






'''simple docstring'''

# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
      import torch




class      A     (   TensorFormatter[Mapping, '''torch.Tensor''', Mapping]    ):






 """simple docstring"""





 def __init__(				self      ,  __lowerCAmelCase=None      ,  **__lowerCAmelCase			):
       super().__init__(features=lowercase__			)
       UpperCamelCase_					: int            =				torch_tensor_kwargs
       import torch  # noqa import torch at initialization
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase			):
       import torch

       if isinstance(lowercase__      ,  lowercase__			) and column:
             if all(
                 isinstance(lowercase__      ,  torch.Tensor			) and x.shape == column[0].shape and x.dtype == column[0].dtype
                 for x in column			):
                   return torch.stack(lowercase__			)
       return column
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase			):
       import torch

       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.tolist()

       UpperCamelCase_					: Union[str, Any]            =				{}

       if isinstance(lowercase__      ,  (np.number, np.ndarray)			) and np.issubdtype(value.dtype      ,  np.integer			):
             UpperCamelCase_					: Union[str, Any]            =				{"""dtype""": torch.intaa}
       elif isinstance(lowercase__      ,  (np.number, np.ndarray)			) and np.issubdtype(value.dtype      ,  np.floating			):
             UpperCamelCase_					: int            =				{"""dtype""": torch.floataa}
       elif config.PIL_AVAILABLE and "PIL" in sys.modules:
             import PIL.Image

             if isinstance(lowercase__      ,  PIL.Image.Image			):
                   UpperCamelCase_					: str            =				np.asarray(lowercase__			)
       return torch.tensor(lowercase__      ,  **{**default_dtype, **self.torch_tensor_kwargs}			)
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase			):
       import torch

       # support for torch, tf, jax etc.
       if hasattr(lowercase__      ,  """__array__"""			) and not isinstance(lowercase__      ,  torch.Tensor			):
             UpperCamelCase_					: List[str]            =				data_struct.__array__()
       # support for nested types like struct of list of struct
       if isinstance(lowercase__      ,  np.ndarray			):
             if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
                   return self._consolidate([self.recursive_tensorize(lowercase__			) for substruct in data_struct]			)
       elif isinstance(lowercase__      ,  (list, tuple)			):
             return self._consolidate([self.recursive_tensorize(lowercase__			) for substruct in data_struct]			)
       return self._tensorize(lowercase__			)
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase			):
       return map_nested(self._recursive_tensorize      ,  lowercase__      ,  map_list=lowercase__			)
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase			):
       UpperCamelCase_					: Tuple            =				self.numpy_arrow_extractor().extract_row(lowercase__			)
       UpperCamelCase_					: int            =				self.python_features_decoder.decode_row(lowercase__			)
       return self.recursive_tensorize(lowercase__			)
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase			):
       UpperCamelCase_					: int            =				self.numpy_arrow_extractor().extract_column(lowercase__			)
       UpperCamelCase_					: Dict            =				self.python_features_decoder.decode_column(lowercase__      ,  pa_table.column_names[0]			)
       UpperCamelCase_					: Union[str, Any]            =				self.recursive_tensorize(lowercase__			)
       UpperCamelCase_					: Optional[Any]            =				self._consolidate(lowercase__			)
       return column





 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase			):
       UpperCamelCase_					: str            =				self.numpy_arrow_extractor().extract_batch(lowercase__			)
       UpperCamelCase_					: Union[str, Any]            =				self.python_features_decoder.decode_batch(lowercase__			)
       UpperCamelCase_					: List[Any]            =				self.recursive_tensorize(lowercase__			)
       for column_name in batch:
             UpperCamelCase_					: List[str]            =				self._consolidate(batch[column_name]			)
       return batch







from __future__ import annotations

from random import choice






def _lowerCamelCase  (		snake_case  ):
							return choice(snake_case  )






def _lowerCamelCase  (		snake_case     , snake_case  ):
							_lowerCAmelCase							=							random_pivot(snake_case  )

							# partition based on pivot
							# linear time
							_lowerCAmelCase							=							[e for e in lst if e < pivot]
							_lowerCAmelCase							=							[e for e in lst if e > pivot]

							# if we get lucky, pivot might be the element we want.
							# we can easily see this:
							# small (elements smaller than k)
							# + pivot (kth element)
							# + big (elements larger than k)
							if len(snake_case  ) == k - 1:
														return pivot
							# pivot is in elements bigger than k
							elif len(snake_case  ) < k - 1:
														return kth_number(snake_case     , k - len(snake_case  ) - 1  )
							# pivot is in elements smaller than k
							else:
														return kth_number(snake_case     , snake_case  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()






