
"""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      UpperCamelCase	(       TensorFormatter[Mapping, np.ndarray, Mapping]      ):

				"""simple docstring"""



				def __init__(				self		,	lowercase__=None		,	**lowercase__			)    ->    Tuple:
									"""simple docstring"""







									super().__init__(features=lowercase__			)
									SCREAMING_SNAKE_CASE				=	np_array_kwargs


				def 							A					(				self		,	lowercase__			)    ->    Optional[Any]:
									"""simple docstring"""







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


				def 							A					(				self		,	lowercase__			)    ->    Optional[int]:
									"""simple docstring"""







									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
									elif isinstance(lowercase__		,	np.number			):
														return value

									SCREAMING_SNAKE_CASE				=	{}

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

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

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


				def 							A					(				self		,	lowercase__			)    ->    Any:
									"""simple docstring"""







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

														if isinstance(lowercase__		,	torch.Tensor			):
																			return self._tensorize(data_struct.detach().cpu().numpy()[()]			)
									if hasattr(lowercase__		,	'__array__'			) and not isinstance(lowercase__		,	(np.ndarray, np.character, np.number)			):
														SCREAMING_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:
																			return self._consolidate([self.recursive_tensorize(lowercase__			) for substruct in data_struct]			)
									if isinstance(lowercase__		,	(list, tuple)			):
														return self._consolidate([self.recursive_tensorize(lowercase__			) for substruct in data_struct]			)
									return self._tensorize(lowercase__			)


				def 							A					(				self		,	lowercase__			)    ->    List[str]:
									"""simple docstring"""







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


				def 							A					(				self		,	lowercase__			)    ->    Mapping:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self.numpy_arrow_extractor().extract_row(lowercase__			)
									SCREAMING_SNAKE_CASE				=	self.python_features_decoder.decode_row(lowercase__			)
									return self.recursive_tensorize(lowercase__			)


				def 							A					(				self		,	lowercase__			)    ->    np.ndarray:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self.numpy_arrow_extractor().extract_column(lowercase__			)
									SCREAMING_SNAKE_CASE				=	self.python_features_decoder.decode_column(lowercase__		,	pa_table.column_names[0]			)
									SCREAMING_SNAKE_CASE				=	self.recursive_tensorize(lowercase__			)
									SCREAMING_SNAKE_CASE				=	self._consolidate(lowercase__			)
									return column




				def 							A					(				self		,	lowercase__			)    ->    Mapping:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self.numpy_arrow_extractor().extract_batch(lowercase__			)
									SCREAMING_SNAKE_CASE				=	self.python_features_decoder.decode_batch(lowercase__			)
									SCREAMING_SNAKE_CASE				=	self.recursive_tensorize(lowercase__			)
									for column_name in batch:
														SCREAMING_SNAKE_CASE				=	self._consolidate(batch[column_name]			)
									return batch


"""simple docstring"""




snake_case    =		1_0_0_0

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					return x * x

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = 4			):
					assert callable(
					    SCREAMING_SNAKE_CASE_			), f'''the function(object) passed should be callable your input : {function}'''
					assert isinstance(SCREAMING_SNAKE_CASE_,     (float, int)			), f'''a should be float or integer your input : {a}'''
					assert isinstance(function(SCREAMING_SNAKE_CASE_			),     (float, int)			), (
					    "the function should return integer or float return type of your function, "
					    f'''{type(SCREAMING_SNAKE_CASE_			)}'''
					)
					assert isinstance(SCREAMING_SNAKE_CASE_,     (float, int)			), f'''b should be float or integer your input : {b}'''
					assert (
					    isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			) and precision > 0
					), f'''precision should be positive integer your input : {precision}'''

					# just applying the formula of simpson for approximate integration written in
					# mentioned article in first comment of this file and above this function

					SCREAMING_SNAKE_CASE				=	(b - a) / N_STEPS
					SCREAMING_SNAKE_CASE				=	function(SCREAMING_SNAKE_CASE_			) + function(SCREAMING_SNAKE_CASE_			)

					for i in range(1,     SCREAMING_SNAKE_CASE_			):
										SCREAMING_SNAKE_CASE				=	a + h * i
										result += function(SCREAMING_SNAKE_CASE_			) * (4 if i % 2 else 2)

					result *= h / 3
					return round(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
