

from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

__A     						=      TypeVar('''T''', bound=bool)



class     lowercase_       (    Generic[T]	):



      def __init__(     self :							Union[str, Any]     ,   A__ :							T						)       ->	None:
        _snake_case		=				value
        _snake_case		=				None
        _snake_case		=				None



      @property
      def 		UpperCamelCase_						(     self :							Optional[Any]						)       ->	T:
        return self._value







      @staticmethod
      def 		UpperCamelCase_						(     A__ :							RandomizedHeapNode[T] | None     ,   A__ :							RandomizedHeapNode[T] | None						)       ->	RandomizedHeapNode[T] | None:
        if not roota:
          return roota

        if not roota:
          return roota

        if roota.value > roota.value:
          _snake_case,	_snake_case		=				roota, roota

        if random.choice([True, False]						):
          _snake_case,	_snake_case		=				roota.right, roota.left

        _snake_case		=				RandomizedHeapNode.merge(roota.left     ,   A__						)

        return roota



class     lowercase_       (    Generic[T]	):



      def __init__(     self :							Any     ,   A__ :							Iterable[T] | None = ()						)       ->	None:
        _snake_case		=				None

        if data:
          for item in data:
            self.insert(A__						)



      def 		UpperCamelCase_						(     self :							Optional[Any]     ,   A__ :							T						)       ->	None:
        _snake_case		=				RandomizedHeapNode.merge(self._root     ,   RandomizedHeapNode(A__						)						)



      def 		UpperCamelCase_						(     self :							int						)       ->	T | None:
        _snake_case		=				self.top()

        if self._root is None:
          return None

        _snake_case		=				RandomizedHeapNode.merge(self._root.left     ,   self._root.right						)

        return result



      def 		UpperCamelCase_						(     self :							Optional[int]						)       ->	T:
        if not self._root:
          raise IndexError('''Can\'t get top element for the empty heap.'''						)
        return self._root.value



      def 		UpperCamelCase_						(     self :							Union[str, Any]						)       ->	None:
        _snake_case		=				None



      def 		UpperCamelCase_						(     self :							Union[str, Any]						)       ->	list[Any]:
        _snake_case		=				[]
        while self:
          result.append(self.pop()						)

        return result







      def __bool__(     self :							Any						)       ->	bool:
        return self._root is not None


if __name__ == "__main__":
 import doctest

 doctest.testmod()




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]	):



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



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



      def 		UpperCamelCase_						(     self :							Tuple     ,   A__ :							Optional[int]						)       ->	Optional[Any]:
        if isinstance(A__     ,   (str, bytes, type(A__						))						):
          return value
        elif isinstance(A__     ,   (np.character, np.ndarray)						) and np.issubdtype(value.dtype     ,   np.character						):
          return value
        elif isinstance(A__     ,   np.number						):
          return value

        _snake_case		=				{}

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

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

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



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

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



      def 		UpperCamelCase_						(     self :							List[Any]     ,   A__ :							dict						)       ->	Tuple:
        return map_nested(self._recursive_tensorize     ,   A__     ,   map_list=A__						)



      def 		UpperCamelCase_						(     self :							str     ,   A__ :							pa.Table						)       ->	Mapping:
        _snake_case		=				self.numpy_arrow_extractor().extract_row(A__						)
        _snake_case		=				self.python_features_decoder.decode_row(A__						)
        return self.recursive_tensorize(A__						)



      def 		UpperCamelCase_						(     self :							Optional[Any]     ,   A__ :							pa.Table						)       ->	np.ndarray:
        _snake_case		=				self.numpy_arrow_extractor().extract_column(A__						)
        _snake_case		=				self.python_features_decoder.decode_column(A__     ,   pa_table.column_names[0]						)
        _snake_case		=				self.recursive_tensorize(A__						)
        _snake_case		=				self._consolidate(A__						)
        return column







      def 		UpperCamelCase_						(     self :							Dict     ,   A__ :							pa.Table						)       ->	Mapping:
        _snake_case		=				self.numpy_arrow_extractor().extract_batch(A__						)
        _snake_case		=				self.python_features_decoder.decode_batch(A__						)
        _snake_case		=				self.recursive_tensorize(A__						)
        for column_name in batch:
          _snake_case		=				self._consolidate(batch[column_name]						)
        return batch

