



from collections.abc import Callable


class   snake_case_ :




       """simple docstring"""






       def __init__(     self     ,lowercase = None):

             """simple docstring"""





             UpperCAmelCase_  :	list				      =		[]
             # Stores indexes of each item for supporting updates and deletion.
             UpperCAmelCase_  :	dict				      =		{}
             # Stores current size of heap.
             UpperCAmelCase_  :	int				      =		0
             # Stores function used to evaluate the score of an item on which basis ordering
             # will be done.
             UpperCAmelCase_  :	Tuple				      =		key or (lambda lowercase: x)



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             return int((i - 1) / 2) if i > 0 else None



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		int(2 * i + 1)
             return left if 0 < left < self.size else None



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		int(2 * i + 2)
             return right if 0 < right < self.size else None



       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	int				      =		(
                 self.pos_map[self.arr[j][0]],
                 self.pos_map[self.arr[i][0]],
             )
             # Then swap the items in the list.
             UpperCAmelCase_      ,      UpperCAmelCase_  :	Union[str, Any]				      =		self.arr[j], self.arr[i]



       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





             return self.arr[i][1] < self.arr[j][1]



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		self._left(lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		self._right(lowercase)
             UpperCAmelCase_  :	Any				      =		i

             if left is not None and not self._cmp(lowercase     ,lowercase):
                   UpperCAmelCase_  :	Tuple				      =		left
             if right is not None and not self._cmp(lowercase     ,lowercase):
                   UpperCAmelCase_  :	List[str]				      =		right

             return valid_parent



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		self._parent(lowercase)
             while parent is not None and not self._cmp(lowercase     ,lowercase):
                   self._swap(lowercase     ,lowercase)
                   UpperCAmelCase_      ,      UpperCAmelCase_  :	List[Any]				      =		parent, self._parent(lowercase)



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		self._get_valid_parent(lowercase)
             while valid_parent != index:
                   self._swap(lowercase     ,lowercase)
                   UpperCAmelCase_      ,      UpperCAmelCase_  :	Tuple				      =		valid_parent, self._get_valid_parent(lowercase)



       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





             if item not in self.pos_map:
                   return
             UpperCAmelCase_  :	Any				      =		self.pos_map[item]
             UpperCAmelCase_  :	List[str]				      =		[item, self.key(lowercase)]
             # Make sure heap is right in both up and down direction.
             # Ideally only one of them will make any change.
             self._heapify_up(lowercase)
             self._heapify_down(lowercase)



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             if item not in self.pos_map:
                   return
             UpperCAmelCase_  :	Tuple				      =		self.pos_map[item]
             del self.pos_map[item]
             UpperCAmelCase_  :	Tuple				      =		self.arr[self.size - 1]
             UpperCAmelCase_  :	Dict				      =		index
             self.size -= 1
             # Make sure heap is right in both up and down direction. Ideally only one
             # of them will make any change- so no performance loss in calling both.
             if self.size > index:
                   self._heapify_up(lowercase)
                   self._heapify_down(lowercase)



       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		len(self.arr)
             if arr_len == self.size:
                   self.arr.append([item, self.key(lowercase)])
             else:
                   UpperCAmelCase_  :	Union[str, Any]				      =		[item, self.key(lowercase)]
             UpperCAmelCase_  :	Any				      =		self.size
             self.size += 1
             self._heapify_up(self.size - 1)



       def  A_   (     self):

             """simple docstring"""





             return self.arr[0] if self.size else None



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		self.get_top()
             if top_item_tuple:
                   self.delete_item(top_item_tuple[0])
             return top_item_tuple







def    _snake_case (       )     ->     None:






      '''simple docstring'''









if __name__ == "__main__":
   import doctest

   doctest.testmod()





# 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   snake_case_ (TensorFormatter[Mapping, """torch.Tensor""", Mapping]  ):




       """simple docstring"""






       def __init__(     self     ,lowercase=None     ,**lowercase):

             """simple docstring"""





             super().__init__(features=lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		torch_tensor_kwargs
             import torch  # noqa import torch at initialization



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             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  A_   (     self     ,lowercase):

             """simple docstring"""





             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_  :	List[str]				      =		{}

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

                   if isinstance(lowercase     ,PIL.Image.Image):
                         UpperCAmelCase_  :	List[str]				      =		np.asarray(lowercase)
             return torch.tensor(lowercase     ,**{**default_dtype, **self.torch_tensor_kwargs})



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             import torch

             # support for torch, tf, jax etc.
             if hasattr(lowercase     ,"__array__") and not isinstance(lowercase     ,torch.Tensor):
                   UpperCAmelCase_  :	int				      =		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  A_   (     self     ,lowercase):

             """simple docstring"""





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



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		self.numpy_arrow_extractor().extract_row(lowercase)
             UpperCAmelCase_  :	Tuple				      =		self.python_features_decoder.decode_row(lowercase)
             return self.recursive_tensorize(lowercase)



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		self.numpy_arrow_extractor().extract_column(lowercase)
             UpperCAmelCase_  :	List[Any]				      =		self.python_features_decoder.decode_column(lowercase     ,pa_table.column_names[0])
             UpperCAmelCase_  :	List[Any]				      =		self.recursive_tensorize(lowercase)
             UpperCAmelCase_  :	List[str]				      =		self._consolidate(lowercase)
             return column



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		self.numpy_arrow_extractor().extract_batch(lowercase)
             UpperCAmelCase_  :	Optional[int]				      =		self.python_features_decoder.decode_batch(lowercase)
             UpperCAmelCase_  :	List[Any]				      =		self.recursive_tensorize(lowercase)
             for column_name in batch:
                   UpperCAmelCase_  :	Optional[int]				      =		self._consolidate(batch[column_name])
             return batch
