





# 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 tensorflow as tf



class 				_UpperCamelCase       (						TensorFormatter[Mapping, '''tf.Tensor''', Mapping]  ):

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




              """simple docstring"""


              super().__init__(features=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	tf_tensor_kwargs
              import tensorflow as tf  # noqa: import tf at initialization

       def lowercase			(		self:			List[Any]       , _SCREAMING_SNAKE_CASE:			int		)							->			str:




              """simple docstring"""


              import tensorflow as tf

              if isinstance(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		) and column:
                     if all(
                         isinstance(_SCREAMING_SNAKE_CASE       , tf.Tensor		) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column		):
                            return tf.stack(_SCREAMING_SNAKE_CASE		)
                     elif all(
                         isinstance(_SCREAMING_SNAKE_CASE       , (tf.Tensor, tf.RaggedTensor)		) and x.ndim == 1 and x.dtype == column[0].dtype
                         for x in column		):
                            # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
                            return tf.ragged.stack(_SCREAMING_SNAKE_CASE		)

              return column

       def lowercase			(		self:			Any       , _SCREAMING_SNAKE_CASE:			Tuple		)							->			Any:




              """simple docstring"""


              import tensorflow as tf

              if value is None:
                     return value

              UpperCamelCase_ 							=	{}

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

                     if isinstance(_SCREAMING_SNAKE_CASE       , PIL.Image.Image		):
                            UpperCamelCase_ 							=	np.asarray(_SCREAMING_SNAKE_CASE		)

              return tf.convert_to_tensor(_SCREAMING_SNAKE_CASE       , **{**default_dtype, **self.tf_tensor_kwargs}		)

       def lowercase			(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Any		)							->			Optional[int]:




              """simple docstring"""


              import tensorflow as tf

              # support for torch, tf, jax etc.
              if config.TORCH_AVAILABLE and "torch" in sys.modules:
                     import torch

                     if isinstance(_SCREAMING_SNAKE_CASE       , torch.Tensor		):
                            return self._tensorize(data_struct.detach().cpu().numpy()[()]		)
              if hasattr(_SCREAMING_SNAKE_CASE       , "__array__"		) and not isinstance(_SCREAMING_SNAKE_CASE       , tf.Tensor		):
                     UpperCamelCase_ 							=	data_struct.__array__()
              # support for nested types like struct of list of struct
              if isinstance(_SCREAMING_SNAKE_CASE       , np.ndarray		):
                     if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
                            return self._consolidate([self.recursive_tensorize(_SCREAMING_SNAKE_CASE		) for substruct in data_struct]		)
              elif isinstance(_SCREAMING_SNAKE_CASE       , (list, tuple)		):
                     return self._consolidate([self.recursive_tensorize(_SCREAMING_SNAKE_CASE		) for substruct in data_struct]		)
              return self._tensorize(_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Any       , _SCREAMING_SNAKE_CASE:			dict		)							->			Tuple:




              """simple docstring"""


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

       def lowercase			(		self:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			pa.Table		)							->			Mapping:




              """simple docstring"""


              UpperCamelCase_ 							=	self.numpy_arrow_extractor().extract_row(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	self.python_features_decoder.decode_row(_SCREAMING_SNAKE_CASE		)
              return self.recursive_tensorize(_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			pa.Table		)							->			"tf.Tensor":




              """simple docstring"""


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



       def lowercase			(		self:			List[Any]       , _SCREAMING_SNAKE_CASE:			pa.Table		)							->			Mapping:




              """simple docstring"""


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


"""simple docstring"""







from __future__ import annotations

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

lowerCamelCase      : List[str]       =TypeVar('''T''', bound=bool)






class 							__snake_case(					Generic[T]	):


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					T								=		value
         __A					:					RandomizedHeapNode[T] | None								=		None
         __A					:					RandomizedHeapNode[T] | None								=		None
    @property
    def 						_a							(  self	):




         '''simple docstring'''



         return self._value

    @staticmethod
    def 						_a							(  __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         if not roota:
              return roota

         if not roota:
              return roota

         if roota.value > roota.value:
              __A						,		__A					:					List[str]								=		roota, roota

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

         __A					:					Dict								=		RandomizedHeapNode.merge(roota.left					,       __lowerCamelCase	)

         return roota







class 							__snake_case(					Generic[T]	):


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase = ()	):




         '''simple docstring'''



         __A					:					RandomizedHeapNode[T] | None								=		None

         if data:
              for item in data:
                   self.insert(__lowerCamelCase	)
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					List[str]								=		RandomizedHeapNode.merge(self._root					,       RandomizedHeapNode(__lowerCamelCase	)	)
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					int								=		self.top()

         if self._root is None:
              return None

         __A					:					Dict								=		RandomizedHeapNode.merge(self._root.left					,       self._root.right	)

         return result
    def 						_a							(  self	):




         '''simple docstring'''



         if not self._root:
              raise IndexError('Can\'t get top element for the empty heap.'	)
         return self._root.value
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Dict								=		None
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Tuple								=		[]
         while self:
              result.append(self.pop()	)

         return result

    def __bool__(  self	):




         '''simple docstring'''



         return self._root is not None


if __name__ == "__main__":
       import doctest

       doctest.testmod()



