


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




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase=None			,						**__lowerCamelCase)      ->    List[str]:
            super().__init__(features=_UpperCAmelCase)
            _A				:	Optional[int]       =			np_array_kwargs


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    Optional[int]:


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


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    Union[str, Any]:


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

            _A				:	Tuple       =			{}

            if isinstance(_UpperCAmelCase			,						np.ndarray) and np.issubdtype(value.dtype			,						np.integer):
                   _A				:	Optional[Any]       =			{"dtype": np.intaa}
            elif isinstance(_UpperCAmelCase			,						np.ndarray) and np.issubdtype(value.dtype			,						np.floating):
                   _A				:	List[Any]       =			{"dtype": np.floataa}
            elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                   import PIL.Image

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

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


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    str:


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

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


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    Optional[Any]:
            return map_nested(self._recursive_tensorize			,						_UpperCAmelCase			,						map_list=_UpperCAmelCase)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    Mapping:
            _A				:	int       =			self.numpy_arrow_extractor().extract_row(_UpperCAmelCase)
            _A				:	Dict       =			self.python_features_decoder.decode_row(_UpperCAmelCase)
            return self.recursive_tensorize(_UpperCAmelCase)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    np.ndarray:
            _A				:	List[str]       =			self.numpy_arrow_extractor().extract_column(_UpperCAmelCase)
            _A				:	int       =			self.python_features_decoder.decode_column(_UpperCAmelCase			,						pa_table.column_names[0])
            _A				:	Dict       =			self.recursive_tensorize(_UpperCAmelCase)
            _A				:	List[str]       =			self._consolidate(_UpperCAmelCase)
            return column


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    Mapping:
            _A				:	Dict       =			self.numpy_arrow_extractor().extract_batch(_UpperCAmelCase)
            _A				:	List[Any]       =			self.python_features_decoder.decode_batch(_UpperCAmelCase)
            _A				:	Any       =			self.recursive_tensorize(_UpperCAmelCase)
            for column_name in batch:
                   _A				:	Optional[int]       =			self._consolidate(batch[column_name])
            return batch




def 							_UpperCAmelCase    (UpperCamelCase__					:  str						):
       _A				:	Tuple       =			str(UpperCamelCase__						).strip()
       if not bin_string:
              raise ValueError("Empty string was passed to the function"						)
       _A				:	Dict       =			bin_string[0] == "-"
       if is_negative:
              _A				:	Any       =			bin_string[1:]
       if not all(char in "01" for char in bin_string						):
              raise ValueError("Non-binary value was passed to the function"						)
       _A				:	Union[str, Any]       =			0
       for char in bin_string:
              _A				:	Union[str, Any]       =			2 * decimal_number + int(UpperCamelCase__						)
       return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
    from doctest import testmod

    testmod()
