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






     """simple docstring"""


     def __init__(	self  :  str,			_snake_case  :  List[str]=None,			**_snake_case  :  Any )       ->List[Any]:
            super().__init__(features=_snake_case )
            snake_case__							:     Dict            =     np_array_kwargs






     def        lowercase_       (	self  :  Union[str, Any],			_snake_case  :  Any )       ->List[Any]:
            if isinstance(_snake_case,			_snake_case ):
                   if column and all(
                       isinstance(_snake_case,			np.ndarray ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
                          return np.stack(_snake_case )
                   else:
                          # don't use np.array(column, dtype=object)
                          # since it fails in certain cases
                          # see https://stackoverflow.com/q/51005699
                          snake_case__							:     List[str]            =     np.empty(len(_snake_case ),			dtype=_snake_case )
                          snake_case__							:     Dict            =     column
                          return out
            return column






     def        lowercase_       (	self  :  List[str],			_snake_case  :  List[str] )       ->List[str]:
            if isinstance(_snake_case,			(str, bytes, type(_snake_case )) ):
                   return value
            elif isinstance(_snake_case,			(np.character, np.ndarray) ) and np.issubdtype(value.dtype,			np.character ):
                   return value
            elif isinstance(_snake_case,			np.number ):
                   return value

            snake_case__							:     List[Any]            =     {}

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

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

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






     def        lowercase_       (	self  :  List[str],			_snake_case  :  List[Any] )       ->Union[str, Any]:
            # support for torch, tf, jax etc.
            if config.TORCH_AVAILABLE and "torch" in sys.modules:
                   import torch

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






     def        lowercase_       (	self  :  List[Any],			_snake_case  :  dict )       ->Any:
            return map_nested(self._recursive_tensorize,			_snake_case,			map_list=_snake_case )






     def        lowercase_       (	self  :  Dict,			_snake_case  :  pa.Table )       ->Mapping:
            snake_case__							:     Dict            =     self.numpy_arrow_extractor().extract_row(_snake_case )
            snake_case__							:     Tuple            =     self.python_features_decoder.decode_row(_snake_case )
            return self.recursive_tensorize(_snake_case )






     def        lowercase_       (	self  :  Dict,			_snake_case  :  pa.Table )       ->np.ndarray:
            snake_case__							:     Tuple            =     self.numpy_arrow_extractor().extract_column(_snake_case )
            snake_case__							:     List[Any]            =     self.python_features_decoder.decode_column(_snake_case,			pa_table.column_names[0] )
            snake_case__							:     str            =     self.recursive_tensorize(_snake_case )
            snake_case__							:     List[str]            =     self._consolidate(_snake_case )
            return column






     def        lowercase_       (	self  :  Optional[int],			_snake_case  :  pa.Table )       ->Mapping:
            snake_case__							:     Union[str, Any]            =     self.numpy_arrow_extractor().extract_batch(_snake_case )
            snake_case__							:     Dict            =     self.python_features_decoder.decode_batch(_snake_case )
            snake_case__							:     Dict            =     self.recursive_tensorize(_snake_case )
            for column_name in batch:
                   snake_case__							:     List[Any]            =     self._consolidate(batch[column_name] )
            return batch











def 	lowercase_							(A			:			int		, A			:			int				):
       return (input_a, input_a).count(0				) % 2
def 	lowercase_							():
       assert xor_gate(0		, 0				) == 0
       assert xor_gate(0		, 1				) == 1
       assert xor_gate(1		, 0				) == 1
       assert xor_gate(1		, 1				) == 0


if __name__ == "__main__":
 print(xor_gate(0, 0))
 print(xor_gate(0, 1))






