
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 							SCREAMING_SNAKE_CASE__     (				TensorFormatter[Mapping, np.ndarray, Mapping]     ):

							"""simple docstring"""






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





													'''simple docstring'''






													super().__init__(features=SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      np_array_kwargs



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> Optional[int]:





													'''simple docstring'''






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



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> Optional[Any]:





													'''simple docstring'''






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

													__UpperCamelCase  	=      {}

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

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

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



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> Dict:





													'''simple docstring'''






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

																			if isinstance(SCREAMING_SNAKE_CASE_  ,						torch.Tensor      ):
																									return self._tensorize(data_struct.detach().cpu().numpy()[()]      )
													if hasattr(SCREAMING_SNAKE_CASE_  ,						'''__array__'''      ) and not isinstance(SCREAMING_SNAKE_CASE_  ,						(np.ndarray, np.character, np.number)      ):
																			__UpperCamelCase  	=      data_struct.__array__()
													# support for nested types like struct of list of struct
													if isinstance(SCREAMING_SNAKE_CASE_  ,						np.ndarray      ):
																			if data_struct.dtype == object:
																									return self._consolidate([self.recursive_tensorize(SCREAMING_SNAKE_CASE_      ) for substruct in data_struct]      )
													if isinstance(SCREAMING_SNAKE_CASE_  ,						(list, tuple)      ):
																			return self._consolidate([self.recursive_tensorize(SCREAMING_SNAKE_CASE_      ) for substruct in data_struct]      )
													return self._tensorize(SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> int:





													'''simple docstring'''






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



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> Mapping:





													'''simple docstring'''






													__UpperCamelCase  	=      self.numpy_arrow_extractor().extract_row(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      self.python_features_decoder.decode_row(SCREAMING_SNAKE_CASE_      )
													return self.recursive_tensorize(SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> np.ndarray:





													'''simple docstring'''






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



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> Mapping:





													'''simple docstring'''






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








_A		:					Union[str, Any]          =							1_000



def      __lowerCAmelCase    (				snake_case						:     float			)							->				float:
   return x * x



def      __lowerCAmelCase    (				snake_case						:     Optional[int]    ,   snake_case						:     float    ,   snake_case						:     float    ,   snake_case						:     int = 4			)							->				float:
   assert callable(
       snake_case			), f'the function(object) passed should be callable your input : {function}'
   assert isinstance(snake_case    ,   (float, int)			), f'a should be float or integer your input : {a}'
   assert isinstance(function(snake_case			)    ,   (float, int)			), (
       "the function should return integer or float return type of your function, "
       f'{type(snake_case			)}'
   )
   assert isinstance(snake_case    ,   (float, int)			), f'b should be float or integer your input : {b}'
   assert (
       isinstance(snake_case    ,   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

   __lowerCamelCase:					List[str]	 =						(b - a) / N_STEPS
   __lowerCamelCase:					Optional[Any]	 =						function(snake_case			) + function(snake_case			)

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

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


if __name__ == "__main__":
   import doctest

   doctest.testmod()


