




'''simple docstring'''

# 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 torch






class 						a				(   TensorFormatter[Mapping, "torch.Tensor", Mapping]     ):


				def __init__(		self    :					Dict       ,				lowercase_    :					List[str]=None       ,				**lowercase_    :					Union[str, Any]					):
									super().__init__(features=lowercase_					)
									snake_case_									=				torch_tensor_kwargs
									import torch  # noqa import torch at initialization


				def   A_ (		self    :					str       ,				lowercase_    :					str					):
									import torch

									if isinstance(lowercase_       ,				lowercase_					) and column:
														if all(
														    isinstance(lowercase_       ,				torch.Tensor					) and x.shape == column[0].shape and x.dtype == column[0].dtype
														    for x in column					):
																			return torch.stack(lowercase_					)
									return column


				def   A_ (		self    :					Any       ,				lowercase_    :					Tuple					):
									import torch

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

									snake_case_									=				{}

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

														if isinstance(lowercase_       ,				PIL.Image.Image					):
																			snake_case_									=				np.asarray(lowercase_					)
									return torch.tensor(lowercase_       ,				**{**default_dtype, **self.torch_tensor_kwargs}					)


				def   A_ (		self    :					List[Any]       ,				lowercase_    :					Optional[int]					):
									import torch

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


				def   A_ (		self    :					Optional[Any]       ,				lowercase_    :					dict					):
									return map_nested(self._recursive_tensorize       ,				lowercase_       ,				map_list=lowercase_					)


				def   A_ (		self    :					Dict       ,				lowercase_    :					pa.Table					):
									snake_case_									=				self.numpy_arrow_extractor().extract_row(lowercase_					)
									snake_case_									=				self.python_features_decoder.decode_row(lowercase_					)
									return self.recursive_tensorize(lowercase_					)


				def   A_ (		self    :					List[Any]       ,				lowercase_    :					pa.Table					):
									snake_case_									=				self.numpy_arrow_extractor().extract_column(lowercase_					)
									snake_case_									=				self.python_features_decoder.decode_column(lowercase_       ,				pa_table.column_names[0]					)
									snake_case_									=				self.recursive_tensorize(lowercase_					)
									snake_case_									=				self._consolidate(lowercase_					)
									return column


				def   A_ (		self    :					str       ,				lowercase_    :					pa.Table					):
									snake_case_									=				self.numpy_arrow_extractor().extract_batch(lowercase_					)
									snake_case_									=				self.python_features_decoder.decode_batch(lowercase_					)
									snake_case_									=				self.recursive_tensorize(lowercase_					)
									for column_name in batch:
														snake_case_									=				self._consolidate(batch[column_name]					)
									return batch







'''simple docstring'''

import math
from collections.abc import Callable


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					float:

					'''simple docstring'''

					snake_case_									=				xa
					snake_case_									=				xa
					while True:
										if x_n == x_na or function(__UpperCAmelCase	) == function(__UpperCAmelCase	):
															raise ZeroDivisionError('''float division by zero, could not find root'''	)
										snake_case_									=				x_na - (
										    function(__UpperCAmelCase	) / ((function(__UpperCAmelCase	) - function(__UpperCAmelCase	)) / (x_na - x_n))
										)
										if abs(x_na - x_na	) < 10**-5:
															return x_na
										snake_case_									=				x_na
										snake_case_									=				x_na


def 		__magic_name__    (	__UpperCAmelCase	)		->					float:

					'''simple docstring'''

					return math.pow(__UpperCAmelCase,      3	) - (2 * x) - 5


if __name__ == "__main__":
							print(intersection(f, 3, 3.5))

