
# 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 							lowercase   (						TensorFormatter[Mapping, "tf.Tensor", Mapping]  ):



		def __init__(					self							, A_=None							, **A_			)    ->   Union[str, Any]:
				"""simple docstring"""
				super().__init__(features=A_			)
				UpperCamelCase            =	tf_tensor_kwargs
				import tensorflow as tf  # noqa: import tf at initialization



		def __UpperCamelCase					(					self							, A_			)    ->   Union[str, Any]:
				"""simple docstring"""
				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 __UpperCamelCase					(					self							, A_			)    ->   Any:
				"""simple docstring"""
				import tensorflow as tf

				if value is None:
						return value

				UpperCamelCase            =	{}

				if isinstance(A_							, (np.number, np.ndarray)			) and np.issubdtype(value.dtype							, np.integer			):
						UpperCamelCase            =	{'dtype': tf.intaa}
				elif isinstance(A_							, (np.number, np.ndarray)			) and np.issubdtype(value.dtype							, np.floating			):
						UpperCamelCase            =	{'dtype': tf.floataa}
				elif config.PIL_AVAILABLE and "PIL" in sys.modules:
						import PIL.Image

						if isinstance(A_							, PIL.Image.Image			):
								UpperCamelCase            =	np.asarray(A_			)

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



		def __UpperCamelCase					(					self							, A_			)    ->   int:
				"""simple docstring"""
				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            =	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 __UpperCamelCase					(					self							, A_			)    ->   Any:
				"""simple docstring"""
				return map_nested(self._recursive_tensorize							, A_							, map_list=A_			)



		def __UpperCamelCase					(					self							, A_			)    ->   Mapping:
				"""simple docstring"""
				UpperCamelCase            =	self.numpy_arrow_extractor().extract_row(A_			)
				UpperCamelCase            =	self.python_features_decoder.decode_row(A_			)
				return self.recursive_tensorize(A_			)



		def __UpperCamelCase					(					self							, A_			)    ->   "tf.Tensor":
				"""simple docstring"""
				UpperCamelCase            =	self.numpy_arrow_extractor().extract_column(A_			)
				UpperCamelCase            =	self.python_features_decoder.decode_column(A_							, pa_table.column_names[0]			)
				UpperCamelCase            =	self.recursive_tensorize(A_			)
				UpperCamelCase            =	self._consolidate(A_			)
				return column



		def __UpperCamelCase					(					self							, A_			)    ->   Mapping:
				"""simple docstring"""
				UpperCamelCase            =	self.numpy_arrow_extractor().extract_batch(A_			)
				UpperCamelCase            =	self.python_features_decoder.decode_batch(A_			)
				UpperCamelCase            =	self.recursive_tensorize(A_			)
				for column_name in batch:
						UpperCamelCase            =	self._consolidate(batch[column_name]			)
				return batch


def  A			(   lowercase					)  ->    int:
		'''simple docstring'''

		return int(lowercase					) if x - int(lowercase					) >= 0 else int(lowercase					) - 1


if __name__ == "__main__":
					import doctest

					doctest.testmod()
