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


  '''simple docstring'''







  def __init__(			self      :			List[str]      ,							UpperCamelCase__      :			List[str]=None      ,							**UpperCamelCase__      :			str   )      ->     List[str]:







    """simple docstring"""






    super().__init__(features=UpperCamelCase__   )
    __magic_name__    					=	np_array_kwargs
  def 							_lowercase							(			self      :			List[str]      ,							UpperCamelCase__      :			Optional[Any]   )      ->     Tuple:







    """simple docstring"""






    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
        __magic_name__    					=	np.empty(len(UpperCamelCase__   )      ,							dtype=UpperCamelCase__   )
        __magic_name__    					=	column
        return out
    return column
  def 							_lowercase							(			self      :			Any      ,							UpperCamelCase__      :			Optional[Any]   )      ->     List[str]:







    """simple docstring"""






    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

    __magic_name__    					=	{}

    if isinstance(UpperCamelCase__      ,							np.ndarray   ) and np.issubdtype(value.dtype      ,							np.integer   ):
      __magic_name__    					=	{"dtype": np.intaa}
    elif isinstance(UpperCamelCase__      ,							np.ndarray   ) and np.issubdtype(value.dtype      ,							np.floating   ):
      __magic_name__    					=	{"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 							_lowercase							(			self      :			List[Any]      ,							UpperCamelCase__      :			Dict   )      ->     str:







    """simple docstring"""






    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)   ):
      __magic_name__    					=	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 							_lowercase							(			self      :			int      ,							UpperCamelCase__      :			Any   )      ->     Union[str, Any]:







    """simple docstring"""






    return map_nested(self._recursive_tensorize      ,							UpperCamelCase__      ,							map_list=UpperCamelCase__   )
  def 							_lowercase							(			self      :			Optional[Any]      ,							UpperCamelCase__      :			List[str]   )      ->     Mapping:







    """simple docstring"""






    __magic_name__    					=	self.numpy_arrow_extractor().extract_row(UpperCamelCase__   )
    __magic_name__    					=	self.python_features_decoder.decode_row(UpperCamelCase__   )
    return self.recursive_tensorize(UpperCamelCase__   )
  def 							_lowercase							(			self      :			List[Any]      ,							UpperCamelCase__      :			Tuple   )      ->     np.ndarray:







    """simple docstring"""






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





  def 							_lowercase							(			self      :			Any      ,							UpperCamelCase__      :			Optional[Any]   )      ->     Mapping:







    """simple docstring"""






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



def a__   (	A_ ):






  '''simple docstring'''





  __magic_name__    					=	str(A_ ).strip()
  if not bin_string:
    raise ValueError("""Empty string was passed to the function""" )
  __magic_name__    					=	bin_string[0] == """-"""
  if is_negative:
    __magic_name__    					=	bin_string[1:]
  if not all(char in """01""" for char in bin_string ):
    raise ValueError("""Non-binary value was passed to the function""" )
  __magic_name__    					=	0
  for char in bin_string:
    __magic_name__    					=	2 * decimal_number + int(A_ )
  return -decimal_number if is_negative else decimal_number


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

    testmod()

