



# 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 	lowerCamelCase__ (		TensorFormatter[Mapping, '''tf.Tensor''', Mapping]   ):


						def __init__( self						:  Optional[int]      ,       _a						:  Optional[Any]=None      ,       **_a						:  Optional[Any]			):
													super().__init__(features=_a			)
													a__:       Optional[int]											=tf_tensor_kwargs
													import tensorflow as tf  # noqa: import tf at initialization


						def   _lowerCamelCase			( self						:  List[str]      ,       _a						:  Union[str, Any]			):
													import tensorflow as tf

													if isinstance(_a      ,       _a			) and column:
																				if all(
																				    isinstance(_a      ,       tf.Tensor			) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column			):
																											return tf.stack(_a			)
																				elif all(
																				    isinstance(_a      ,       (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(_a			)

													return column


						def   _lowerCamelCase			( self						:  Dict      ,       _a						:  Dict			):
													import tensorflow as tf

													if value is None:
																				return value

													a__:       Optional[Any]											={}

													if isinstance(_a      ,       (np.number, np.ndarray)			) and np.issubdtype(value.dtype      ,       np.integer			):
																				a__:       Any											={"dtype": tf.intaa}
													elif isinstance(_a      ,       (np.number, np.ndarray)			) and np.issubdtype(value.dtype      ,       np.floating			):
																				a__:       List[str]											={"dtype": tf.floataa}
													elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																				import PIL.Image

																				if isinstance(_a      ,       PIL.Image.Image			):
																											a__:       Optional[int]											=np.asarray(_a			)

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


						def   _lowerCamelCase			( self						:  Optional[int]      ,       _a						:  List[Any]			):
													import tensorflow as tf

													# 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      ,       tf.Tensor			):
																				a__:       List[str]											=data_struct.__array__()
													# support for nested types like struct of list of struct
													if isinstance(_a      ,       np.ndarray			):
																				if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
																											return self._consolidate([self.recursive_tensorize(_a			) for substruct in data_struct]			)
													elif isinstance(_a      ,       (list, tuple)			):
																				return self._consolidate([self.recursive_tensorize(_a			) for substruct in data_struct]			)
													return self._tensorize(_a			)


						def   _lowerCamelCase			( self						:  Optional[Any]      ,       _a						:  dict			):
													return map_nested(self._recursive_tensorize      ,       _a      ,       map_list=_a			)


						def   _lowerCamelCase			( self						:  Any      ,       _a						:  pa.Table			):
													a__:       int											=self.numpy_arrow_extractor().extract_row(_a			)
													a__:       Optional[int]											=self.python_features_decoder.decode_row(_a			)
													return self.recursive_tensorize(_a			)


						def   _lowerCamelCase			( self						:  Union[str, Any]      ,       _a						:  pa.Table			):
													a__:       Tuple											=self.numpy_arrow_extractor().extract_column(_a			)
													a__:       str											=self.python_features_decoder.decode_column(_a      ,       pa_table.column_names[0]			)
													a__:       Tuple											=self.recursive_tensorize(_a			)
													a__:       Optional[int]											=self._consolidate(_a			)
													return column







						def   _lowerCamelCase			( self						:  Dict      ,       _a						:  pa.Table			):
													a__:       Dict											=self.numpy_arrow_extractor().extract_batch(_a			)
													a__:       List[str]											=self.python_features_decoder.decode_batch(_a			)
													a__:       Optional[Any]											=self.recursive_tensorize(_a			)
													for column_name in batch:
																				a__:       List[str]											=self._consolidate(batch[column_name]			)
													return batch










from __future__ import annotations

from typing import Generic, TypeVar

__UpperCAmelCase						  =     TypeVar('''T''')


class 	lowerCamelCase__ (		Generic[T]   ):


						def __init__( self						:  int      ,       _a						:  T			):
													a__:       int											=data  # Assign data
													a__:       Node[T] | None											=None  # Initialize next as null
													a__:       Node[T] | None											=None  # Initialize prev as null


class 	lowerCamelCase__ (		Generic[T]   ):


						def __init__( self						:  Optional[Any]			):
													a__:       Node[T] | None											=None


						def   _lowerCamelCase			( self						:  List[Any]      ,       _a						:  T			):
													if self.head is None:
																				a__:       Dict											=Node(_a			)
													else:
																				a__:       int											=Node(_a			)
																				a__:       Optional[Any]											=new_node
																				a__:       int											=self.head
																				a__:       int											=None
																				a__:       Any											=new_node


						def   _lowerCamelCase			( self						:  Tuple			):
													if self.head is None:
																				return None
													else:
																				assert self.head is not None
																				a__:       Optional[int]											=self.head.data
																				a__:       Union[str, Any]											=self.head.next
																				if self.head is not None:
																											a__:       str											=None
																				return temp


						def   _lowerCamelCase			( self						:  int			):
													return self.head.data if self.head is not None else None


						def __len__( self						:  Union[str, Any]			):
													a__:       Tuple											=self.head
													a__:       int											=0
													while temp is not None:
																				count += 1
																				a__:       List[str]											=temp.next
													return count


						def   _lowerCamelCase			( self						:  int			):
													return self.head is None







						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													print("stack elements are:"			)
													a__:       Optional[int]											=self.head
													while temp is not None:
																				print(temp.data      ,       end="->"			)
																				a__:       Union[str, Any]											=temp.next


# Code execution starts here
if __name__ == "__main__":
		# Start with the empty stack
		__UpperCAmelCase						  =     Stack()

		# Insert 4 at the beginning. So stack becomes 4->None
		print('''Stack operations using Doubly LinkedList''')
		stack.push(4)

		# Insert 5 at the beginning. So stack becomes 4->5->None
		stack.push(5)

		# Insert 6 at the beginning. So stack becomes 4->5->6->None
		stack.push(6)

		# Insert 7 at the beginning. So stack becomes 4->5->6->7->None
		stack.push(7)

		# Print the stack
		stack.print_stack()

		# Print the top element
		print('''\nTop element is ''', stack.top())

		# Print the stack size
		print('''Size of the stack is ''', len(stack))

		# pop the top element
		stack.pop()

		# pop the top element
		stack.pop()

		# two elements have now been popped off
		stack.print_stack()

		# Print True if the stack is empty else False
		print('''\nstack is empty:''', stack.is_empty())





