A		)







	def 					A     (						self      :					List[str]		,      __snake_case      :					pa.Table		)      ->		Tuple:
								UpperCAmelCase			:	Optional[int]										=		self.numpy_arrow_extractor().extract_row(_A		)
								UpperCAmelCase			:	Tuple										=		self.python_features_decoder.decode_row(_A		)
								return self.recursive_tensorize(_A		)







	def 					A     (						self      :					List[str]		,      __snake_case      :					pa.Table		)      ->		List[Any]:
								UpperCAmelCase			:	Union[str, Any]										=		self.numpy_arrow_extractor().extract_column(_A		)
								UpperCAmelCase			:	Union[str, Any]										=		self.python_features_decoder.decode_column(_A		,      pa_table.column_names[0]		)
								UpperCAmelCase			:	Dict										=		self.recursive_tensorize(_A		)
								UpperCAmelCase			:	str										=		self._consolidate(_A		)
								return column







	def 					A     (						self      :					List[str]		,      __snake_case      :					pa.Table		)      ->		str:
								UpperCAmelCase			:	Any										=		self.numpy_arrow_extractor().extract_batch(_A		)
								UpperCAmelCase			:	Union[str, Any]										=		self.python_features_decoder.decode_batch(_A		)
								UpperCAmelCase			:	str										=		self.recursive_tensorize(_A		)
								for column_name in batch:
															UpperCAmelCase			:	str										=		self._consolidate(batch[column_name]		)
								return batch










'''simple docstring'''






from __future__ import annotations


def 			snake_case_					( _lowerCAmelCase  :						list[int]       ,				_lowerCAmelCase  :						int  )				->       list[int]:
							UpperCAmelCase			:	dict[int, int]										=		{}
							for index, val in enumerate(_lowerCAmelCase  ):
														UpperCAmelCase			:	Tuple										=		target - val
														if compl in chk_map:
																					return [chk_map[compl], index]
														UpperCAmelCase			:	Any										=		index
							return []


if __name__ == "__main__":
						import doctest

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





